Prep 0.20.0 (#77)

* fix issue where recent save cover images would sometimes not load

* paraphrase prompt tweaks

* action_to_narration regenerate compatibility fixes

* sim suite add asnwer question instruction

* more sim suite tweaks

* refactor agent details display in agent bar

* visual agent progres (a1111 support)

* visual gen prompt tweaks

* openai compat client pass max_tokens

* world state sequential reinforcement max tokens tightened

* improve item names

* Improve item names

* attempt to remove "changed from.." notes when altering an existing character sheet

* prompt improvements for single character portraits

* visual agent progress

* fix issue where character.update wouldn't update long-term memory

* remove experimental flag for now

* add better instructions for updating existing character sheet

* background processing for agents, visual and tts

* fix selected voice not saving between restarts for elevenlabs

* lessen timeout

* clean up agent status logic

* conditional agent configs

* comfyui support

* visualization queue

* refactor visual styles, comfyui progress

* regen images
auto cover image assign
websocket handler plugin abstraction
agent websocket handler

* automatic1111 fixes
agent status and ready checks

* tweaks to character portrait prompt

* system prompt for visualize

* textgenwebui use temp smoothing on yi models

* comment out api key for now

* fixes issues with openai compat client for retaining api key and auto fixing urls

* update_reinforcment tweaks

* agent status emit from one place

* emit agent status as asyncio task

* remove debug output

* tts add openai support

* openai img gen support

* fix issue with confyui checkbox list not loading

* tts model selection for openai

* narrate_query include character sheet if character is referenced in query
improve visual character portrit generation prompt

* client implementation extra field support and runpod vllm client example

* relock

* fix issue where changing context length would cause next generation to error

* visual agent tweaks and auto gen character cover image in sim suite

* fix issue with readyness lock when there werent any clients defined

* load scene readiness fixes

* linting

* docs

* notes for the runpod vllm example

.gitignore

@@ -8,6 +8,7 @@
 talemate_env
 chroma
 config.yaml
+templates/llm-prompt/user/*.jinja2
 scenes/
 !scenes/infinity-quest-dynamic-scenario/
 !scenes/infinity-quest-dynamic-scenario/assets/

README.md

@@ -1,33 +1,36 @@
 # Talemate
 
-Allows you to play roleplay scenarios with large language models. 
+Roleplay with AI with a focus on strong narration and consistent world and game state tracking.
 
-
-|![Screenshot 1](docs/img/0.17.0/ss-1.png)|![Screenshot 2](docs/img/0.17.0/ss-2.png)|
-|------------------------------------------|------------------------------------------|
-|![Screenshot 1](docs/img/0.17.0/ss-4.png)|![Screenshot 2](docs/img/0.17.0/ss-3.png)|
+|![Screenshot 3](docs/img/0.17.0/ss-1.png)|![Screenshot 3](docs/img/0.17.0/ss-2.png)|
 |------------------------------------------|------------------------------------------|
+|![Screenshot 4](docs/img/0.17.0/ss-4.png)|![Screenshot 1](docs/img/0.19.0/Screenshot_15.png)|
+|![Screenshot 2](docs/img/0.19.0/Screenshot_16.png)|![Screenshot 3](docs/img/0.19.0/Screenshot_17.png)|
 
-> :warning: **It does not run any large language models itself but relies on existing APIs. Currently supports OpenAI, text-generation-webui and LMStudio.**
+> :warning: **It does not run any large language models itself but relies on existing APIs. Currently supports OpenAI, text-generation-webui and LMStudio. 0.18.0 also adds support for generic OpenAI api implementations, but generation quality on that will vary.**
 
 This means you need to either have:
 - an [OpenAI](https://platform.openai.com/overview) api key
-- OR setup local (or remote via runpod) LLM inference via one of these options:
+- setup local (or remote via runpod) LLM inference via:
     - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
     - [LMStudio](https://lmstudio.ai/)
+- Any other OpenAI api implementation that implements the v1/completions endpoint
+    - tested llamacpp with the `api_like_OAI.py` wrapper
+    - let me know if you have tested any other implementations and they failed / worked or landed somewhere in between
 
 ## Current features
 
-- responive modern ui
+- responsive modern ui
 - agents
     - conversation: handles character dialogue
     - narration: handles narrative exposition
-    - summarization: handles summarization to compress context while maintain history
+    - summarization: handles summarization to compress context while maintaining history
     - director: can be used to direct the story / characters
     - editor: improves AI responses (very hit and miss at the moment)
     - world state: generates world snapshot and handles passage of time (objects and characters)
     - creator: character / scenario creator
-    - tts: text to speech via elevenlabs, coqui studio, coqui local
+    - tts: text to speech via elevenlabs, OpenAI or local tts
+    - visual: stable-diffusion client for in place visual generation via AUTOMATIC1111, ComfyUI or OpenAI
 - multi-client support (agents can be connected to separate APIs)
 - long term memory
     - chromadb integration
@@ -36,6 +39,7 @@ This means you need to either have:
     - Automatically keep track and reinforce selected character and world truths / states.
 - narrative tools
 - creative tools 
+    - manage multiple NPCs
     - AI backed character creation with template support (jinja2)
     - AI backed scenario creation
 - context managegement
@@ -51,7 +55,6 @@ Kinda making it up as i go along, but i want to lean more into gameplay through
 
 In no particular order:
 
-
 - Extension support
     - modular agents and clients
 - Improved world state
@@ -65,7 +68,26 @@ In no particular order:
     - objectives
     - quests
     - win / lose conditions
-- stable-diffusion client for in place visual generation
+
+
+# Instructions
+
+Please read the documents in the `docs` folder for more advanced configuration and usage.
+
+- [Quickstart](#quickstart)
+    - [Installation](#installation)
+    - [Connecting to an LLM](#connecting-to-an-llm)
+        - [Text-generation-webui](#text-generation-webui)
+        - [Recommended Models](#recommended-models)
+        - [OpenAI](#openai)
+    - [Ready to go](#ready-to-go)
+    - [Load the introductory scenario "Infinity Quest"](#load-the-introductory-scenario-infinity-quest)
+    - [Loading character cards](#loading-character-cards)
+- [Text-to-Speech (TTS)](docs/tts.md)
+- [Visual Generation](docs/visual.md)
+- [ChromaDB (long term memory) configuration](docs/chromadb.md)
+- [Runpod Integration](docs/runpod.md)
+- [Prompt template overrides](docs/templates.md)
 
 # Quickstart
 
@@ -78,7 +100,7 @@ There is also a [troubleshooting guide](docs/troubleshoot.md) that might help.
 ### Windows
 
 1. Download and install Python 3.10 or Python 3.11 from the [official Python website](https://www.python.org/downloads/windows/). :warning: python3.12 is currently not supported.
-1. Download and install Node.js from the [official Node.js website](https://nodejs.org/en/download/). This will also install npm.
+1. Download and install Node.js v20 from the [official Node.js website](https://nodejs.org/en/download/). This will also install npm. :warning: v21 is currently not supported.
 1. Download the Talemate project to your local machine. Download from [the Releases page](https://github.com/vegu-ai/talemate/releases).
 1. Unpack the download and run `install.bat` by double clicking it. This will set up the project on your local machine.
 1. Once the installation is complete, you can start the backend and frontend servers by running `start.bat`.
@@ -88,45 +110,14 @@ There is also a [troubleshooting guide](docs/troubleshoot.md) that might help.
 
 `python 3.10` or `python 3.11` is required. :warning: `python 3.12` not supported yet. 
 
+`nodejs v19 or v20` :warning: `v21` not supported yet.
+
 1. `git clone git@github.com:vegu-ai/talemate`
 1. `cd talemate`
 1. `source install.sh`
 1. Start the backend: `python src/talemate/server/run.py runserver --host 0.0.0.0 --port 5050`.
 1. Open a new terminal, navigate to the `talemate_frontend` directory, and start the frontend server by running `npm run serve`.
 
-## Configuration
-
-### OpenAI
-
-To set your openai api key, open `config.yaml` in any text editor and uncomment / add
-
-```yaml
-openai:
-    api_key: sk-my-api-key-goes-here
-```
-
-You will need to restart the backend for this change to take effect.
-
-### RunPod
-
-To set your runpod api key, open `config.yaml` in any text editor and uncomment / add
-
-```yaml
-runpod:
-    api_key: my-api-key-goes-here
-```
-You will need to restart the backend for this change to take effect.
-
-Once the api key is set Pods loaded from text-generation-webui templates (or the bloke's runpod llm template) will be autoamtically added to your client list in talemate. 
-
-**ATTENTION**: Talemate is not a suitable for way for you to determine whether your pod is currently running or not. **Always** check the runpod dashboard to see if your pod is running or not.
-
-## Recommended Models 
-(as of2023.10.25)
-
-Any of the top models in any of the size classes here should work well:
-https://www.reddit.com/r/LocalLLaMA/comments/17fhp9k/huge_llm_comparisontest_39_models_tested_7b70b/
-
 ## Connecting to an LLM
 
 On the right hand side click the "Add Client" button. If there is no button, you may need to toggle the client options by clicking this button:
@@ -141,13 +132,43 @@ In the modal if you're planning to connect to text-generation-webui, you can lik
 
 ![Add client modal](docs/img/client-setup-0.13.png)
 
+
+#### Recommended Models 
+
+As of 2024.02.06 my personal regular drivers (the ones i test with) are:
+
+- Kunoichi-7B
+- sparsetral-16x7B
+- Nous-Hermes-2-SOLAR-10.7B
+- brucethemoose_Yi-34B-200K-RPMerge
+- dolphin-2.7-mixtral-8x7b
+- Mixtral-8x7B-instruct
+- GPT-3.5-turbo 0125
+- GPT-4-turbo 0116
+
+That said, any of the top models in any of the size classes here should work well (i wouldn't recommend going lower than 7B):
+
+https://www.reddit.com/r/LocalLLaMA/comments/18yp9u4/llm_comparisontest_api_edition_gpt4_vs_gemini_vs/
+
 ### OpenAI
 
 If you want to add an OpenAI client, just change the client type and select the apropriate model.
 
 ![Add client modal](docs/img/add-client-modal-openai.png)
 
-### Ready to go
+If you are setting this up for the first time, you should now see the client, but it will have a red dot next to it, stating that it requires an API key.
+
+![OpenAI API Key missing](docs/img/0.18.0/openai-api-key-1.png)
+
+Click the `SET API KEY` button. This will open a modal where you can enter your API key.
+
+![OpenAI API Key missing](docs/img/0.18.0/openai-api-key-2.png)
+
+Click `Save` and after a moment the client should have a green dot next to it, indicating that it is ready to go.
+
+![OpenAI API Key set](docs/img/0.18.0/openai-api-key-3.png)
+
+## Ready to go
 
 You will know you are good to go when the client and all the agents have a green dot next to them.
 
@@ -171,14 +192,4 @@ Expand the "Load" menu in the top left corner and either click on "Upload a char
 
 Once a character is uploaded, talemate may actually take a moment because it needs to convert it to a talemate format and will also run additional LLM prompts to generate character attributes and world state.
 
-Make sure you save the scene after the character is loaded as it can then be loaded as normal talemate scenario in the future.
-
-## Further documentation
-
-Please read the documents in the `docs` folder for more advanced configuration and usage.
-
-- [Prompt template overrides](docs/templates.md)
-- [Text-to-Speech (TTS)](docs/tts.md)
-- [ChromaDB (long term memory)](docs/chromadb.md)
-- [Runpod Integration](docs/runpod.md)
-- Creative mode
+Make sure you save the scene after the character is loaded as it can then be loaded as normal talemate scenario in the future.

config.example.yaml

@@ -48,6 +48,7 @@ game:
 #  embeddings: instructor
 #  instructor_device: cuda
 #  instructor_model: hkunlp/instructor-xl
+#  openai_model: text-embedding-3-small 
   
 ## Remote LLMs
 

docs/chromadb.md

@@ -56,6 +56,7 @@ Then add the following to `config.yaml` for chromadb:
 ```yaml
 chromadb:
     embeddings: openai
+    openai_model: text-embedding-3-small
 ```
 
 **Note**: As with everything openai, using this isn't free. It's way cheaper than their text completion though. ALSO - if you send super explicit content they may flag / ban your key, so keep that in mind (i hear they usually send warnings first though), and always monitor your usage on their dashboard.

docs/dev/agents/example/test/__init__.py

@@ -0,0 +1,48 @@
+from talemate.agents.base import Agent, AgentAction
+from talemate.agents.registry import register
+from talemate.events import GameLoopEvent
+import talemate.emit.async_signals
+from talemate.emit import emit
+
+@register()
+class TestAgent(Agent):
+    
+    agent_type = "test"
+    verbose_name = "Test"
+    
+    def __init__(self, client):
+        self.client = client
+        self.is_enabled = True
+        self.actions = {
+            "test": AgentAction(
+                enabled=True,
+                label="Test",
+                description="Test",
+            ),
+        }
+        
+    @property
+    def enabled(self):
+        return self.is_enabled
+
+    @property
+    def has_toggle(self):
+        return True
+
+    @property
+    def experimental(self):
+        return True
+
+    def connect(self, scene):
+        super().connect(scene)
+        talemate.emit.async_signals.get("game_loop").connect(self.on_game_loop)
+        
+    async def on_game_loop(self, emission: GameLoopEvent):
+        """
+        Called on the beginning of every game loop
+        """
+
+        if not self.enabled:
+            return
+
+        emit("status", status="info", message="Annoying you with a test message every game loop.")

docs/dev/client/example/runpod_vllm/__init__.py

@@ -0,0 +1,130 @@
+"""
+An attempt to write a client against the runpod serverless vllm worker.
+
+This is close to functional, but since runpod serverless gpu availability is currently terrible, i have
+been unable to properly test it.
+
+Putting it here for now since i think it makes a decent example of how to write a client against a new service.
+"""
+
+import pydantic
+import structlog
+import runpod
+import asyncio
+import aiohttp
+from talemate.client.base import ClientBase, ExtraField
+from talemate.client.registry import register
+from talemate.emit import emit
+from talemate.config import Client as BaseClientConfig
+
+log = structlog.get_logger("talemate.client.runpod_vllm")
+
+class Defaults(pydantic.BaseModel):
+    max_token_length: int = 4096
+    model: str = ""
+    runpod_id: str = ""
+    
+class ClientConfig(BaseClientConfig):
+    runpod_id: str = ""
+
+@register()
+class RunPodVLLMClient(ClientBase):
+    client_type = "runpod_vllm"
+    conversation_retries = 5
+    config_cls = ClientConfig
+
+    class Meta(ClientBase.Meta):
+        title: str = "Runpod VLLM"
+        name_prefix: str = "Runpod VLLM"
+        enable_api_auth: bool = True
+        manual_model: bool = True
+        defaults: Defaults = Defaults()
+        extra_fields: dict[str, ExtraField] = {
+            "runpod_id": ExtraField(
+                name="runpod_id",
+                type="text",
+                label="Runpod ID",
+                required=True,
+                description="The Runpod ID to connect to.",
+            )
+        }
+
+
+    def __init__(self, model=None, runpod_id=None, **kwargs):
+        self.model_name = model
+        self.runpod_id = runpod_id
+        super().__init__(**kwargs)
+
+    @property
+    def experimental(self):
+        return False
+
+
+    def set_client(self, **kwargs):
+        log.debug("set_client", kwargs=kwargs, runpod_id=self.runpod_id)
+        self.runpod_id = kwargs.get("runpod_id", self.runpod_id)
+        
+        
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
+        super().tune_prompt_parameters(parameters, kind)
+
+        keys = list(parameters.keys())
+
+        valid_keys = ["temperature", "top_p", "max_tokens"]
+
+        for key in keys:
+            if key not in valid_keys:
+                del parameters[key]
+
+    async def get_model_name(self):
+        return self.model_name
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
+        """
+        Generates text from the given prompt and parameters.
+        """
+        prompt = prompt.strip()
+
+        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
+
+        try:
+            
+            async with aiohttp.ClientSession() as session:
+                endpoint = runpod.AsyncioEndpoint(self.runpod_id, session)
+                
+                run_request = await endpoint.run({
+                    "input": {
+                        "prompt": prompt,   
+                    }
+                    #"parameters": parameters
+                })
+                
+                while (await run_request.status()) not in ["COMPLETED", "FAILED", "CANCELLED"]:
+                    status = await run_request.status()
+                    log.debug("generate", status=status)
+                    await asyncio.sleep(0.1)
+                    
+                status = await run_request.status()
+                
+                log.debug("generate", status=status)
+                    
+                response = await run_request.output()
+                
+                log.debug("generate", response=response)
+                
+                return response["choices"][0]["tokens"][0]
+            
+        except Exception as e:
+            self.log.error("generate error", e=e)
+            emit(
+                "status", message="Error during generation (check logs)", status="error"
+            )
+            return ""
+
+    def reconfigure(self, **kwargs):
+        if kwargs.get("model"):
+            self.model_name = kwargs["model"]
+        if "runpod_id" in kwargs:
+            self.api_auth = kwargs["runpod_id"]
+        log.warning("reconfigure", kwargs=kwargs)
+        self.set_client(**kwargs)

docs/dev/client/example/test/__init__.py

@@ -0,0 +1,67 @@
+import pydantic
+from openai import AsyncOpenAI
+
+from talemate.client.base import ClientBase
+from talemate.client.registry import register
+
+
+class Defaults(pydantic.BaseModel):
+    api_url: str = "http://localhost:1234"
+    max_token_length: int = 4096
+
+@register()
+class TestClient(ClientBase):
+    client_type = "test"
+
+    class Meta(ClientBase.Meta):
+        name_prefix: str = "test"
+        title: str = "Test"
+        defaults: Defaults = Defaults()
+
+    def set_client(self, **kwargs):
+        self.client = AsyncOpenAI(base_url=self.api_url + "/v1", api_key="sk-1111")
+
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
+        
+        """
+        Talemate adds a bunch of parameters to the prompt, but not all of them are valid for all clients.
+        
+        This method is called before the prompt is sent to the client, and it allows the client to remove
+        any parameters that it doesn't support.
+        """
+        
+        super().tune_prompt_parameters(parameters, kind)
+
+        keys = list(parameters.keys())
+
+        valid_keys = ["temperature", "top_p"]
+
+        for key in keys:
+            if key not in valid_keys:
+                del parameters[key]
+
+    async def get_model_name(self):
+        
+        """
+        This should return the name of the model that is being used.
+        """
+        
+        return "Mock test model"
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
+        """
+        Generates text from the given prompt and parameters.
+        """
+        human_message = {"role": "user", "content": prompt.strip()}
+
+        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
+
+        try:
+            response = await self.client.chat.completions.create(
+                model=self.model_name, messages=[human_message], **parameters
+            )
+
+            return response.choices[0].message.content
+        except Exception as e:
+            self.log.error("generate error", e=e)
+            return ""

docs/tts.md

@@ -17,21 +17,6 @@ elevenlabs:
   api_key: <YOUR_ELEVENLABS_API_KEY>
 ```
 
-## Configuring Coqui TTS
-
-To use Coqui TTS with Talemate, follow these steps:
-
-1. Visit [Coqui](https://app.coqui.ai) and sign up for an account.
-2. Go to the [account page](https://app.coqui.ai/account) and scroll to the bottom to find your API key.
-3. In the `config.yaml` file, under the `coqui` section, set the `api_key` field with your Coqui API key.
-
-Example configuration snippet:
-
-```yaml
-coqui:
-  api_key: <YOUR_COQUI_API_KEY>
-```
-
 ## Configuring Local TTS API
 
 For running a local TTS API, Talemate requires specific dependencies to be installed.

docs/visual.md

@@ -0,0 +1,117 @@
+# Visual Agent
+
+The visual agent currently allows for some bare bones visual generation using various stable-diffusion APIs. This is early development and experimental.
+
+Its important to note that the visualization agent actually specifies two clients. One is the backend for the visual generation, and the other is the text generation client to use for prompt generation.
+
+The client for prompt generation can be assigned to the agent as you would for any other agent. The client for visual generation is assigned in the Visualizer config.
+
+## Index
+
+- [OpenAI](#openai)
+- [AUTOMATIC1111](#automatic1111)
+- [ComfyUI](#comfyui)
+- [How to use](#how-to-use)
+
+## OpenAI
+
+Most straightforward to use, as it runs on the OpenAI API. You will need to have an API key and set it in the application config.
+
+![Set OpenAI Api Key](img/0.18.0/openai-api-key-2.png)
+
+Then open the Visualizer config by clicking the agent's name in the agent list and choose `OpenAI` as the backend.
+
+![OpenAI Visualizer Config](img/0.20.0/visual-config-openai.png)
+
+Note: `Client` here refers to the text-generation client to use for prompt generation. While `Backend` refers to the visual generation backend. You are **NOT** required to use the OpenAI client for prompt generation even if you are using the OpenAI backend for image generation.
+
+## AUTOMATIC1111
+
+This requires you to setup a local instance of the AUTOMATIC1111 API. Follow the instructions from their [GitHub](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to get it running.
+
+Once you have it running, you will want to adjust the `webui-user.bat` in the AUTOMATIC1111 directory to include the following command arguments:
+
+```bat
+set COMMANDLINE_ARGS=--api --listen --port 7861
+```
+
+Then run the `webui-user.bat` to start the API.
+
+Once your AUTOAMTIC1111 API is running (check with your browser) you can set the Visualizer config to use the `AUTOMATIC1111` backend 
+
+![AUTOMATIC1111 Visualizer Config](img/0.20.0/visual-config-a1111.png)
+
+#### Extra Configuration
+
+- `api url`: the url of the API, usually `http://localhost:7861`
+- `steps`: render steps
+- `model type`: sdxl or sd1.5 - this will dictate the resolution of the image generation and actually matters for the quality so make sure this is set to the correct model type for the model you are using.
+
+## ComfyUI
+
+This requires you to setup a local instance of the ComfyUI API. Follow the instructions from their [GitHub](https://github.com/comfyanonymous/ComfyUI) to get it running.
+
+Once you're setup, copy their `start.bat` file to a new `start-listen.bat` file and change the contents to.
+
+```bat
+call venv\Scripts\activate
+call python main.py --port 8188 --listen 0.0.0.0
+``` 
+
+Then run the `start-listen.bat` to start the API.
+
+Once your ComfyUI API is running (check with your browser) you can set the Visualizer config to use the `ComfyUI` backend.
+
+![ComfyUI Visualizer Config](img/0.20.0/visual-config-comfyui.png)
+
+### Extra Configuration
+
+- `api url`: the url of the API, usually `http://localhost:8188`
+- `workflow`: the workflow file to use. This is a comfyui api workflow file that needs to exist in `./templates/comfyui-workflows` inside the talemate directory. Talemate provides two very barebones workflows with `default-sdxl.json` and `default-sd15.json`. You can create your own workflows and place them in this directory to use them. :warning: The workflow file must be generated using the API Workflow export not the UI export. Please refer to their documentation for more information.
+- `checkpoint`: the model to use - this will load a list of all available models in your comfyui instance. Select which one you want to use for the image generation.
+
+### Custom Workflows
+
+When creating custom workflows for ideal compatibility with Talemate, ensure the following.
+
+- A `CheckpointLoaderSimple` node named `Talemate Load Checkpoint`
+- A `EmptyLatentImage` node name `Talemate Resolution`
+- A `ClipTextEncode` node named `Talemate Positive Prompt`
+- A `ClipTextEncode` node named `Talemate Negative Prompt`
+- A `SaveImage` node at the end of the workflow.
+
+![ComfyUI Base workflow example](img/0.20.0/comfyui-base-workflow.png)
+
+## How to use
+
+Once you're done setting up the visualizer agent should have a green dot next to it and display both the selected image generation backend and the selected prompt generation client.
+
+![Visualizer ready](img/0.20.0/visualizer-ready.png)
+
+Your hotbar should then also enable the visualization menu for you to use (once you have a scene loaded).
+
+![Visualization actions](img/0.20.0/visualize-scene-tools.png)
+
+Right now you can generate a portrait for any NPC in the scene or a background image for the scene itself.
+
+Image generation by default will actually happen in the background, allowing you to continue using Talemate while the image is being generated. 
+
+You can tell if an image is being generated by the blueish spinner next to the visualization agent.
+
+![Visualizer busy](img/0.20.0/visualizer-busy.png)
+
+Once the image is generated, it will be avaible for you to view via the visual queue button on top of the screen.
+
+![Images ready](img/0.20.0/visualze-new-images.png)
+
+Click it to open the visual queue and view the generated images.
+
+![alt text](img/0.20.0/visual-queue.png)
+
+### Character Portrait
+
+For character potraits you can chose whether or not to replace the main portrait for the character (the one being displated in the left sidebar when a talemate scene is active).
+
+### Background Image
+
+Right now there is nothing to do with the background image, other than to view it in the visual queue. More functionality will be added in the future.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.masonry.api"
 
 [tool.poetry]
 name = "talemate"
-version = "0.17.0"
+version = "0.20.0"
 description = "AI-backed roleplay and narrative tools"
 authors = ["FinalWombat"]
 license = "GNU Affero General Public License v3.0"
@@ -20,7 +20,7 @@ jinja2 = "^3.0"
 openai = ">=1"
 requests = "^2.26"
 colorama = ">=0.4.6"
-Pillow = "^9.5"
+Pillow = ">=9.5"
 httpx = "<1"
 piexif = "^1.1"
 typing-inspect = "0.8.0"
@@ -38,6 +38,7 @@ isodate = ">=0.6.1"
 thefuzz = ">=0.20.0"
 tiktoken = ">=0.5.1"
 nltk = ">=3.8.1"
+huggingface-hub = ">=0.20.2"
 
 # ChromaDB
 chromadb = ">=0.4.17,<1"

scenes/infinity-quest-dynamic-scenario/infinity-quest.json

@@ -98,6 +98,7 @@
     }
   ],
   "immutable_save": true,
+  "experimental": true,
   "goal": null,
   "goals": [],
   "context": "an epic sci-fi adventure aimed at an adult audience.",
@@ -109,10 +110,10 @@
     "variables": {}
   },
   "assets": {
-    "cover_image": "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df",
+    "cover_image": "e7c712a0b276342d5767ba23806b03912d10c7c4b82dd1eec0056611e2cd5404",
     "assets": {
-      "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df": {
-        "id": "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df",
+      "e7c712a0b276342d5767ba23806b03912d10c7c4b82dd1eec0056611e2cd5404": {
+        "id": "e7c712a0b276342d5767ba23806b03912d10c7c4b82dd1eec0056611e2cd5404",
         "file_type": "png",
         "media_type": "image/png"
       }

scenes/infinity-quest-dynamic-scenario/templates/instructions.jinja2

@@ -5,7 +5,7 @@
     {%- set _ = emit_system("warning", "This is a dynamic scenario generation experiment for Infinity Quest. It will likely require a strong LLM to generate something coherent. GPT-4 or 34B+ if local. Temper your expectations.") -%}
 
     {#- emit status update to the UX -#}
-    {%- set _ = emit_status("busy", "Generating scenario ... [1/3]") -%}
+    {%- set _ = emit_status("busy", "Generating scenario ... [1/3]", as_scene_message=True) -%}
 
     {#- thematic tags will be used to randomize generation -#}
     {%- set tags = thematic_generator.generate("color", "state_of_matter", "scifi_trope") -%}
@@ -17,17 +17,17 @@
 
 
     {#- generate introductory text -#}
-    {%- set _ = emit_status("busy", "Generating scenario ... [2/3]") -%}
+    {%- set _ = emit_status("busy", "Generating scenario ... [2/3]", as_scene_message=True) -%}
     {%- set tmpl__scenario_intro = render_template('generate-scenario-intro', premise=instr__premise) %}
     {%- set instr__intro = "*"+render_and_request(tmpl__scenario_intro)+"*" -%}
 
     {#- generate win conditions -#}
-    {%- set _ = emit_status("busy", "Generating scenario ... [3/3]") -%}
+    {%- set _ = emit_status("busy", "Generating scenario ... [3/3]", as_scene_message=True) -%}
     {%- set tmpl__win_conditions = render_template('generate-win-conditions', premise=instr__premise) %}
     {%- set instr__win_conditions = render_and_request(tmpl__win_conditions) -%}
 
     {#- emit status update to the UX -#}
-    {%- set status = emit_status("info", "Scenario ready.") -%}
+    {%- set status = emit_status("success", "Scenario ready.", as_scene_message=True) -%}
 
     {# set gamestate variables #}
     {%- set _ = game_state.set_var("instr.premise", instr__premise, commit=True) -%}

scenes/simulation-suite/simulation-suite.json

@@ -0,0 +1,52 @@
+{
+    "name": "Simulation Suite",
+    "environment": "scene",
+    "immutable_save": true,
+    "restore_from": "simulation-suite.json",
+    "experimental": true,
+    "help": "Address the computer by starting your statements with 'Computer, ' followed by an instruction.\n\nExamples:\n'Computer, i would like to experience an adventure on a derelict space station'\n'Computer, add a horrific alien creature that is chasing me.'",
+    "description": "",
+    "intro": "*You have entered the simulation suite. No simulation is currently active and you are in a non-descript space with paneled walls surrounding you. The control panel next to you is pulsating with a green light, indicating readiness to receive a prompt to start the simulation.*",
+    "archived_history": [],
+    "history": [],
+    "ts": "PT1S",
+    "characters": [
+        {
+            "name": "You",
+            "gender": "unknown",
+            "color": "cornflowerblue",
+            "base_attributes": {},
+            "is_player": true
+        }
+    ],
+    "context": "a simulated experience",
+    "game_state": {
+        "ops":{
+          "run_on_start": true,
+          "always_direct": true
+        },
+        "variables": {}
+    },
+    "world_state": {
+      "character_name_mappings": {
+        "You": [
+          "user",
+          "player",
+          "player character",
+          "user character",
+          "the user",
+          "the player"
+        ]
+      }
+    },
+    "assets": {
+        "cover_image": "4b157dccac2ba71adb078a9d591f9900d6d62f3e86168a5e0e5e1e9faf6dc103",
+        "assets": {
+          "4b157dccac2ba71adb078a9d591f9900d6d62f3e86168a5e0e5e1e9faf6dc103": {
+            "id": "4b157dccac2ba71adb078a9d591f9900d6d62f3e86168a5e0e5e1e9faf6dc103",
+            "file_type": "png",
+            "media_type": "image/png"
+          }
+        }
+    }
+}

scenes/simulation-suite/templates/computer.jinja2

@@ -0,0 +1,116 @@
+<|SECTION:CONTEXT|>
+{% set scene_history=scene.context_history(budget=1024)  %}
+{% for scene_context in scene_history -%}
+{{ loop.index }}. {{ scene_context }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:FUNCTIONS|>
+The player has instructed the computer to alter the current simulation. 
+
+You have access to the following functions, you can call as many as you want to fulfill the player's requests.
+
+You must at least call one of the following functions:
+
+- change_environment
+- add_ai_character 
+- change_ai_character
+- remove_ai_character
+- set_player_persona
+- set_player_name
+- end_simulation
+- answer_question
+
+Set the player persona at the beginning of a new simulation or if the player requests a change.
+
+Only end the simulation if the player requests it explicitly.
+<|CLOSE_SECTION|>
+<|SECTION:EXAMPLES|>
+Request: Computer, I want to be on a mountain top
+```simulation-stack
+change_environment("mountain top")
+set_player_persona("mountain climber")
+set_player_name("Hank")
+```
+
+Request: Computer, I want to be more muscular and taller
+```simulation-stack
+set_player_persona("make player more muscular and taller")
+```
+
+Request: Computer, the building should be on fire
+```simulation-stack
+change_environment("building on fire")
+```
+
+Request: Computer, a rocket hits the building and George is now injured
+```simulation-stack
+change_environment("building on fire")
+change_ai_character("George is injured")
+```
+
+Request: Computer, I want to experience a rollercoaster ride with a friend
+```simulation-stack
+change_environment("theme park, riding a rollercoaster")
+set_player_persona("young female experiencing rollercoaster ride")
+set_player_name("Susanne")
+add_ai_character("a female friend of player named Sarah")
+```
+
+Request: Computer, I want to experience the international space station
+```simulation-stack
+change_environment("international space station")
+set_player_persona("astronaut experiencing first trip to ISS")
+set_player_name("George")
+add_ai_character("astronaut named Henry")
+```
+
+Request: Computer, remove the goblin and add an elven woman instead
+```simulation-stack
+remove_ai_character("goblin")
+add_ai_character("elven woman named Elune")
+```
+
+Request: Computer, change the skiing instructor to be older.
+```simulation-stack
+change_ai_character("make skiing instructor older")
+```
+
+Request: Computer, change my grandma to my grandpa
+```simulation-stack
+remove_ai_character("grandma")
+add_ai_character("grandpa named Steven")
+```
+
+Request: Computer, remove the skiing instructor and add my friend instead.
+```simulation-stack
+remove_ai_character("skiing instructor")
+add_ai_character("player's friend named Tara")
+```
+
+Request: Computer, replace the skiing instructor with my friend.
+```simulation-stack
+remove_ai_character("skiing instructor")
+add_ai_character("player's friend named Lisa")
+```
+
+Request: Computer, I want to end the simulation
+```simulation-stack
+end_simulation("simulation ended")
+```
+
+Request: Computer, shut down the simulation
+```simulation-stack
+end_simulation("simulation ended")
+```
+
+Request: Computer, what do you know about the game of thrones?
+```simulation-stack
+answer_question("what do you know about the game of thrones?")
+```
+
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Respond with the simulation stack for the following request:
+
+Request: {{ player_instruction }}
+{{ bot_token }}```simulation-stack

scenes/simulation-suite/templates/instructions.jinja2

@@ -0,0 +1,177 @@
+{% set update_world_state = False %}
+{% set _ = debug("HOLODECK SIMULATION") -%}
+{% set player_character = scene.get_player_character() %}
+{% set player_message = scene.last_player_message() %}
+{% set last_processed = game_state.get_var('instr.last_processed', -1) %}
+{% set player_message_is_instruction = (player_message and player_message.raw.lower().startswith("computer") and not player_message.hidden) and not player_message.raw.lower().strip() == "computer" and not last_processed >= player_message.id %}
+{% set simulation_reset = False %}
+{% if not game_state.has_var('instr.simulation_stopped') %}
+{# simulation NOT started #}
+
+    {# get last player instruction #}
+    {% if player_message_is_instruction %}
+    {# player message exists #}
+
+        {#% set _ = agent_action("narrator", "action_to_narration", action_name="paraphrase", narration="The computer is processing the request, please wait a moment.", emit_message=True) %#}
+
+        {% set calls = render_and_request(render_template("computer", player_instruction=player_message.raw), dedupe_enabled=False) %}
+
+        {% set _ = debug("HOLODECK simulation calls", calls=calls ) %}
+        {% set processed = make_list() %}
+
+        {% for call in calls.split("\n") %}
+            {% set _ = debug("CALL", call=call, processed=processed) %}
+            {% set inject = "The computer executes the function `"+call+"`" %}
+            {% if call.strip().startswith('change_environment') %}
+                {# change environment #}
+                {% set _ = processed.append(call) %}
+            
+            {% elif call.strip().startswith("answer_question") %}
+                {# answert a query #}
+
+                {% set _ = agent_action("narrator", "action_to_narration", action_name="progress_story", narrative_direction="The computer calls the following function:\n"+call+"\nand answers the player's question.", emit_message=True) %}
+
+
+            {% elif call.strip().startswith("set_player_persona") %}
+                {# treansform player #}
+                {% set _ = emit_status("busy", "Simulation suite altering user persona.", as_scene_message=True) %}
+
+                {% set character_attributes = agent_action("world_state", "extract_character_sheet", name=player_character.name, text=player_message.raw)%}
+
+                {% set _ = player_character.update(base_attributes=character_attributes) %}
+
+                {% set character_description = agent_action("creator", "determine_character_description", character=player_character) %}
+
+                {% set _ = player_character.update(description=character_description) %}
+
+                {% set _ = debug("HOLODECK transform player", attributes=character_attributes, description=character_description) %}
+                {% set _ = processed.append(call) %}
+            {% elif call.strip().startswith("set_player_name") %}
+                {# change player name #}
+                {% set _ = emit_status("busy", "Simulation suite adjusting user idenity.", as_scene_message=True) %}
+                {% set character_name = agent_action("creator", "determine_character_name", character_name=inject+" - What is a fitting name for the player persona? Respond with the current name if it still fits.") %}
+
+                {% set _ = debug("HOLODECK player name", character_name=character_name) %}
+
+                {% if character_name != player_character.name %}
+                    {% set _ = processed.append(call) %}
+                    {% set _ = player_character.rename(character_name) %}
+                {% endif %}
+            {% elif call.strip().startswith("add_ai_character") %}
+                {# add new npc #}
+
+                {% set _ = emit_status("busy", "Simulation suite adding character.", as_scene_message=True) %}
+                {% set character_name = agent_action("creator", "determine_character_name", character_name=inject+" - what is the name of the character to be added to the scene? If no name can extracted from the text, extract a short descriptive name instead. Respond only with the name.") %}
+
+                {% set _ = emit_status("busy", "Simulation suite adding character: "+character_name, as_scene_message=True) %}
+                {% set _ = debug("HOLODECK add npc", name=character_name)%}
+                {% set npc = agent_action("director", "persist_character", name=character_name, content=player_message.raw )%}
+                {% set _ = agent_action("world_state", "manager", action_name="add_detail_reinforcement", character_name=npc.name, question="Goal", instructions="Generate a goal for "+npc.name+", based on the user's chosen simulation", interval=25, run_immediately=True) %}
+                {% set _ = debug("HOLODECK added npc", npc=npc) %}
+                {% set _ = processed.append(call) %}
+                {% set _ = agent_action("visual", "generate_character_portrait", character_name=npc.name) %}
+            {% elif call.strip().startswith("remove_ai_character") %}
+                {# remove npc #}
+
+                {% set _ = emit_status("busy", "Simulation suite removing character.", as_scene_message=True) %}
+                {% set character_name = agent_action("creator", "determine_character_name", character_name=inject+" - what is the name of the character being removed?", allowed_names=scene.npc_character_names) %}
+
+                {% set npc = scene.get_character(character_name) %}
+
+                {% if npc %}
+                    {% set _ = debug("HOLODECK remove npc", npc=npc.name) %}
+                    {% set _ = agent_action("world_state", "manager", action_name="deactivate_character", character_name=npc.name) %}
+                    {% set _ = processed.append(call) %}
+                {% endif %}
+            {% elif call.strip().startswith("change_ai_character") %}
+                {# change existing npc #}
+
+                {% set _ = emit_status("busy", "Simulation suite altering character.", as_scene_message=True) %}
+                {% set character_name = agent_action("creator", "determine_character_name", character_name=inject+" - what is the name of the character receiving the changes (before the change)?", allowed_names=scene.npc_character_names) %}
+
+                {% set character_name_after = agent_action("creator", "determine_character_name", character_name=inject+" - what is the name of the character receiving the changes (after the changes)?") %}
+
+                {% set npc = scene.get_character(character_name) %}
+
+                {% if npc %}
+                    {% set _ = emit_status("busy", "Changing "+character_name+" -> "+character_name_after, as_scene_message=True) %}
+                    {% set _ = debug("HOLODECK transform npc", npc=npc) %}
+                    {% set character_attributes = agent_action("world_state", "extract_character_sheet", name=npc.name, alteration_instructions=player_message.raw)%}
+                    {% set _ = npc.update(base_attributes=character_attributes) %}
+                    {% set character_description = agent_action("creator", "determine_character_description", character=npc) %}
+                    {% set _ = npc.update(description=character_description) %}
+                    {% set _ = debug("HOLODECK transform npc", attributes=character_attributes, description=character_description) %}
+                    {% set _ = processed.append(call) %}
+                    {% if character_name_after != character_name %}
+                        {% set _ = npc.rename(character_name_after) %}
+                    {% endif %}
+                {% endif %}
+            {% elif call.strip().startswith("end_simulation") %}
+                {# end simulation #}
+                {% set explicit_command = query_text_eval("has the player explicitly asked to end the simulation?", player_message.raw) %}
+                {% if explicit_command %}
+                    {% set _ = emit_status("busy", "Simulation suite ending current simulation.", as_scene_message=True) %}
+                    {% set _ = agent_action("narrator", "action_to_narration", action_name="progress_story", narrative_direction="The computer ends the simulation, disolving the environment and all artifical characters, erasing all memory of it and finally returning the player to the inactive simulation suite.List of artificial characters: "+(",".join(scene.npc_character_names))+". The player is also transformed back to their normal persona.", emit_message=True) %}
+                    {% set _ = scene.sync_restore() %}
+                    {% set _ = agent_action("world_state", "update_world_state", force=True) %}
+                    {% set simulation_reset = True %}
+                {% endif %}
+            {% elif "(" in call.strip() %}
+                {# unknown function call, still add it to processed stack so it can be incoorporated in the narration #}
+                {% set _ = processed.append(call) %}
+            {% endif %}
+        {% endfor %}
+
+        {% if processed and not simulation_reset %}
+            {% set _ = game_state.set_var("instr.has_issued_instructions", "yes", commit=False) %}
+            {% set _ = emit_status("busy", "Simulation suite altering environment.", as_scene_message=True) %}
+            {% set update_world_state = True %}
+            {% set _ = agent_action("narrator", "action_to_narration", action_name="progress_story", narrative_direction="The computer calls the following functions:\n"+processed.join("\n")+"\nand the simulation adjusts the environment according to the user's wishes. Write the narrative that describes the changes.", emit_message=True) %}
+        {% endif %}
+
+    {% elif not game_state.has_var("instr.simulation_started") %}
+    {# no player message yet, start of scenario #}
+        {% set _ = emit_status("busy", "Simulation suite powering up.", as_scene_message=True) %}
+        {% set _ = game_state.set_var("instr.simulation_started", "yes", commit=False) %}
+        {% set _ = agent_action("narrator", "action_to_narration", action_name="progress_story", narrative_direction="Narrate the computer asking the user to state the nature of their desired simulation.", emit_message=False) %}
+        {% set _ = agent_action("narrator", "action_to_narration", action_name="passthrough", narration="Please state your commands by addressing the computer by stating \"Computer,\" followed by an instruction.") %}
+
+        {# pin to make sure characters don't try to interact with the simulation #}
+        {% set _ = agent_action("world_state", "manager", action_name="save_world_entry", entry_id="sim.quarantined", text="Characters in the simulation ARE NOT AWARE OF THE COMPUTER.", meta=make_dict(), pin=True) %}
+        
+        {% set _ = emit_status("success", "Simulation suite ready", as_scene_message=True) %}
+    {% endif %}
+
+{% else %}
+{# simulation ongoing #}
+
+{% endif %}
+
+{% if update_world_state %}
+    {% set _ = emit_status("busy", "Simulation suite updating world state.", as_scene_message=True) %}
+    {% set _ = agent_action("world_state", "update_world_state", force=True) %}
+{% endif %}
+
+{% if not scene.npc_character_names and not simulation_reset %}
+    {# no characters in the scene, see if there are any to add #}
+    {% set npcs = agent_action("director", "persist_characters_from_worldstate", exclude=["computer", "user", "player", "you"]) %}
+    {% for npc in npcs %}
+        {% set _ = agent_action("world_state", "manager", action_name="add_detail_reinforcement", character_name=npc.name, question="Goal", instructions="Generate a goal for the character, based on the user's chosen simulation", interval=25, run_immediately=True) %}
+    {% endfor %}
+    {% if npcs %}
+        {% set _ = agent_action("world_state", "update_world_state", force=True) %}
+    {% endif %}
+{% endif %}
+
+{% if player_message_is_instruction %}
+    {# hide player message to the computer, so its not included in the scene context #}
+    {% set _ = player_message.hide() %}
+    {% set _ = game_state.set_var("instr.last_processed", player_message.id, commit=False) %}
+    {% set _ = emit_status("success", "Simulation suite processed instructions", as_scene_message=True) %}
+{% elif player_message and not game_state.has_var("instr.has_issued_instructions") %}
+    {# simulation not started, but player message is not an instruction #}
+    {% set _ = agent_action("narrator", "action_to_narration", action_name="paraphrase", narration="Instructions to the simulation computer are only process if the computer is addressed at the beginning of the instruction. Please state your commands by addressing the computer by stating \"Computer,\" followed by an instruction. For example ... \"Computer, i want to experience being on a derelict spaceship.\"", emit_message=True) %}
+{% elif player_message and not scene.npc_character_names %}
+    {# simulation started, player message is NOT an instruction, but there are no npcs to interact with #}
+    {% set _ = agent_action("narrator", "action_to_narration", action_name="progress_story", narrative_direction="The environment reacts to the player's actions. YOU MUST NOT ACT ON BEHALF OF THE PLAYER. YOU MUST NOT INTERACT WITH THE COMPUTER.", emit_message=True) %}
+{% endif %}

src/talemate/__init__.py

@@ -2,4 +2,4 @@ from .agents import Agent
 from .client import TextGeneratorWebuiClient
 from .tale_mate import *
 
-VERSION = "0.17.0"
+VERSION = "0.20.0"

src/talemate/agents/__init__.py

@@ -1,11 +1,12 @@
 from .base import Agent
-from .creator import CreatorAgent
 from .conversation import ConversationAgent
+from .creator import CreatorAgent
 from .director import DirectorAgent
+from .editor import EditorAgent
 from .memory import ChromaDBMemoryAgent, MemoryAgent
 from .narrator import NarratorAgent
 from .registry import AGENT_CLASSES, get_agent_class, register
 from .summarize import SummarizeAgent
-from .editor import EditorAgent
+from .tts import TTSAgent
+from .visual import VisualAgent
 from .world_state import WorldStateAgent
-from .tts import TTSAgent

src/talemate/agents/base.py

@@ -1,24 +1,30 @@
 from __future__ import annotations
 
 import asyncio
+import dataclasses
 import re
 from abc import ABC
+from functools import wraps
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
+import pydantic
+import structlog
 from blinker import signal
 
+import talemate.emit.async_signals
 import talemate.instance as instance
 import talemate.util as util
 from talemate.agents.context import ActiveAgent
 from talemate.emit import emit
 from talemate.events import GameLoopStartEvent
-import talemate.emit.async_signals
-import dataclasses
-import pydantic
-import structlog
 
 __all__ = [
     "Agent",
+    "AgentAction",
+    "AgentActionConditional",
+    "AgentActionConfig",
+    "AgentDetail",
+    "AgentEmission",
     "set_processing",
 ]
 
@@ -36,26 +42,42 @@ class AgentActionConfig(pydantic.BaseModel):
     step: Union[int, float, None] = None
     scope: str = "global"
     choices: Union[list[dict[str, str]], None] = None
-        
+    note: Union[str, None] = None
+
     class Config:
         arbitrary_types_allowed = True
-        
+
+
+class AgentActionConditional(pydantic.BaseModel):
+    attribute: str
+    value: Union[int, float, str, bool, None] = None
+
 
 class AgentAction(pydantic.BaseModel):
     enabled: bool = True
     label: str
     description: str = ""
     config: Union[dict[str, AgentActionConfig], None] = None
-    
+    condition: Union[AgentActionConditional, None] = None
+
+
+class AgentDetail(pydantic.BaseModel):
+    value: Union[str, None] = None
+    description: Union[str, None] = None
+    icon: Union[str, None] = None
+    color: str = "grey"
+
+
 def set_processing(fn):
     """
     decorator that emits the agent status as processing while the function
     is running.
-    
+
     Done via a try - final block to ensure the status is reset even if
     the function fails.
     """
-    
+
+    @wraps(fn)
     async def wrapper(self, *args, **kwargs):
         with ActiveAgent(self, fn):
             try:
@@ -68,9 +90,7 @@ def set_processing(fn):
                     # not sure why this happens
                     # some concurrency error?
                     log.error("error emitting agent status", exc=exc)
-                    
-    wrapper.__name__ = fn.__name__
-            
+
     return wrapper
 
 
@@ -84,6 +104,9 @@ class Agent(ABC):
     set_processing = set_processing
     requires_llm_client = True
     auto_break_repetition = False
+    websocket_handler = None
+    essential = True
+    ready_check_error = None
 
     @property
     def agent_details(self):
@@ -96,46 +119,51 @@ class Agent(ABC):
     def verbose_name(self):
         return self.agent_type.capitalize()
 
-
-
     @property
     def ready(self):
         if not getattr(self.client, "enabled", True):
             return False
-        
+
         if self.client and self.client.current_status in ["error", "warning"]:
             return False
-        
+
         return self.client is not None
 
     @property
     def status(self):
-        if self.ready:
-            if not self.enabled:
-                return "disabled"
-            return "idle" if getattr(self, "processing", 0) == 0 else "busy"
-        else:
+        if not self.enabled:
+            return "disabled"
+
+        if not self.ready:
             return "uninitialized"
 
+        if getattr(self, "processing", 0) > 0:
+            return "busy"
+
+        if getattr(self, "processing_bg", 0) > 0:
+            return "busy_bg"
+
+        return "idle"
+
     @property
     def enabled(self):
         # by default, agents are enabled, an agent class that
         # is disableable should override this property
         return True
-    
+
     @property
     def disable(self):
         # by default, agents are enabled, an agent class that
-        # is disableable should override this property to 
+        # is disableable should override this property to
         # disable the agent
         pass
-    
+
     @property
     def has_toggle(self):
         # by default, agents do not have toggles to enable / disable
         # an agent class that is disableable should override this property
         return False
-    
+
     @property
     def experimental(self):
         # by default, agents are not experimental, an agent class that
@@ -152,100 +180,170 @@ class Agent(ABC):
             "requires_llm_client": cls.requires_llm_client,
         }
         actions = getattr(agent, "actions", None)
-        
+
         if actions:
             config_options["actions"] = {k: v.model_dump() for k, v in actions.items()}
         else:
             config_options["actions"] = {}
-            
+
         return config_options
 
-    def apply_config(self, *args, **kwargs):
+    @property
+    def meta(self):
+        return {
+            "essential": self.essential,
+        }
+
+    async def _handle_ready_check(self, fut: asyncio.Future):
+        callback_failure = getattr(self, "on_ready_check_failure", None)
+        if fut.cancelled():
+            if callback_failure:
+                await callback_failure()
+            return
+
+        if fut.exception():
+            exc = fut.exception()
+            self.ready_check_error = exc
+            log.error("agent ready check error", agent=self.agent_type, exc=exc)
+            if callback_failure:
+                await callback_failure(exc)
+            return
+
+        callback = getattr(self, "on_ready_check_success", None)
+        if callback:
+            await callback()
+
+    async def ready_check(self, task: asyncio.Task = None):
+        self.ready_check_error = None
+        if task:
+            task.add_done_callback(
+                lambda fut: asyncio.create_task(self._handle_ready_check(fut))
+            )
+            return
+        return True
+
+    async def apply_config(self, *args, **kwargs):
         if self.has_toggle and "enabled" in kwargs:
             self.is_enabled = kwargs.get("enabled", False)
-            
+
         if not getattr(self, "actions", None):
             return
-            
+
         for action_key, action in self.actions.items():
-            
             if not kwargs.get("actions"):
                 continue
-            
-            action.enabled = kwargs.get("actions", {}).get(action_key, {}).get("enabled", False)
-            
+
+            action.enabled = (
+                kwargs.get("actions", {}).get(action_key, {}).get("enabled", False)
+            )
+
             if not action.config:
                 continue
-            
+
             for config_key, config in action.config.items():
                 try:
-                    config.value = kwargs.get("actions", {}).get(action_key, {}).get("config", {}).get(config_key, {}).get("value", config.value)
+                    config.value = (
+                        kwargs.get("actions", {})
+                        .get(action_key, {})
+                        .get("config", {})
+                        .get(config_key, {})
+                        .get("value", config.value)
+                    )
                 except AttributeError:
                     pass
-    
-    async def on_game_loop_start(self, event:GameLoopStartEvent):
-        
+
+    async def on_game_loop_start(self, event: GameLoopStartEvent):
         """
         Finds all ActionConfigs that have a scope of "scene" and resets them to their default values
         """
-        
+
         if not getattr(self, "actions", None):
             return
-        
+
         for _, action in self.actions.items():
             if not action.config:
                 continue
-            
+
             for _, config in action.config.items():
                 if config.scope == "scene":
-                    # if default_value is None, just use the `type` of the current 
+                    # if default_value is None, just use the `type` of the current
                     # value
                     if config.default_value is None:
                         default_value = type(config.value)()
                     else:
                         default_value = config.default_value
-                    
-                    log.debug("resetting config", config=config, default_value=default_value)
+
+                    log.debug(
+                        "resetting config", config=config, default_value=default_value
+                    )
                     config.value = default_value
-        
+
         await self.emit_status()
-              
+
     async def emit_status(self, processing: bool = None):
-        
         # should keep a count of processing requests, and when the
         # number is 0 status is "idle", if the number is greater than 0
         # status is "busy"
         #
         # increase / decrease based on value of `processing`
-        
+
         if getattr(self, "processing", None) is None:
             self.processing = 0
-            
-        if not processing:
+
+        if processing is False:
             self.processing -= 1
             self.processing = max(0, self.processing)
-        else:
+        elif processing is True:
             self.processing += 1
-            
-        status = "busy" if self.processing > 0 else "idle"
-        if not self.enabled:
-            status = "disabled"
-        
+
         emit(
             "agent_status",
             message=self.verbose_name or "",
             id=self.agent_type,
-            status=status,
+            status=self.status,
             details=self.agent_details,
+            meta=self.meta,
             data=self.config_options(agent=self),
         )
 
         await asyncio.sleep(0.01)
 
+    async def _handle_background_processing(self, fut: asyncio.Future):
+        try:
+            if fut.cancelled():
+                return
+
+            if fut.exception():
+                log.error(
+                    "background processing error",
+                    agent=self.agent_type,
+                    exc=fut.exception(),
+                )
+                await self.emit_status()
+                return
+
+            log.info("background processing done", agent=self.agent_type)
+        finally:
+            self.processing_bg -= 1
+            await self.emit_status()
+
+    async def set_background_processing(self, task: asyncio.Task):
+        log.info("set_background_processing", agent=self.agent_type)
+        if not hasattr(self, "processing_bg"):
+            self.processing_bg = 0
+
+        self.processing_bg += 1
+
+        await self.emit_status()
+        task.add_done_callback(
+            lambda fut: asyncio.create_task(self._handle_background_processing(fut))
+        )
+
     def connect(self, scene):
         self.scene = scene
-        talemate.emit.async_signals.get("game_loop_start").connect(self.on_game_loop_start)
-
+        talemate.emit.async_signals.get("game_loop_start").connect(
+            self.on_game_loop_start
+        )
 
     def clean_result(self, result):
         if "#" in result:
@@ -290,23 +388,28 @@ class Agent(ABC):
 
                 current_memory_context.append(memory)
         return current_memory_context
-    
+
     # LLM client related methods. These are called during or after the client
     # sends the prompt to the API.
 
-    def inject_prompt_paramters(self, prompt_param:dict, kind:str, agent_function_name:str):
+    def inject_prompt_paramters(
+        self, prompt_param: dict, kind: str, agent_function_name: str
+    ):
         """
         Injects prompt parameters before the client sends off the prompt
         Override as needed.
         """
         pass
 
-    def allow_repetition_break(self, kind:str, agent_function_name:str, auto:bool=False):
+    def allow_repetition_break(
+        self, kind: str, agent_function_name: str, auto: bool = False
+    ):
         """
         Returns True if repetition breaking is allowed, False otherwise.
         """
         return False
 
+
 @dataclasses.dataclass
 class AgentEmission:
-    agent: Agent
+    agent: Agent

src/talemate/agents/character_creator.py

@@ -1,3 +1,3 @@
 """
 Code has been moved.
-"""
+"""

src/talemate/agents/context.py

@@ -1,6 +1,6 @@
-
-from typing import Callable, TYPE_CHECKING
 import contextvars
+from typing import TYPE_CHECKING, Callable
+
 import pydantic
 
 __all__ = [
@@ -9,25 +9,38 @@ __all__ = [
 
 active_agent = contextvars.ContextVar("active_agent", default=None)
 
+
 class ActiveAgentContext(pydantic.BaseModel):
     agent: object
     fn: Callable
-    
+    agent_stack: list = pydantic.Field(default_factory=list)
+
     class Config:
-        arbitrary_types_allowed=True
-        
+        arbitrary_types_allowed = True
+
     @property
     def action(self):
         return self.fn.__name__
 
+    def __str__(self):
+        return f"{self.agent.verbose_name}.{self.action}"
+
+
 class ActiveAgent:
-    
     def __init__(self, agent, fn):
         self.agent = ActiveAgentContext(agent=agent, fn=fn)
-    
+
     def __enter__(self):
+
+        previous_agent = active_agent.get()
+
+        if previous_agent:
+            self.agent.agent_stack = previous_agent.agent_stack + [str(self.agent)]
+        else:
+            self.agent.agent_stack = [str(self.agent)]
+
         self.token = active_agent.set(self.agent)
-    
+
     def __exit__(self, *args, **kwargs):
         active_agent.reset(self.token)
         return False

src/talemate/agents/conversation.py

@@ -1,40 +1,48 @@
 from __future__ import annotations
+
 import dataclasses
-import re
 import random
+import re
 from datetime import datetime
 from typing import TYPE_CHECKING, Optional, Union
 
+import structlog
+
 import talemate.client as client
+import talemate.emit.async_signals
 import talemate.instance as instance
 import talemate.util as util
-import structlog
+from talemate.client.context import (
+    client_context_attribute,
+    set_client_context_attribute,
+    set_conversation_context_attribute,
+)
 from talemate.emit import emit
-import talemate.emit.async_signals
-from talemate.scene_message import CharacterMessage, DirectorMessage
-from talemate.prompts import Prompt
 from talemate.events import GameLoopEvent
-from talemate.client.context import set_conversation_context_attribute, client_context_attribute, set_client_context_attribute
+from talemate.prompts import Prompt
+from talemate.scene_message import CharacterMessage, DirectorMessage
 
-from .base import Agent, AgentEmission, set_processing, AgentAction, AgentActionConfig
+from .base import Agent, AgentAction, AgentActionConfig, AgentEmission, set_processing
 from .registry import register
 
 if TYPE_CHECKING:
-    from talemate.tale_mate import Character, Scene, Actor
-    
+    from talemate.tale_mate import Actor, Character, Scene
+
 log = structlog.get_logger("talemate.agents.conversation")
 
+
 @dataclasses.dataclass
 class ConversationAgentEmission(AgentEmission):
     actor: Actor
     character: Character
     generation: list[str]
-    
+
+
 talemate.emit.async_signals.register(
-    "agent.conversation.before_generate",
-    "agent.conversation.generated"
+    "agent.conversation.before_generate", "agent.conversation.generated"
 )
 
+
 @register()
 class ConversationAgent(Agent):
     """
@@ -45,7 +53,7 @@ class ConversationAgent(Agent):
 
     agent_type = "conversation"
     verbose_name = "Conversation"
-    
+
     min_dialogue_length = 75
 
     def __init__(
@@ -60,28 +68,28 @@ class ConversationAgent(Agent):
         self.logging_enabled = logging_enabled
         self.logging_date = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
         self.current_memory_context = None
-        
+
         # several agents extend this class, but we only want to initialize
         # these actions for the conversation agent
-        
+
         if self.agent_type != "conversation":
             return
-        
+
         self.actions = {
             "generation_override": AgentAction(
-                enabled = True,
-                label = "Generation Override",
-                description = "Override generation parameters",
-                config = {
+                enabled=True,
+                label="Generation Override",
+                description="Override generation parameters",
+                config={
                     "length": AgentActionConfig(
                         type="number",
                         label="Generation Length (tokens)",
                         description="Maximum number of tokens to generate for a conversation response.",
-                        value=96, 
+                        value=128,
                         min=32,
                         max=512,
                         step=32,
-                    ),#
+                    ),  #
                     "instructions": AgentActionConfig(
                         type="text",
                         label="Instructions",
@@ -96,24 +104,24 @@ class ConversationAgent(Agent):
                         min=0.0,
                         max=1.0,
                         step=0.1,
-                    )
-                }
+                    ),
+                },
             ),
             "auto_break_repetition": AgentAction(
-                enabled = True,
-                label = "Auto Break Repetition",
-                description = "Will attempt to automatically break AI repetition.",
+                enabled=True,
+                label="Auto Break Repetition",
+                description="Will attempt to automatically break AI repetition.",
             ),
             "natural_flow": AgentAction(
-                enabled = True,
-                label = "Natural Flow",
-                description = "Will attempt to generate a more natural flow of conversation between multiple characters.",
-                config = {
+                enabled=True,
+                label="Natural Flow",
+                description="Will attempt to generate a more natural flow of conversation between multiple characters.",
+                config={
                     "max_auto_turns": AgentActionConfig(
                         type="number",
                         label="Max. Auto Turns",
                         description="The maximum number of turns the AI is allowed to generate before it stops and waits for the player to respond.",
-                        value=4, 
+                        value=4,
                         min=1,
                         max=100,
                         step=1,
@@ -122,31 +130,40 @@ class ConversationAgent(Agent):
                         type="number",
                         label="Max. Idle Turns",
                         description="The maximum number of turns a character can go without speaking before they are considered overdue to speak.",
-                        value=8, 
+                        value=8,
                         min=1,
                         max=100,
                         step=1,
                     ),
-                }
+                },
             ),
             "use_long_term_memory": AgentAction(
-                enabled = True,
-                label = "Long Term Memory",
-                description = "Will augment the conversation prompt with long term memory.",
-                config = {
+                enabled=True,
+                label="Long Term Memory",
+                description="Will augment the conversation prompt with long term memory.",
+                config={
                     "retrieval_method": AgentActionConfig(
                         type="text",
                         label="Context Retrieval Method",
                         description="How relevant context is retrieved from the long term memory.",
                         value="direct",
                         choices=[
-                            {"label": "Context queries based on recent dialogue (fast)", "value": "direct"},
-                            {"label": "Context queries generated by AI", "value": "queries"},
-                            {"label": "AI compiled question and answers (slow)", "value": "questions"},
-                        ]
+                            {
+                                "label": "Context queries based on recent dialogue (fast)",
+                                "value": "direct",
+                            },
+                            {
+                                "label": "Context queries generated by AI",
+                                "value": "queries",
+                            },
+                            {
+                                "label": "AI compiled question and answers (slow)",
+                                "value": "questions",
+                            },
+                        ],
                     ),
-                }
-            ),  
+                },
+            ),
         }
 
     def connect(self, scene):
@@ -154,40 +171,37 @@ class ConversationAgent(Agent):
         talemate.emit.async_signals.get("game_loop").connect(self.on_game_loop)
 
     def last_spoken(self):
-        
         """
         Returns the last time each character spoke
         """
-        
+
         last_turn = {}
         turns = 0
         character_names = self.scene.character_names
         max_idle_turns = self.actions["natural_flow"].config["max_idle_turns"].value
-        
+
         for idx in range(len(self.scene.history) - 1, -1, -1):
-            
             if isinstance(self.scene.history[idx], CharacterMessage):
-                
                 if turns >= max_idle_turns:
                     break
-                
+
                 character = self.scene.history[idx].character_name
-                
+
                 if character in character_names:
                     last_turn[character] = turns
                     character_names.remove(character)
-                    
+
                 if not character_names:
                     break
-                
+
                 turns += 1
-        
+
         if character_names and turns >= max_idle_turns:
             for character in character_names:
-                last_turn[character] = max_idle_turns        
+                last_turn[character] = max_idle_turns
 
         return last_turn
-        
+
     def repeated_speaker(self):
         """
         Counts the amount of times the most recent speaker has spoken in a row
@@ -203,125 +217,164 @@ class ConversationAgent(Agent):
                 else:
                     break
         return count
-                
-    async def on_game_loop(self, event:GameLoopEvent):
+
+    async def on_game_loop(self, event: GameLoopEvent):
         await self.apply_natural_flow()
-    
+
     async def apply_natural_flow(self, force: bool = False, npcs_only: bool = False):
         """
         If the natural flow action is enabled, this will attempt to determine
         the ideal character to talk next.
-        
+
         This will let the AI pick a character to talk to, but if the AI can't figure
         it out it will apply rules based on max_idle_turns and max_auto_turns.
-        
+
         If all fails it will just pick a random character.
-        
+
         Repetition is also taken into account, so if a character has spoken twice in a row
         they will not be picked again until someone else has spoken.
         """
-        
+
         scene = self.scene
-        
+
         if not scene.auto_progress and not force:
             # we only apply natural flow if auto_progress is enabled
             return
-        
+
         if self.actions["natural_flow"].enabled and len(scene.character_names) > 2:
-            
             # last time each character spoke (turns ago)
             max_idle_turns = self.actions["natural_flow"].config["max_idle_turns"].value
             max_auto_turns = self.actions["natural_flow"].config["max_auto_turns"].value
             last_turn = self.last_spoken()
             player_name = scene.get_player_character().name
             last_turn_player = last_turn.get(player_name, 0)
-            
+
             if last_turn_player >= max_auto_turns and not npcs_only:
                 self.scene.next_actor = scene.get_player_character().name
-                log.debug("conversation_agent.natural_flow", next_actor="player", overdue=True, player_character=scene.get_player_character().name)
+                log.debug(
+                    "conversation_agent.natural_flow",
+                    next_actor="player",
+                    overdue=True,
+                    player_character=scene.get_player_character().name,
+                )
                 return
-            
+
             log.debug("conversation_agent.natural_flow", last_turn=last_turn)
-            
+
             # determine random character to talk, this will be the fallback in case
             # the AI can't figure out who should talk next
-            
+
             if scene.prev_actor:
-                
                 # we dont want to talk to the same person twice in a row
                 character_names = scene.character_names
                 character_names.remove(scene.prev_actor)
-                
+
                 if npcs_only:
                     character_names = [c for c in character_names if c != player_name]
-                    
+
                 random_character_name = random.choice(character_names)
             else:
-                character_names = scene.character_names                
+                character_names = scene.character_names
                 # no one has talked yet, so we just pick a random character
-                
+
                 if npcs_only:
                     character_names = [c for c in character_names if c != player_name]
-                    
+
                 random_character_name = random.choice(scene.character_names)
-            
-            overdue_characters = [character for character, turn in last_turn.items() if turn >= max_idle_turns]
-            
+
+            overdue_characters = [
+                character
+                for character, turn in last_turn.items()
+                if turn >= max_idle_turns
+            ]
+
             if npcs_only:
                 overdue_characters = [c for c in overdue_characters if c != player_name]
-            
+
             if overdue_characters and self.scene.history:
                 # Pick a random character from the overdue characters
                 scene.next_actor = random.choice(overdue_characters)
             elif scene.history:
                 scene.next_actor = None
-                
+
                 # AI will attempt to figure out who should talk next
                 next_actor = await self.select_talking_actor(character_names)
                 next_actor = next_actor.strip().strip('"').strip(".")
-                
+
                 for character_name in scene.character_names:
-                    if next_actor.lower() in character_name.lower() or character_name.lower() in next_actor.lower():
+                    if (
+                        next_actor.lower() in character_name.lower()
+                        or character_name.lower() in next_actor.lower()
+                    ):
                         scene.next_actor = character_name
                         break
-                
+
                 if not scene.next_actor:
                     # AI couldn't figure out who should talk next, so we just pick a random character
-                    log.debug("conversation_agent.natural_flow", next_actor="random", random_character_name=random_character_name)
+                    log.debug(
+                        "conversation_agent.natural_flow",
+                        next_actor="random",
+                        random_character_name=random_character_name,
+                    )
                     scene.next_actor = random_character_name
                 else:
-                    log.debug("conversation_agent.natural_flow", next_actor="picked", ai_next_actor=scene.next_actor)
+                    log.debug(
+                        "conversation_agent.natural_flow",
+                        next_actor="picked",
+                        ai_next_actor=scene.next_actor,
+                    )
             else:
                 # always start with main character (TODO: configurable?)
                 player_character = scene.get_player_character()
-                log.debug("conversation_agent.natural_flow", next_actor="main_character", main_character=player_character)
-                scene.next_actor = player_character.name if player_character else random_character_name
-                
-            scene.log.debug("conversation_agent.natural_flow", next_actor=scene.next_actor)
+                log.debug(
+                    "conversation_agent.natural_flow",
+                    next_actor="main_character",
+                    main_character=player_character,
+                )
+                scene.next_actor = (
+                    player_character.name if player_character else random_character_name
+                )
+
+            scene.log.debug(
+                "conversation_agent.natural_flow", next_actor=scene.next_actor
+            )
 
-            
             # same character cannot go thrice in a row, if this is happening, pick a random character that
             # isnt the same as the last character
-            
-            if self.repeated_speaker() >= 2 and self.scene.prev_actor == self.scene.next_actor:
-                scene.next_actor = random.choice([c for c in scene.character_names if c != scene.prev_actor])
-                scene.log.debug("conversation_agent.natural_flow", next_actor="random (repeated safeguard)", random_character_name=scene.next_actor)
-            
+
+            if (
+                self.repeated_speaker() >= 2
+                and self.scene.prev_actor == self.scene.next_actor
+            ):
+                scene.next_actor = random.choice(
+                    [c for c in scene.character_names if c != scene.prev_actor]
+                )
+                scene.log.debug(
+                    "conversation_agent.natural_flow",
+                    next_actor="random (repeated safeguard)",
+                    random_character_name=scene.next_actor,
+                )
+
         else:
             scene.next_actor = None
 
-    
     @set_processing
-    async def select_talking_actor(self, character_names: list[str]=None):
-        result = await Prompt.request("conversation.select-talking-actor", self.client, "conversation_select_talking_actor", vars={
-            "scene": self.scene,
-            "max_tokens": self.client.max_token_length,
-            "character_names": character_names or self.scene.character_names,
-            "character_names_formatted": ", ".join(character_names or self.scene.character_names),
-        })
-        
+    async def select_talking_actor(self, character_names: list[str] = None):
+        result = await Prompt.request(
+            "conversation.select-talking-actor",
+            self.client,
+            "conversation_select_talking_actor",
+            vars={
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "character_names": character_names or self.scene.character_names,
+                "character_names_formatted": ", ".join(
+                    character_names or self.scene.character_names
+                ),
+            },
+        )
+
         return result
-        
 
     async def build_prompt_default(
         self,
@@ -335,17 +388,17 @@ class ConversationAgent(Agent):
         # we subtract 200 to account for the response
 
         scene = character.actor.scene
-        
+
         total_token_budget = self.client.max_token_length - 200
         scene_and_dialogue_budget = total_token_budget - 500
         long_term_memory_budget = min(int(total_token_budget * 0.05), 200)
-        
+
         scene_and_dialogue = scene.context_history(
-            budget=scene_and_dialogue_budget, 
+            budget=scene_and_dialogue_budget,
             keep_director=True,
             sections=False,
         )
-        
+
         memory = await self.build_prompt_default_memory(character)
 
         main_character = scene.main_character.character
@@ -360,36 +413,39 @@ class ConversationAgent(Agent):
             )
         else:
             formatted_names = character_names[0] if character_names else ""
-            
+
         try:
             director_message = isinstance(scene_and_dialogue[-1], DirectorMessage)
         except IndexError:
             director_message = False
-            
+
         extra_instructions = ""
         if self.actions["generation_override"].enabled:
-            extra_instructions = self.actions["generation_override"].config["instructions"].value
+            extra_instructions = (
+                self.actions["generation_override"].config["instructions"].value
+            )
+
+        prompt = Prompt.get(
+            "conversation.dialogue",
+            vars={
+                "scene": scene,
+                "max_tokens": self.client.max_token_length,
+                "scene_and_dialogue_budget": scene_and_dialogue_budget,
+                "scene_and_dialogue": scene_and_dialogue,
+                "memory": memory,
+                "characters": list(scene.get_characters()),
+                "main_character": main_character,
+                "formatted_names": formatted_names,
+                "talking_character": character,
+                "partial_message": char_message,
+                "director_message": director_message,
+                "extra_instructions": extra_instructions,
+            },
+        )
 
-        prompt = Prompt.get("conversation.dialogue", vars={
-            "scene": scene,
-            "max_tokens": self.client.max_token_length,
-            "scene_and_dialogue_budget": scene_and_dialogue_budget,
-            "scene_and_dialogue": scene_and_dialogue,
-            "memory": memory,
-            "characters": list(scene.get_characters()),
-            "main_character": main_character,
-            "formatted_names": formatted_names,
-            "talking_character": character,
-            "partial_message": char_message,
-            "director_message": director_message,
-            "extra_instructions": extra_instructions,
-        })
-        
         return str(prompt)
-    
-    async def build_prompt_default_memory(
-        self, character: Character
-    ):
+
+    async def build_prompt_default_memory(self, character: Character):
         """
         Builds long term memory for the conversation prompt
 
@@ -404,39 +460,56 @@ class ConversationAgent(Agent):
         if not self.actions["use_long_term_memory"].enabled:
             return []
 
-
         if self.current_memory_context:
             return self.current_memory_context
 
         self.current_memory_context = ""
-        retrieval_method = self.actions["use_long_term_memory"].config["retrieval_method"].value
-        
+        retrieval_method = (
+            self.actions["use_long_term_memory"].config["retrieval_method"].value
+        )
 
         if retrieval_method != "direct":
-            
             world_state = instance.get_agent("world_state")
-            history = self.scene.context_history(min_dialogue=3, max_dialogue=15, keep_director=False, sections=False, add_archieved_history=False)
+            history = self.scene.context_history(
+                min_dialogue=3,
+                max_dialogue=15,
+                keep_director=False,
+                sections=False,
+                add_archieved_history=False,
+            )
             text = "\n".join(history)
-            log.debug("conversation_agent.build_prompt_default_memory", direct=False, version=retrieval_method)
-                    
+            log.debug(
+                "conversation_agent.build_prompt_default_memory",
+                direct=False,
+                version=retrieval_method,
+            )
+
             if retrieval_method == "questions":
-                self.current_memory_context = (await world_state.analyze_text_and_extract_context(
-                    text, f"continue the conversation as {character.name}"
-                )).split("\n")
+                self.current_memory_context = (
+                    await world_state.analyze_text_and_extract_context(
+                        text, f"continue the conversation as {character.name}"
+                    )
+                ).split("\n")
             elif retrieval_method == "queries":
-                self.current_memory_context = await world_state.analyze_text_and_extract_context_via_queries(
-                    text, f"continue the conversation as {character.name}"
+                self.current_memory_context = (
+                    await world_state.analyze_text_and_extract_context_via_queries(
+                        text, f"continue the conversation as {character.name}"
+                    )
                 )
 
         else:
             history = list(map(str, self.scene.collect_messages(max_iterations=3)))
-            log.debug("conversation_agent.build_prompt_default_memory", history=history, direct=True)
+            log.debug(
+                "conversation_agent.build_prompt_default_memory",
+                history=history,
+                direct=True,
+            )
             memory = instance.get_agent("memory")
-            
+
             context = await memory.multi_query(history, max_tokens=500, iterate=5)
-             
+
             self.current_memory_context = context
-        
+
         return self.current_memory_context
 
     async def build_prompt(self, character, char_message: str = ""):
@@ -445,10 +518,9 @@ class ConversationAgent(Agent):
         return await fn(character, char_message=char_message)
 
     def clean_result(self, result, character):
-        
         if "#" in result:
             result = result.split("#")[0]
-        
+
         result = result.replace(" :", ":")
         result = result.replace("[", "*").replace("]", "*")
         result = result.replace("(", "*").replace(")", "*")
@@ -459,31 +531,38 @@ class ConversationAgent(Agent):
     def set_generation_overrides(self):
         if not self.actions["generation_override"].enabled:
             return
-        
-        set_conversation_context_attribute("length", self.actions["generation_override"].config["length"].value)
-            
+
+        set_conversation_context_attribute(
+            "length", self.actions["generation_override"].config["length"].value
+        )
+
         if self.actions["generation_override"].config["jiggle"].value > 0.0:
             nuke_repetition = client_context_attribute("nuke_repetition")
             if nuke_repetition == 0.0:
                 # we only apply the agent override if some other mechanism isn't already
                 # setting the nuke_repetition value
-                nuke_repetition = self.actions["generation_override"].config["jiggle"].value
+                nuke_repetition = (
+                    self.actions["generation_override"].config["jiggle"].value
+                )
                 set_client_context_attribute("nuke_repetition", nuke_repetition)
 
     @set_processing
-    async def converse(self, actor, editor=None):
+    async def converse(self, actor):
         """
         Have a conversation with the AI
         """
 
-        history = actor.history
         self.current_memory_context = None
 
         character = actor.character
-        
-        emission = ConversationAgentEmission(agent=self, generation="", actor=actor, character=character)
-        await talemate.emit.async_signals.get("agent.conversation.before_generate").send(emission)
-        
+
+        emission = ConversationAgentEmission(
+            agent=self, generation="", actor=actor, character=character
+        )
+        await talemate.emit.async_signals.get(
+            "agent.conversation.before_generate"
+        ).send(emission)
+
         self.set_generation_overrides()
 
         result = await self.client.send_prompt(await self.build_prompt(character))
@@ -506,7 +585,7 @@ class ConversationAgent(Agent):
 
             result = self.clean_result(result, character)
 
-            total_result += " "+result
+            total_result += " " + result
 
             if len(total_result) == 0 and max_loops < 10:
                 max_loops += 1
@@ -530,7 +609,7 @@ class ConversationAgent(Agent):
 
         # Removes partial sentence at the end
         total_result = util.clean_dialogue(total_result, main_name=character.name)
-        
+
         # Remove "{character.name}:" - all occurences
         total_result = total_result.replace(f"{character.name}:", "")
 
@@ -549,13 +628,17 @@ class ConversationAgent(Agent):
         )
 
         response_message = util.parse_messages_from_str(total_result, [character.name])
-        
-        log.info("conversation agent", result=response_message)
-            
-        emission = ConversationAgentEmission(agent=self, generation=response_message, actor=actor, character=character)
-        await talemate.emit.async_signals.get("agent.conversation.generated").send(emission)
 
-        #log.info("conversation agent", generation=emission.generation)
+        log.info("conversation agent", result=response_message)
+
+        emission = ConversationAgentEmission(
+            agent=self, generation=response_message, actor=actor, character=character
+        )
+        await talemate.emit.async_signals.get("agent.conversation.generated").send(
+            emission
+        )
+
+        # log.info("conversation agent", generation=emission.generation)
 
         messages = [CharacterMessage(message) for message in emission.generation]
 
@@ -564,15 +647,17 @@ class ConversationAgent(Agent):
 
         return messages
 
-
-    def allow_repetition_break(self, kind: str, agent_function_name: str, auto: bool = False):
-        
+    def allow_repetition_break(
+        self, kind: str, agent_function_name: str, auto: bool = False
+    ):
         if auto and not self.actions["auto_break_repetition"].enabled:
             return False
-        
+
         return agent_function_name == "converse"
-    
-    def inject_prompt_paramters(self, prompt_param: dict, kind: str, agent_function_name: str):
+
+    def inject_prompt_paramters(
+        self, prompt_param: dict, kind: str, agent_function_name: str
+    ):
         if prompt_param.get("extra_stopping_strings") is None:
             prompt_param["extra_stopping_strings"] = []
-        prompt_param["extra_stopping_strings"] += ['[']
+        prompt_param["extra_stopping_strings"] += ["#"]

src/talemate/agents/creator/__init__.py

@@ -3,22 +3,23 @@ from __future__ import annotations
 import json
 import os
 
+import talemate.client as client
 from talemate.agents.base import Agent, set_processing
 from talemate.agents.registry import register
 from talemate.emit import emit
 from talemate.prompts import Prompt
-import talemate.client as client
 
+from .assistant import AssistantMixin
 from .character import CharacterCreatorMixin
 from .scenario import ScenarioCreatorMixin
 
+
 @register()
-class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, Agent):
-    
+class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, AssistantMixin, Agent):
     """
     Creates characters and scenarios and other fun stuff!
     """
-    
+
     agent_type = "creator"
     verbose_name = "Creator"
 
@@ -78,12 +79,14 @@ class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, Agent):
         # Remove duplicates while preserving the order for list type keys
         for key, value in merged_data.items():
             if isinstance(value, list):
-                merged_data[key] = [x for i, x in enumerate(value) if x not in value[:i]]
+                merged_data[key] = [
+                    x for i, x in enumerate(value) if x not in value[:i]
+                ]
 
         merged_data["context"] = context
 
         return merged_data
-    
+
     def load_templates_old(self, names: list, template_type: str = "character") -> dict:
         """
         Loads multiple character creation templates from ./templates/character and merges them in order.
@@ -128,8 +131,10 @@ class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, Agent):
 
                 if "context" in template_data["instructions"]:
                     context = template_data["instructions"]["context"]
-                    
-                merged_instructions[name]["questions"] = [q[0] for q in template_data.get("questions", [])]
+
+                merged_instructions[name]["questions"] = [
+                    q[0] for q in template_data.get("questions", [])
+                ]
 
         # Remove duplicates while preserving the order
         merged_template = [
@@ -158,24 +163,33 @@ class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, Agent):
 
         return rv
 
-
     @set_processing
     async def generate_json_list(
         self,
-        text:str,
-        count:int=20,
-        first_item:str=None,
+        text: str,
+        count: int = 20,
+        first_item: str = None,
     ):
-        _, json_list = await Prompt.request(f"creator.generate-json-list", self.client, "create", vars={
-            "text": text,
-            "first_item": first_item,
-            "count": count,
-        })
-        return json_list.get("items",[])
-    
+        _, json_list = await Prompt.request(
+            f"creator.generate-json-list",
+            self.client,
+            "create",
+            vars={
+                "text": text,
+                "first_item": first_item,
+                "count": count,
+            },
+        )
+        return json_list.get("items", [])
+
     @set_processing
-    async def generate_title(self, text:str):
-        title = await Prompt.request(f"creator.generate-title", self.client, "create_short", vars={
-            "text": text,
-        })
-        return title
+    async def generate_title(self, text: str):
+        title = await Prompt.request(
+            f"creator.generate-title",
+            self.client,
+            "create_short",
+            vars={
+                "text": text,
+            },
+        )
+        return title

src/talemate/agents/creator/assistant.py

@@ -0,0 +1,95 @@
+from typing import TYPE_CHECKING, Union
+
+import pydantic
+
+import talemate.util as util
+from talemate.agents.base import set_processing
+from talemate.prompts import Prompt
+
+if TYPE_CHECKING:
+    from talemate.tale_mate import Character, Scene
+
+
+class ContentGenerationContext(pydantic.BaseModel):
+    """
+    A context for generating content.
+    """
+
+    context: str
+    instructions: str
+    length: int
+    character: Union[str, None] = None
+    original: Union[str, None] = None
+
+    @property
+    def computed_context(self) -> (str, str):
+        typ, context = self.context.split(":", 1)
+        return typ, context
+
+
+class AssistantMixin:
+    """
+    Creator mixin that allows quick contextual generation of content.
+    """
+
+    async def contextual_generate_from_args(
+        self,
+        context: str,
+        instructions: str,
+        length: int = 100,
+        character: Union[str, None] = None,
+        original: Union[str, None] = None,
+    ):
+        """
+        Request content from the assistant.
+        """
+
+        generation_context = ContentGenerationContext(
+            context=context,
+            instructions=instructions,
+            length=length,
+            character=character,
+            original=original,
+        )
+
+        return await self.contextual_generate(generation_context)
+
+    @set_processing
+    async def contextual_generate(
+        self,
+        generation_context: ContentGenerationContext,
+    ):
+        """
+        Request content from the assistant.
+        """
+
+        context_typ, context_name = generation_context.computed_context
+
+        if generation_context.length < 100:
+            kind = "create_short"
+        elif generation_context.length < 500:
+            kind = "create_concise"
+        else:
+            kind = "create"
+
+        content = await Prompt.request(
+            f"creator.contextual-generate",
+            self.client,
+            kind,
+            vars={
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "generation_context": generation_context,
+                "context_typ": context_typ,
+                "context_name": context_name,
+                "character": (
+                    self.scene.get_character(generation_context.character)
+                    if generation_context.character
+                    else None
+                ),
+            },
+        )
+
+        content = util.strip_partial_sentences(content)
+
+        return content.strip()

src/talemate/agents/creator/character.py

@@ -1,42 +1,48 @@
 from __future__ import annotations
 
-import re
 import asyncio
 import random
-import structlog
+import re
 from typing import TYPE_CHECKING, Callable
 
+import structlog
+
 import talemate.util as util
-from talemate.emit import emit
-from talemate.prompts import Prompt, LoopedPrompt
-from talemate.exceptions import LLMAccuracyError
 from talemate.agents.base import set_processing
+from talemate.emit import emit
+from talemate.exceptions import LLMAccuracyError
+from talemate.prompts import LoopedPrompt, Prompt
 
 if TYPE_CHECKING:
     from talemate.tale_mate import Character
 
 log = structlog.get_logger("talemate.agents.creator.character")
 
-def validate(k,v):
+
+def validate(k, v):
     if k and k.lower() == "gender":
         return v.lower().strip()
     if k and k.lower() == "age":
         try:
             return int(v.split("\n")[0].strip())
         except (ValueError, TypeError):
-            raise LLMAccuracyError("Was unable to get a valid age from the response", model_name=None)
-            
+            raise LLMAccuracyError(
+                "Was unable to get a valid age from the response", model_name=None
+            )
+
     return v.strip().strip("\n")
 
-DEFAULT_CONTENT_CONTEXT="a fun and engaging adventure aimed at an adult audience."
+
+DEFAULT_CONTENT_CONTEXT = "a fun and engaging adventure aimed at an adult audience."
+
 
 class CharacterCreatorMixin:
     """
     Adds character creation functionality to the creator agent
     """
-    
+
     ## NEW
-    
+
     @set_processing
     async def create_character_attributes(
         self,
@@ -48,8 +54,6 @@ class CharacterCreatorMixin:
         custom_attributes: dict[str, str] = dict(),
         predefined_attributes: dict[str, str] = dict(),
     ):
-        
-        
         def spice(prompt, spices):
             # generate number from 0 to 1 and if its smaller than use_spice
             # select a random spice from the list and return it formatted
@@ -57,69 +61,74 @@ class CharacterCreatorMixin:
             if random.random() < use_spice:
                 spice = random.choice(spices)
                 return prompt.format(spice=spice)
-            return ""    
-        
+            return ""
+
         # drop any empty attributes from predefined_attributes
-        
-        predefined_attributes = {k:v for k,v in predefined_attributes.items() if v}
-        
-        prompt = Prompt.get(f"creator.character-attributes-{template}", vars={
-            "character_prompt": character_prompt,
-            "template": template,
-            "spice": spice,
-            "content_context": content_context,
-            "custom_attributes": custom_attributes,
-            "character_sheet": LoopedPrompt(
-                validate_value=validate,
-                on_update=attribute_callback,
-                generated=predefined_attributes,
-            ),
-        })
+
+        predefined_attributes = {k: v for k, v in predefined_attributes.items() if v}
+
+        prompt = Prompt.get(
+            f"creator.character-attributes-{template}",
+            vars={
+                "character_prompt": character_prompt,
+                "template": template,
+                "spice": spice,
+                "content_context": content_context,
+                "custom_attributes": custom_attributes,
+                "character_sheet": LoopedPrompt(
+                    validate_value=validate,
+                    on_update=attribute_callback,
+                    generated=predefined_attributes,
+                ),
+            },
+        )
         await prompt.loop(self.client, "character_sheet", kind="create_concise")
-        
+
         return prompt.vars["character_sheet"].generated
-        
-    
-    
+
     @set_processing
     async def create_character_description(
-        self, 
-        character:Character, 
+        self,
+        character: Character,
         content_context: str = DEFAULT_CONTENT_CONTEXT,
     ):
-        
-        description = await Prompt.request(f"creator.character-description", self.client, "create", vars={
-            "character": character,
-            "content_context": content_context,
-        })
-        
+        description = await Prompt.request(
+            f"creator.character-description",
+            self.client,
+            "create",
+            vars={
+                "character": character,
+                "content_context": content_context,
+            },
+        )
+
         return description.strip()
 
-        
-    
     @set_processing
     async def create_character_details(
-        self, 
+        self,
         character: Character,
         template: str,
         detail_callback: Callable = lambda question, answer: None,
         questions: list[str] = None,
         content_context: str = DEFAULT_CONTENT_CONTEXT,
     ):
-        prompt = Prompt.get(f"creator.character-details-{template}", vars={
-            "character_details": LoopedPrompt(
-                validate_value=validate,
-                on_update=detail_callback,
-            ),
-            "template": template,
-            "content_context": content_context,
-            "character": character,
-            "custom_questions": questions or [],
-        })
+        prompt = Prompt.get(
+            f"creator.character-details-{template}",
+            vars={
+                "character_details": LoopedPrompt(
+                    validate_value=validate,
+                    on_update=detail_callback,
+                ),
+                "template": template,
+                "content_context": content_context,
+                "character": character,
+                "custom_questions": questions or [],
+            },
+        )
         await prompt.loop(self.client, "character_details", kind="create_concise")
         return prompt.vars["character_details"].generated
 
-    
     @set_processing
     async def create_character_example_dialogue(
         self,
@@ -131,97 +140,135 @@ class CharacterCreatorMixin:
         example_callback: Callable = lambda example: None,
         rules_callback: Callable = lambda rules: None,
     ):
-        
-        dialogue_rules = await Prompt.request(f"creator.character-dialogue-rules", self.client, "create", vars={
-            "guide": guide,
-            "character": character,
-            "examples": examples or [],
-            "content_context": content_context,
-        })
-        
+        dialogue_rules = await Prompt.request(
+            f"creator.character-dialogue-rules",
+            self.client,
+            "create",
+            vars={
+                "guide": guide,
+                "character": character,
+                "examples": examples or [],
+                "content_context": content_context,
+            },
+        )
+
         log.info("dialogue_rules", dialogue_rules=dialogue_rules)
-        
+
         if rules_callback:
             rules_callback(dialogue_rules)
-            
-        example_dialogue_prompt = Prompt.get(f"creator.character-example-dialogue-{template}", vars={
-            "guide": guide,
-            "character": character,
-            "examples": examples or [],
-            "content_context": content_context,
-            "dialogue_rules": dialogue_rules,
-            "generated_examples": LoopedPrompt(
-                validate_value=validate,
-                on_update=example_callback,
-            ),
-        })
-        
-        await example_dialogue_prompt.loop(self.client, "generated_examples", kind="create")
-        
+
+        example_dialogue_prompt = Prompt.get(
+            f"creator.character-example-dialogue-{template}",
+            vars={
+                "guide": guide,
+                "character": character,
+                "examples": examples or [],
+                "content_context": content_context,
+                "dialogue_rules": dialogue_rules,
+                "generated_examples": LoopedPrompt(
+                    validate_value=validate,
+                    on_update=example_callback,
+                ),
+            },
+        )
+
+        await example_dialogue_prompt.loop(
+            self.client, "generated_examples", kind="create"
+        )
+
         return example_dialogue_prompt.vars["generated_examples"].generated
 
-            
-            
     @set_processing
     async def determine_content_context_for_character(
         self,
         character: Character,
     ):
-        
-        content_context = await Prompt.request(f"creator.determine-content-context", self.client, "create", vars={
-            "character": character,
-        })
+        content_context = await Prompt.request(
+            f"creator.determine-content-context",
+            self.client,
+            "create",
+            vars={
+                "character": character,
+            },
+        )
         return content_context.strip()
 
-            
     @set_processing
     async def determine_character_attributes(
         self,
         character: Character,
     ):
-        
-        attributes = await Prompt.request(f"creator.determine-character-attributes", self.client, "analyze_long", vars={
-            "character": character,
-        })
+        attributes = await Prompt.request(
+            f"creator.determine-character-attributes",
+            self.client,
+            "analyze_long",
+            vars={
+                "character": character,
+            },
+        )
         return attributes
-    
+
+    @set_processing
+    async def determine_character_name(
+        self,
+        character_name: str,
+        allowed_names: list[str] = None,
+    ) -> str:
+        name = await Prompt.request(
+            f"creator.determine-character-name",
+            self.client,
+            "analyze_freeform_short",
+            vars={
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "character_name": character_name,
+                "allowed_names": allowed_names or [],
+            },
+        )
+        return name.split('"', 1)[0].strip().strip(".").strip()
+
     @set_processing
     async def determine_character_description(
-        self,
-        character: Character,
-        text:str=""
+        self, character: Character, text: str = ""
     ):
-        
-        description = await Prompt.request(f"creator.determine-character-description", self.client, "create", vars={
-            "character": character,
-            "scene": self.scene,
-            "text": text,
-            "max_tokens": self.client.max_token_length,
-        })
+        description = await Prompt.request(
+            f"creator.determine-character-description",
+            self.client,
+            "create",
+            vars={
+                "character": character,
+                "scene": self.scene,
+                "text": text,
+                "max_tokens": self.client.max_token_length,
+            },
+        )
         return description.strip()
-    
+
     @set_processing
     async def determine_character_goals(
         self,
         character: Character,
         goal_instructions: str,
     ):
-        
-        goals = await Prompt.request(f"creator.determine-character-goals", self.client, "create", vars={
-            "character": character,
-            "scene": self.scene,
-            "goal_instructions": goal_instructions,
-            "npc_name": character.name,
-            "player_name": self.scene.get_player_character().name,
-            "max_tokens": self.client.max_token_length,
-        })
-        
+        goals = await Prompt.request(
+            f"creator.determine-character-goals",
+            self.client,
+            "create",
+            vars={
+                "character": character,
+                "scene": self.scene,
+                "goal_instructions": goal_instructions,
+                "npc_name": character.name,
+                "player_name": self.scene.get_player_character().name,
+                "max_tokens": self.client.max_token_length,
+            },
+        )
+
         log.debug("determine_character_goals", goals=goals, character=character)
         await character.set_detail("goals", goals.strip())
-        
+
         return goals.strip()
-    
-    
+
     @set_processing
     async def generate_character_from_text(
         self,
@@ -229,11 +276,8 @@ class CharacterCreatorMixin:
         template: str,
         content_context: str = DEFAULT_CONTENT_CONTEXT,
     ):
-        
         base_attributes = await self.create_character_attributes(
             character_prompt=text,
             template=template,
             content_context=content_context,
         )
-        
-        

src/talemate/agents/creator/scenario.py

@@ -1,36 +1,35 @@
-from talemate.emit import emit, wait_for_input_yesno
-import re
 import random
+import re
 
-from talemate.prompts import Prompt
 from talemate.agents.base import set_processing
+from talemate.emit import emit, wait_for_input_yesno
+from talemate.prompts import Prompt
+
 
 class ScenarioCreatorMixin:
-    
     """
     Adds scenario creation functionality to the creator agent
     """
-    
+
     @set_processing
     async def create_scene_description(
         self,
-        prompt:str,
-        content_context:str,
+        prompt: str,
+        content_context: str,
     ):
-        
         """
         Creates a new scene.
-        
+
         Arguments:
-        
+
             prompt (str): The prompt to use to create the scene.
-            
+
             content_context (str): The content context to use for the scene.
-            
+
             callback (callable): A callback to call when the scene has been created.
         """
         scene = self.scene
-        
+
         description = await Prompt.request(
             "creator.scenario-description",
             self.client,
@@ -40,35 +39,32 @@ class ScenarioCreatorMixin:
                 "content_context": content_context,
                 "max_tokens": self.client.max_token_length,
                 "scene": scene,
-            }
+            },
         )
         description = description.strip()
-        
+
         return description
-            
-    
-    
+
     @set_processing
     async def create_scene_name(
         self,
-        prompt:str,
-        content_context:str,
-        description:str,
+        prompt: str,
+        content_context: str,
+        description: str,
     ):
-        
         """
         Generates a scene name.
-        
+
         Arguments:
-        
+
             prompt (str): The prompt to use to generate the scene name.
-            
+
             content_context (str): The content context to use for the scene.
-            
+
             description (str): The description of the scene.
         """
         scene = self.scene
-        
+
         name = await Prompt.request(
             "creator.scenario-name",
             self.client,
@@ -78,37 +74,35 @@ class ScenarioCreatorMixin:
                 "content_context": content_context,
                 "description": description,
                 "scene": scene,
-            }
+            },
         )
-        name = name.strip().strip('.!').replace('"','')
+        name = name.strip().strip(".!").replace('"', "")
         return name
-    
-    
+
     @set_processing
     async def create_scene_intro(
         self,
-        prompt:str,
-        content_context:str,
-        description:str,
-        name:str,
+        prompt: str,
+        content_context: str,
+        description: str,
+        name: str,
     ):
-        
         """
         Generates a scene introduction.
-        
+
         Arguments:
-        
+
             prompt (str): The prompt to use to generate the scene introduction.
-            
+
             content_context (str): The content context to use for the scene.
-            
+
             description (str): The description of the scene.
-            
+
             name (str): The name of the scene.
         """
-        
+
         scene = self.scene
-        
+
         intro = await Prompt.request(
             "creator.scenario-intro",
             self.client,
@@ -119,17 +113,19 @@ class ScenarioCreatorMixin:
                 "description": description,
                 "name": name,
                 "scene": scene,
-            }
+            },
         )
         intro = intro.strip()
         return intro
-    
+
     @set_processing
-    async def determine_scenario_description(
-        self,
-        text:str
-    ):
-        description = await Prompt.request(f"creator.determine-scenario-description", self.client, "analyze_long", vars={
-            "text": text,
-        })
-        return description
+    async def determine_scenario_description(self, text: str):
+        description = await Prompt.request(
+            f"creator.determine-scenario-description",
+            self.client,
+            "analyze_long",
+            vars={
+                "text": text,
+            },
+        )
+        return description

src/talemate/agents/custom/__init__.py

@@ -0,0 +1,34 @@
+import importlib
+import os
+
+import structlog
+
+log = structlog.get_logger("talemate.agents.custom")
+
+# import every submodule in this directory
+#
+# each directory in this directory is a submodule
+
+# get the current directory
+current_directory = os.path.dirname(__file__)
+
+# get all subdirectories
+subdirectories = [
+    os.path.join(current_directory, name)
+    for name in os.listdir(current_directory)
+    if os.path.isdir(os.path.join(current_directory, name))
+]
+
+# import every submodule
+
+for subdirectory in subdirectories:
+    # get the name of the submodule
+    submodule_name = os.path.basename(subdirectory)
+
+    if submodule_name.startswith("__"):
+        continue
+
+    log.info("activating custom agent", module=submodule_name)
+
+    # import the submodule
+    importlib.import_module(f".{submodule_name}", __package__)

src/talemate/agents/custom/third_party_agents_go_here.txt

@@ -0,0 +1,5 @@
+Each agent should be in its own subdirectory.
+
+The subdirectory itself must be a valid python module.
+
+Check out docs/dev/agents/example/test for a very simplistic custom agent example.

src/talemate/agents/director.py

@@ -1,200 +1,254 @@
 from __future__ import annotations
 
 import asyncio
-import re
 import random
-import structlog
+import re
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
-import talemate.util as util
-from talemate.emit import wait_for_input, emit
-import talemate.emit.async_signals
-from talemate.prompts import Prompt
-from talemate.scene_message import NarratorMessage, DirectorMessage
-from talemate.automated_action import AutomatedAction
+import structlog
+
 import talemate.automated_action as automated_action
-from talemate.agents.conversation import ConversationAgentEmission
-from .registry import register
-from .base import set_processing, AgentAction, AgentActionConfig, Agent
-from talemate.events import GameLoopActorIterEvent, GameLoopStartEvent, SceneStateEvent
+import talemate.emit.async_signals
 import talemate.instance as instance
+import talemate.util as util
+from talemate.agents.conversation import ConversationAgentEmission
+from talemate.automated_action import AutomatedAction
+from talemate.emit import emit, wait_for_input
+from talemate.events import GameLoopActorIterEvent, GameLoopStartEvent, SceneStateEvent
+from talemate.prompts import Prompt
+from talemate.scene_message import DirectorMessage, NarratorMessage
+
+from .base import Agent, AgentAction, AgentActionConfig, set_processing
+from .registry import register
 
 if TYPE_CHECKING:
     from talemate import Actor, Character, Player, Scene
 
 log = structlog.get_logger("talemate.agent.director")
 
+
 @register()
 class DirectorAgent(Agent):
     agent_type = "director"
     verbose_name = "Director"
-    
+
     def __init__(self, client, **kwargs):
         self.is_enabled = True
         self.client = client
         self.next_direct_character = {}
         self.next_direct_scene = 0
         self.actions = {
-            "direct": AgentAction(enabled=True, label="Direct", description="Will attempt to direct the scene. Runs automatically after AI dialogue (n turns).", config={
-                "turns": AgentActionConfig(type="number", label="Turns", description="Number of turns to wait before directing the sceen", value=5, min=1, max=100, step=1),
-                "direct_scene": AgentActionConfig(type="bool", label="Direct Scene", description="If enabled, the scene will be directed through narration", value=True),
-                "direct_actors": AgentActionConfig(type="bool", label="Direct Actors", description="If enabled, direction will be given to actors based on their goals.", value=True),
-            }),
+            "direct": AgentAction(
+                enabled=True,
+                label="Direct",
+                description="Will attempt to direct the scene. Runs automatically after AI dialogue (n turns).",
+                config={
+                    "turns": AgentActionConfig(
+                        type="number",
+                        label="Turns",
+                        description="Number of turns to wait before directing the sceen",
+                        value=5,
+                        min=1,
+                        max=100,
+                        step=1,
+                    ),
+                    "direct_scene": AgentActionConfig(
+                        type="bool",
+                        label="Direct Scene",
+                        description="If enabled, the scene will be directed through narration",
+                        value=True,
+                    ),
+                    "direct_actors": AgentActionConfig(
+                        type="bool",
+                        label="Direct Actors",
+                        description="If enabled, direction will be given to actors based on their goals.",
+                        value=True,
+                    ),
+                },
+            ),
         }
-        
+
     @property
     def enabled(self):
         return self.is_enabled
-    
+
     @property
     def has_toggle(self):
         return True
-        
+
     @property
     def experimental(self):
         return True
-    
+
     def connect(self, scene):
         super().connect(scene)
-        talemate.emit.async_signals.get("agent.conversation.before_generate").connect(self.on_conversation_before_generate)
-        talemate.emit.async_signals.get("game_loop_actor_iter").connect(self.on_player_dialog)
+        talemate.emit.async_signals.get("agent.conversation.before_generate").connect(
+            self.on_conversation_before_generate
+        )
+        talemate.emit.async_signals.get("game_loop_actor_iter").connect(
+            self.on_player_dialog
+        )
         talemate.emit.async_signals.get("scene_init").connect(self.on_scene_init)
-        
+
     async def on_scene_init(self, event: SceneStateEvent):
         """
         If game state instructions specify to be run at the start of the game loop
         we will run them here.
         """
-        
+
         if not self.enabled:
             if self.scene.game_state.has_scene_instructions:
-               self.is_enabled = True
-               log.warning("on_scene_init - enabling director", scene=self.scene)
+                self.is_enabled = True
+                log.warning("on_scene_init - enabling director", scene=self.scene)
             else:
                 return
-        
+
         if not self.scene.game_state.has_scene_instructions:
             return
 
         if not self.scene.game_state.ops.run_on_start:
             return
-        
+
         log.info("on_game_loop_start - running game state instructions")
         await self.run_gamestate_instructions()
-        
-    async def on_conversation_before_generate(self, event:ConversationAgentEmission):
+
+    async def on_conversation_before_generate(self, event: ConversationAgentEmission):
         log.info("on_conversation_before_generate", director_enabled=self.enabled)
         if not self.enabled:
             return
-        
+
         await self.direct(event.character)
-    
-    async def on_player_dialog(self, event:GameLoopActorIterEvent):
-    
+
+    async def on_player_dialog(self, event: GameLoopActorIterEvent):
         if not self.enabled:
             return
-        
+
         if not self.scene.game_state.has_scene_instructions:
             return
 
         if not event.actor.character.is_player:
             return
-        
+
         if event.game_loop.had_passive_narration:
-            log.debug("director.on_player_dialog", skip=True, had_passive_narration=event.game_loop.had_passive_narration)
+            log.debug(
+                "director.on_player_dialog",
+                skip=True,
+                had_passive_narration=event.game_loop.had_passive_narration,
+            )
             return
 
         event.game_loop.had_passive_narration = await self.direct(None)
-        
+
     async def direct(self, character: Character) -> bool:
-        
         if not self.actions["direct"].enabled:
             return False
-        
+
         if character:
-            
             if not self.actions["direct"].config["direct_actors"].value:
-                log.info("direct", skip=True, reason="direct_actors disabled", character=character)
+                log.info(
+                    "direct",
+                    skip=True,
+                    reason="direct_actors disabled",
+                    character=character,
+                )
                 return False
-            
-            # character direction, see if there are character goals 
+
+            # character direction, see if there are character goals
             # defined
             character_goals = character.get_detail("goals")
             if not character_goals:
                 log.info("direct", skip=True, reason="no goals", character=character)
                 return False
-        
+
             next_direct = self.next_direct_character.get(character.name, 0)
-            
-            if next_direct % self.actions["direct"].config["turns"].value != 0 or next_direct == 0:
-                log.info("direct", skip=True, next_direct=next_direct, character=character)
+
+            if (
+                next_direct % self.actions["direct"].config["turns"].value != 0
+                or next_direct == 0
+            ):
+                log.info(
+                    "direct", skip=True, next_direct=next_direct, character=character
+                )
                 self.next_direct_character[character.name] = next_direct + 1
                 return False
-            
+
             self.next_direct_character[character.name] = 0
             await self.direct_scene(character, character_goals)
             return True
         else:
-            
             if not self.actions["direct"].config["direct_scene"].value:
                 log.info("direct", skip=True, reason="direct_scene disabled")
                 return False
-            
+
             # no character, see if there are NPC characters at all
             # if not we always want to direct narration
-            always_direct = (not self.scene.npc_character_names)
-            
+            always_direct = (
+                not self.scene.npc_character_names
+                or self.scene.game_state.ops.always_direct
+            )
+
             next_direct = self.next_direct_scene
-            
-            if next_direct % self.actions["direct"].config["turns"].value != 0 or next_direct == 0:
+
+            if (
+                next_direct % self.actions["direct"].config["turns"].value != 0
+                or next_direct == 0
+            ):
                 if not always_direct:
                     log.info("direct", skip=True, next_direct=next_direct)
                     self.next_direct_scene += 1
                     return False
-        
+
             self.next_direct_scene = 0
             await self.direct_scene(None, None)
             return True
-        
+
     @set_processing
     async def run_gamestate_instructions(self):
         """
         Run game state instructions, if they exist.
         """
-        
+
         if not self.scene.game_state.has_scene_instructions:
             return
-        
+
         await self.direct_scene(None, None)
-        
+
     @set_processing
-    async def direct_scene(self, character: Character, prompt:str):
-        
+    async def direct_scene(self, character: Character, prompt: str):
         if not character and self.scene.game_state.game_won:
             # we are not directing a character, and the game has been won
             # so we don't need to direct the scene any further
             return
-        
+
         if character:
-            
             # direct a character
-    
-            response = await Prompt.request("director.direct-character", self.client, "director", vars={
-                "max_tokens": self.client.max_token_length,
-                "scene": self.scene,
-                "prompt": prompt,
-                "character": character,
-                "player_character": self.scene.get_player_character(),
-                "game_state": self.scene.game_state,
-            })
-            
+
+            response = await Prompt.request(
+                "director.direct-character",
+                self.client,
+                "director",
+                vars={
+                    "max_tokens": self.client.max_token_length,
+                    "scene": self.scene,
+                    "prompt": prompt,
+                    "character": character,
+                    "player_character": self.scene.get_player_character(),
+                    "game_state": self.scene.game_state,
+                },
+            )
+
             if "#" in response:
                 response = response.split("#")[0]
-            
-            log.info("direct_character", character=character, prompt=prompt, response=response)
-            
+
+            log.info(
+                "direct_character",
+                character=character,
+                prompt=prompt,
+                response=response,
+            )
+
             response = response.strip().split("\n")[0].strip()
-            #response += f" (current story goal: {prompt})"
+            # response += f" (current story goal: {prompt})"
             message = DirectorMessage(response, source=character.name)
             emit("director", message, character=character)
             self.scene.push_history(message)
@@ -202,26 +256,71 @@ class DirectorAgent(Agent):
             # run scene instructions
             self.scene.game_state.scene_instructions
 
+    @set_processing
+    async def persist_characters_from_worldstate(
+        self, exclude: list[str] = None
+    ) -> List[Character]:
+        log.warning(
+            "persist_characters_from_worldstate",
+            world_state_characters=self.scene.world_state.characters,
+            scene_characters=self.scene.character_names,
+        )
+
+        created_characters = []
+
+        for character_name in self.scene.world_state.characters.keys():
+
+            if exclude and character_name.lower() in exclude:
+                continue
+
+            if character_name in self.scene.character_names:
+                continue
+
+            character = await self.persist_character(name=character_name)
+
+            created_characters.append(character)
+
+        self.scene.emit_status()
+
+        return created_characters
+
     @set_processing
     async def persist_character(
-        self, 
-        name:str, 
-        content:str = None,
-        attributes:str = None,
+        self,
+        name: str,
+        content: str = None,
+        attributes: str = None,
     ):
-        
         world_state = instance.get_agent("world_state")
         creator = instance.get_agent("creator")
+
         self.scene.log.debug("persist_character", name=name)
+        name = await creator.determine_character_name(name)
+        self.scene.log.debug("persist_character", adjusted_name=name)
 
         character = self.scene.Character(name=name)
-        character.color = random.choice(['#F08080', '#FFD700', '#90EE90', '#ADD8E6', '#DDA0DD', '#FFB6C1', '#FAFAD2', '#D3D3D3', '#B0E0E6', '#FFDEAD'])
+        character.color = random.choice(
+            [
+                "#F08080",
+                "#FFD700",
+                "#90EE90",
+                "#ADD8E6",
+                "#DDA0DD",
+                "#FFB6C1",
+                "#FAFAD2",
+                "#D3D3D3",
+                "#B0E0E6",
+                "#FFDEAD",
+            ]
+        )
 
         if not attributes:
-            attributes = await world_state.extract_character_sheet(name=name, text=content)
+            attributes = await world_state.extract_character_sheet(
+                name=name, text=content
+            )
         else:
             attributes = world_state._parse_character_sheet(attributes)
-        
+
         self.scene.log.debug("persist_character", attributes=attributes)
 
         character.base_attributes = attributes
@@ -232,35 +331,54 @@ class DirectorAgent(Agent):
 
         self.scene.log.debug("persist_character", description=description)
 
-        actor = self.scene.Actor(character=character, agent=instance.get_agent("conversation"))
+        actor = self.scene.Actor(
+            character=character, agent=instance.get_agent("conversation")
+        )
 
         await self.scene.add_actor(actor)
         self.scene.emit_status()
-        
+
         return character
-        
+
     @set_processing
-    async def update_content_context(self, content:str=None, extra_choices:list[str]=None):
-        
+    async def update_content_context(
+        self, content: str = None, extra_choices: list[str] = None
+    ):
         if not content:
-            content = "\n".join(self.scene.context_history(sections=False, min_dialogue=25, budget=2048))
-            
-        response = await Prompt.request("world_state.determine-content-context", self.client, "analyze_freeform", vars={
-            "content": content,
-            "extra_choices": extra_choices or [],
-        })
-        
+            content = "\n".join(
+                self.scene.context_history(sections=False, min_dialogue=25, budget=2048)
+            )
+
+        response = await Prompt.request(
+            "world_state.determine-content-context",
+            self.client,
+            "analyze_freeform",
+            vars={
+                "content": content,
+                "extra_choices": extra_choices or [],
+            },
+        )
+
         self.scene.context = response.strip()
         self.scene.emit_status()
-        
-    def inject_prompt_paramters(self, prompt_param: dict, kind: str, agent_function_name: str):
-        log.debug("inject_prompt_paramters", prompt_param=prompt_param, kind=kind, agent_function_name=agent_function_name)
+
+    def inject_prompt_paramters(
+        self, prompt_param: dict, kind: str, agent_function_name: str
+    ):
+        log.debug(
+            "inject_prompt_paramters",
+            prompt_param=prompt_param,
+            kind=kind,
+            agent_function_name=agent_function_name,
+        )
         character_names = [f"\n{c.name}:" for c in self.scene.get_characters()]
         if prompt_param.get("extra_stopping_strings") is None:
             prompt_param["extra_stopping_strings"] = []
         prompt_param["extra_stopping_strings"] += character_names + ["#"]
         if agent_function_name == "update_content_context":
             prompt_param["extra_stopping_strings"] += ["\n"]
-        
-    def allow_repetition_break(self, kind: str, agent_function_name: str, auto:bool=False):
-        return True
+
+    def allow_repetition_break(
+        self, kind: str, agent_function_name: str, auto: bool = False
+    ):
+        return True

src/talemate/agents/editor.py

@@ -1,30 +1,30 @@
 from __future__ import annotations
 
 import asyncio
+import re
+import time
 import traceback
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
+import structlog
+
 import talemate.data_objects as data_objects
-import talemate.util as util
 import talemate.emit.async_signals
+import talemate.util as util
 from talemate.prompts import Prompt
 from talemate.scene_message import DirectorMessage, TimePassageMessage
 
-from .base import Agent, set_processing, AgentAction, AgentActionConfig
+from .base import Agent, AgentAction, AgentActionConfig, set_processing
 from .registry import register
 
-import structlog
-
-import time
-import re
-
 if TYPE_CHECKING:
-    from talemate.tale_mate import Actor, Character, Scene
     from talemate.agents.conversation import ConversationAgentEmission
     from talemate.agents.narrator import NarratorAgentEmission
+    from talemate.tale_mate import Actor, Character, Scene
 
 log = structlog.get_logger("talemate.agents.editor")
 
+
 @register()
 class EditorAgent(Agent):
     """
@@ -35,175 +35,159 @@ class EditorAgent(Agent):
 
     agent_type = "editor"
     verbose_name = "Editor"
-    
+
     def __init__(self, client, **kwargs):
         self.client = client
         self.is_enabled = True
         self.actions = {
-            "edit_dialogue": AgentAction(enabled=False, label="Edit dialogue", description="Will attempt to improve the quality of dialogue based on the character and scene. Runs automatically after each AI dialogue."),
-            "fix_exposition": AgentAction(enabled=True, label="Fix exposition", description="Will attempt to fix exposition and emotes, making sure they are displayed in italics. Runs automatically after each AI dialogue.", config={
-                "narrator": AgentActionConfig(type="bool", label="Fix narrator messages", description="Will attempt to fix exposition issues in narrator messages", value=True),
-            }),
-            "add_detail": AgentAction(enabled=False, label="Add detail", description="Will attempt to add extra detail and exposition to the dialogue. Runs automatically after each AI dialogue.")
+            "fix_exposition": AgentAction(
+                enabled=True,
+                label="Fix exposition",
+                description="Will attempt to fix exposition and emotes, making sure they are displayed in italics. Runs automatically after each AI dialogue.",
+                config={
+                    "narrator": AgentActionConfig(
+                        type="bool",
+                        label="Fix narrator messages",
+                        description="Will attempt to fix exposition issues in narrator messages",
+                        value=True,
+                    ),
+                },
+            ),
+            "add_detail": AgentAction(
+                enabled=False,
+                label="Add detail",
+                description="Will attempt to add extra detail and exposition to the dialogue. Runs automatically after each AI dialogue.",
+            ),
         }
-        
+
     @property
     def enabled(self):
         return self.is_enabled
-    
+
     @property
     def has_toggle(self):
         return True
-        
+
     @property
     def experimental(self):
         return True
 
     def connect(self, scene):
         super().connect(scene)
-        talemate.emit.async_signals.get("agent.conversation.generated").connect(self.on_conversation_generated)
-        talemate.emit.async_signals.get("agent.narrator.generated").connect(self.on_narrator_generated)
-        
-    async def on_conversation_generated(self, emission:ConversationAgentEmission):
+        talemate.emit.async_signals.get("agent.conversation.generated").connect(
+            self.on_conversation_generated
+        )
+        talemate.emit.async_signals.get("agent.narrator.generated").connect(
+            self.on_narrator_generated
+        )
+
+    async def on_conversation_generated(self, emission: ConversationAgentEmission):
         """
         Called when a conversation is generated
         """
-        
+
         if not self.enabled:
             return
-        
+
         log.info("editing conversation", emission=emission)
-        
+
         edited = []
         for text in emission.generation:
+            edit = await self.add_detail(text, emission.character)
+
+            edit = await self.fix_exposition(edit, emission.character)
 
-            
-            edit = await self.add_detail(
-                text,
-                emission.character
-            )
-            
-            edit = await self.edit_conversation(
-                edit,
-                emission.character
-            )
-            
-            edit = await self.fix_exposition(
-                edit,
-                emission.character
-            )
-            
             edited.append(edit)
-            
+
         emission.generation = edited
-        
-    async def on_narrator_generated(self, emission:NarratorAgentEmission):
+
+    async def on_narrator_generated(self, emission: NarratorAgentEmission):
         """
         Called when a narrator message is generated
         """
-        
+
         if not self.enabled:
             return
-        
+
         log.info("editing narrator", emission=emission)
-        
+
         edited = []
-        
+
         for text in emission.generation:
             edit = await self.fix_exposition_on_narrator(text)
             edited.append(edit)
-            
+
         emission.generation = edited
-        
-        
+
     @set_processing
-    async def edit_conversation(self, content:str, character:Character):
-        """
-        Edits a conversation
-        """
-        
-        if not self.actions["edit_dialogue"].enabled:
-            return content
-        
-        response = await Prompt.request("editor.edit-dialogue", self.client, "edit_dialogue", vars={
-            "content": content,
-            "character": character,
-            "scene": self.scene,
-            "max_length": self.client.max_token_length
-        })
-        
-        response = response.split("[end]")[0]
-        
-        response = util.replace_exposition_markers(response)
-        response = util.clean_dialogue(response, main_name=character.name)        
-        response = util.strip_partial_sentences(response)
-        
-        return response
-        
-    @set_processing
-    async def fix_exposition(self, content:str, character:Character):
+    async def fix_exposition(self, content: str, character: Character):
         """
         Edits a text to make sure all narrative exposition and emotes is encased in *
         """
-        
+
         if not self.actions["fix_exposition"].enabled:
             return content
-        
+
         if not character.is_player:
-            if '"' not in content and '*' not in content:
+            if '"' not in content and "*" not in content:
                 content = util.strip_partial_sentences(content)
                 character_prefix = f"{character.name}: "
                 message = content.split(character_prefix)[1]
-                content = f"{character_prefix}*{message.strip('*')}*"
+                content = f'{character_prefix}"{message.strip()}"'
                 return content
             elif '"' in content:
-                
                 # silly hack to clean up some LLMs that always start with a quote
                 # even though the immediate next thing is a narration (indicated by *)
-                content = content.replace(f"{character.name}: \"*", f"{character.name}: *")
-                
-        content = util.clean_dialogue(content, main_name=character.name)        
+                content = content.replace(
+                    f'{character.name}: "*', f"{character.name}: *"
+                )
+
+        content = util.clean_dialogue(content, main_name=character.name)
         content = util.strip_partial_sentences(content)
         content = util.ensure_dialog_format(content, talking_character=character.name)
-        
+
         return content
-    
+
     @set_processing
-    async def fix_exposition_on_narrator(self, content:str):
-        
+    async def fix_exposition_on_narrator(self, content: str):
         if not self.actions["fix_exposition"].enabled:
             return content
-        
+
         if not self.actions["fix_exposition"].config["narrator"].value:
             return content
-        
+
         content = util.strip_partial_sentences(content)
-        
+
         if '"' not in content:
             content = f"*{content.strip('*')}*"
         else:
             content = util.ensure_dialog_format(content)
-               
+
         return content
-    
+
     @set_processing
-    async def add_detail(self, content:str, character:Character):
+    async def add_detail(self, content: str, character: Character):
         """
         Edits a text to increase its length and add extra detail and exposition
         """
-        
+
         if not self.actions["add_detail"].enabled:
             return content
-        
-        response = await Prompt.request("editor.add-detail", self.client, "edit_add_detail", vars={
-            "content": content,
-            "character": character,
-            "scene": self.scene,
-            "max_length": self.client.max_token_length
-        })
-        
+
+        response = await Prompt.request(
+            "editor.add-detail",
+            self.client,
+            "edit_add_detail",
+            vars={
+                "content": content,
+                "character": character,
+                "scene": self.scene,
+                "max_length": self.client.max_token_length,
+            },
+        )
+
         response = util.replace_exposition_markers(response)
-        response = util.clean_dialogue(response, main_name=character.name)        
+        response = util.clean_dialogue(response, main_name=character.name)
         response = util.strip_partial_sentences(response)
-        
-        return response
+
+        return response

src/talemate/agents/memory.py

@@ -1,19 +1,21 @@
 from __future__ import annotations
 
 import asyncio
+import functools
 import os
+import shutil
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
+
+import structlog
 from chromadb.config import Settings
+
 import talemate.events as events
 import talemate.util as util
+from talemate.agents.base import set_processing
+from talemate.config import load_config
+from talemate.context import scene_is_loading
 from talemate.emit import emit
 from talemate.emit.signals import handlers
-from talemate.context import scene_is_loading
-from talemate.config import load_config
-from talemate.agents.base import set_processing
-import structlog
-import shutil
-import functools
 
 try:
     import chromadb
@@ -28,19 +30,20 @@ if not chromadb:
     log.info("ChromaDB not found, disabling Chroma agent")
 
 
-from .base import Agent
+from .base import Agent, AgentDetail
+
 
 class MemoryDocument(str):
-    
     def __new__(cls, text, meta, id, raw):
         inst = super().__new__(cls, text)
 
         inst.meta = meta
         inst.id = id
         inst.raw = raw
-        
+
         return inst
 
+
 class MemoryAgent(Agent):
     """
     An agent that can be used to maintain and access a memory of the world
@@ -52,10 +55,11 @@ class MemoryAgent(Agent):
 
     @property
     def readonly(self):
-        
-        if scene_is_loading.get() and not getattr(self.scene, "_memory_never_persisted", False):
+        if scene_is_loading.get() and not getattr(
+            self.scene, "_memory_never_persisted", False
+        ):
             return True
-        
+
         return False
 
     @property
@@ -72,9 +76,9 @@ class MemoryAgent(Agent):
         self.memory_tracker = {}
         self.config = load_config()
         self._ready_to_add = False
-        
+
         handlers["config_saved"].connect(self.on_config_saved)
-        
+
     def on_config_saved(self, event):
         openai_key = self.openai_api_key
         self.config = load_config()
@@ -92,23 +96,68 @@ class MemoryAgent(Agent):
         raise NotImplementedError()
 
     @set_processing
-    async def add(self, text, character=None, uid=None, ts:str=None, **kwargs):
+    async def add(self, text, character=None, uid=None, ts: str = None, **kwargs):
         if not text:
             return
         if self.readonly:
             log.debug("memory agent", status="readonly")
             return
-        
+
         while not self._ready_to_add:
             await asyncio.sleep(0.1)
-            
-        log.debug("memory agent add", text=text[:50], character=character, uid=uid, ts=ts, **kwargs)
-         
-        loop = asyncio.get_running_loop()
-        
-        await loop.run_in_executor(None, functools.partial(self._add, text, character, uid=uid, ts=ts, **kwargs))
 
-    def _add(self, text, character=None, ts:str=None, **kwargs):
+        log.debug(
+            "memory agent add",
+            text=text[:50],
+            character=character,
+            uid=uid,
+            ts=ts,
+            **kwargs,
+        )
+
+        loop = asyncio.get_running_loop()
+
+        try:
+            await loop.run_in_executor(
+                None,
+                functools.partial(self._add, text, character, uid=uid, ts=ts, **kwargs),
+            )
+        except AttributeError as e:
+            # not sure how this sometimes happens.
+            # chromadb model None
+            # race condition because we are forcing async context onto it?
+
+            log.error(
+                "memory agent",
+                error="failed to add memory",
+                details=e,
+                text=text[:50],
+                character=character,
+                uid=uid,
+                ts=ts,
+                **kwargs,
+            )
+            await asyncio.sleep(1.0)
+            try:
+                await loop.run_in_executor(
+                    None,
+                    functools.partial(
+                        self._add, text, character, uid=uid, ts=ts, **kwargs
+                    ),
+                )
+            except Exception as e:
+                log.error(
+                    "memory agent",
+                    error="failed to add memory (retried)",
+                    details=e,
+                    text=text[:50],
+                    character=character,
+                    uid=uid,
+                    ts=ts,
+                    **kwargs,
+                )
+
+    def _add(self, text, character=None, ts: str = None, **kwargs):
         raise NotImplementedError()
 
     @set_processing
@@ -119,44 +168,46 @@ class MemoryAgent(Agent):
 
         while not self._ready_to_add:
             await asyncio.sleep(0.1)
- 
-        log.debug("memory agent add many", len=len(objects)) 
- 
+
+        log.debug("memory agent add many", len=len(objects))
+
         loop = asyncio.get_running_loop()
         await loop.run_in_executor(None, self._add_many, objects)
-    
+
     def _add_many(self, objects: list[dict]):
         """
         Add multiple objects to the memory
         """
         raise NotImplementedError()
 
-    def _delete(self, meta:dict):
+    def _delete(self, meta: dict):
         """
         Delete an object from the memory
         """
         raise NotImplementedError()
-    
+
     @set_processing
-    async def delete(self, meta:dict):
+    async def delete(self, meta: dict):
         """
         Delete an object from the memory
         """
         if self.readonly:
             log.debug("memory agent", status="readonly")
             return
-        
+
         while not self._ready_to_add:
             await asyncio.sleep(0.1)
-        
+
         loop = asyncio.get_running_loop()
         await loop.run_in_executor(None, self._delete, meta)
 
     @set_processing
     async def get(self, text, character=None, **query):
         loop = asyncio.get_running_loop()
-        
-        return await loop.run_in_executor(None, functools.partial(self._get, text, character, **query))
+
+        return await loop.run_in_executor(
+            None, functools.partial(self._get, text, character, **query)
+        )
 
     def _get(self, text, character=None, **query):
         raise NotImplementedError()
@@ -165,12 +216,14 @@ class MemoryAgent(Agent):
     async def get_document(self, id):
         loop = asyncio.get_running_loop()
         return await loop.run_in_executor(None, self._get_document, id)
-    
+
     def _get_document(self, id):
         raise NotImplementedError()
 
     def on_archive_add(self, event: events.ArchiveEvent):
-        asyncio.ensure_future(self.add(event.text, uid=event.memory_id, ts=event.ts, typ="history"))
+        asyncio.ensure_future(
+            self.add(event.text, uid=event.memory_id, ts=event.ts, typ="history")
+        )
 
     def on_character_state(self, event: events.CharacterStateEvent):
         asyncio.ensure_future(
@@ -210,10 +263,10 @@ class MemoryAgent(Agent):
         """
 
         memory_context = []
-        
+
         if not query:
             return memory_context
-        
+
         for memory in await self.get(query):
             if memory in memory_context:
                 continue
@@ -227,17 +280,26 @@ class MemoryAgent(Agent):
                 break
         return memory_context
 
-    async def query(self, query:str, max_tokens:int=1000, filter:Callable=lambda x:True, **where):
+    async def query(
+        self,
+        query: str,
+        max_tokens: int = 1000,
+        filter: Callable = lambda x: True,
+        **where,
+    ):
         """
         Get the character memory context for a given character
         """
 
         try:
-            return (await self.multi_query([query], max_tokens=max_tokens, filter=filter, **where))[0]
+            return (
+                await self.multi_query(
+                    [query], max_tokens=max_tokens, filter=filter, **where
+                )
+            )[0]
         except IndexError:
             return None
 
-
     async def multi_query(
         self,
         queries: list[str],
@@ -246,7 +308,7 @@ class MemoryAgent(Agent):
         filter: Callable = lambda x: True,
         formatter: Callable = lambda x: x,
         limit: int = 10,
-        **where
+        **where,
     ):
         """
         Get the character memory context for a given character
@@ -254,10 +316,9 @@ class MemoryAgent(Agent):
 
         memory_context = []
         for query in queries:
-            
             if not query:
                 continue
-            
+
             i = 0
             for memory in await self.get(formatter(query), limit=limit, **where):
                 if memory in memory_context:
@@ -284,15 +345,13 @@ from .registry import register
 
 @register(condition=lambda: chromadb is not None)
 class ChromaDBMemoryAgent(MemoryAgent):
-
     requires_llm_client = False
 
     @property
     def ready(self):
-        
         if self.embeddings == "openai" and not self.openai_api_key:
             return False
-        
+
         if getattr(self, "db_client", None):
             return True
         return False
@@ -301,80 +360,112 @@ class ChromaDBMemoryAgent(MemoryAgent):
     def status(self):
         if self.ready:
             return "active" if not getattr(self, "processing", False) else "busy"
-        
+
         if self.embeddings == "openai" and not self.openai_api_key:
             return "error"
-        
+
         return "waiting"
 
     @property
     def agent_details(self):
-        
+
+        details = {
+            "backend": AgentDetail(
+                icon="mdi-server-outline",
+                value="ChromaDB",
+                description="The backend to use for long-term memory",
+            ).model_dump(),
+            "embeddings": AgentDetail(
+                icon="mdi-cube-unfolded",
+                value=self.embeddings,
+                description="The embeddings model.",
+            ).model_dump(),
+        }
+
         if self.embeddings == "openai" and not self.openai_api_key:
-            return "No OpenAI API key set"
-        
+            # return "No OpenAI API key set"
+            details["error"] = {
+                "icon": "mdi-alert",
+                "value": "No OpenAI API key set",
+                "description": "You must provide an OpenAI API key to use OpenAI embeddings",
+                "color": "error",
+            }
+
+        return details
+
         return f"ChromaDB: {self.embeddings}"
-    
+
     @property
     def embeddings(self):
         """
         Returns which embeddings to use
-        
+
         will read from TM_CHROMADB_EMBEDDINGS env variable and default to 'default' using
         the default embeddings specified by chromadb.
-        
+
         other values are
-        
+
         - openai: use openai embeddings
         - instructor: use instructor embeddings
-        
+
         for `openai`:
-        
+
         you will also need to provide an `OPENAI_API_KEY` env variable
-        
+
         for `instructor`:
-        
+
         you will also need to provide which instructor model to use with the `TM_INSTRUCTOR_MODEL` env variable, which defaults to hkunlp/instructor-xl
-        
+
         additionally you can provide the `TM_INSTRUCTOR_DEVICE` env variable to specify which device to use, which defaults to cpu
         """
-        
+
         embeddings = self.config.get("chromadb").get("embeddings")
-        
-        assert embeddings in ["default", "openai", "instructor"], f"Unknown embeddings {embeddings}"
-        
+
+        assert embeddings in [
+            "default",
+            "openai",
+            "instructor",
+        ], f"Unknown embeddings {embeddings}"
+
         return embeddings
-    
+
     @property
     def USE_OPENAI(self):
         return self.embeddings == "openai"
-    
+
     @property
     def USE_INSTRUCTOR(self):
         return self.embeddings == "instructor"
-    
+
     @property
     def db_name(self):
         return getattr(self, "collection_name", "<unnamed>")
 
     @property
     def openai_api_key(self):
-        return self.config.get("openai",{}).get("api_key")
+        return self.config.get("openai", {}).get("api_key")
 
     def make_collection_name(self, scene):
-        
         if self.USE_OPENAI:
-            suffix = "-openai"
+            model_name = self.config.get("chromadb").get(
+                "openai_model", "text-embedding-3-small"
+            )
+            if model_name == "text-embedding-ada-002":
+                suffix = "-openai"
+            else:
+                suffix = f"-openai-{model_name}"
         elif self.USE_INSTRUCTOR:
             suffix = "-instructor"
-            model = self.config.get("chromadb").get("instructor_model", "hkunlp/instructor-xl")
+            model = self.config.get("chromadb").get(
+                "instructor_model", "hkunlp/instructor-xl"
+            )
             if "xl" in model:
                 suffix += "-xl"
             elif "large" in model:
                 suffix += "-large"
         else:
             suffix = ""
-        
+
         return f"{scene.memory_id}-tm{suffix}"
 
     async def count(self):
@@ -387,9 +478,8 @@ class ChromaDBMemoryAgent(MemoryAgent):
         await loop.run_in_executor(None, self._set_db)
 
     def _set_db(self):
-        
         self._ready_to_add = False
-        
+
         if not getattr(self, "db_client", None):
             log.info("chromadb agent", status="setting up db client to persistent db")
             self.db_client = chromadb.PersistentClient(
@@ -397,49 +487,67 @@ class ChromaDBMemoryAgent(MemoryAgent):
             )
 
         openai_key = self.openai_api_key
-        
+
         self.collection_name = collection_name = self.make_collection_name(self.scene)
-        
-        log.info("chromadb agent", status="setting up db", collection_name=collection_name)
-        
+
+        log.info(
+            "chromadb agent", status="setting up db", collection_name=collection_name
+        )
+
         if self.USE_OPENAI:
-            
             if not openai_key:
-                raise ValueError("You must provide an the openai ai key in the config if you want to use it for chromadb embeddings")
-            
+                raise ValueError(
+                    "You must provide an the openai ai key in the config if you want to use it for chromadb embeddings"
+                )
+
+            model_name = self.config.get("chromadb").get(
+                "openai_model", "text-embedding-3-small"
+            )
+
             log.info(
-                "crhomadb", status="using openai", openai_key=openai_key[:5] + "..."
+                "crhomadb",
+                status="using openai",
+                openai_key=openai_key[:5] + "...",
+                model=model_name,
             )
             openai_ef = embedding_functions.OpenAIEmbeddingFunction(
-                api_key = openai_key,
-                model_name="text-embedding-ada-002",
+                api_key=openai_key,
+                model_name=model_name,
             )
             self.db = self.db_client.get_or_create_collection(
                 collection_name, embedding_function=openai_ef
             )
         elif self.USE_INSTRUCTOR:
-            
-            instructor_device = self.config.get("chromadb").get("instructor_device", "cpu")
-            instructor_model = self.config.get("chromadb").get("instructor_model", "hkunlp/instructor-xl")
-            
-            log.info("chromadb", status="using instructor", model=instructor_model, device=instructor_device)
-            
+            instructor_device = self.config.get("chromadb").get(
+                "instructor_device", "cpu"
+            )
+            instructor_model = self.config.get("chromadb").get(
+                "instructor_model", "hkunlp/instructor-xl"
+            )
+
+            log.info(
+                "chromadb",
+                status="using instructor",
+                model=instructor_model,
+                device=instructor_device,
+            )
+
             # ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-mpnet-base-v2")
             ef = embedding_functions.InstructorEmbeddingFunction(
                 model_name=instructor_model, device=instructor_device
             )
-            
+
             log.info("chromadb", status="embedding function ready")
-            
+
             self.db = self.db_client.get_or_create_collection(
                 collection_name, embedding_function=ef
             )
-            
+
             log.info("chromadb", status="instructor db ready")
         else:
             log.info("chromadb", status="using default embeddings")
             self.db = self.db_client.get_or_create_collection(collection_name)
-        
+
         self.scene._memory_never_persisted = self.db.count() == 0
         log.info("chromadb agent", status="db ready")
         self._ready_to_add = True
@@ -447,17 +555,21 @@ class ChromaDBMemoryAgent(MemoryAgent):
     def clear_db(self):
         if not self.db:
             return
-        
-        log.info("chromadb agent", status="clearing db", collection_name=self.collection_name)
-        
+
+        log.info(
+            "chromadb agent", status="clearing db", collection_name=self.collection_name
+        )
+
         self.db.delete(where={"source": "talemate"})
-        
+
     def drop_db(self):
         if not self.db:
             return
-        
-        log.info("chromadb agent", status="dropping db", collection_name=self.collection_name)
-        
+
+        log.info(
+            "chromadb agent", status="dropping db", collection_name=self.collection_name
+        )
+
         try:
             self.db_client.delete_collection(self.collection_name)
         except ValueError as exc:
@@ -467,31 +579,43 @@ class ChromaDBMemoryAgent(MemoryAgent):
     def close_db(self, scene):
         if not self.db:
             return
-        
-        log.info("chromadb agent", status="closing db", collection_name=self.collection_name)
-        
+
+        log.info(
+            "chromadb agent", status="closing db", collection_name=self.collection_name
+        )
+
         if not scene.saved and not scene.saved_memory_session_id:
             # scene was never saved so we can discard the memory
             collection_name = self.make_collection_name(scene)
-            log.info("chromadb agent", status="discarding memory", collection_name=collection_name)
+            log.info(
+                "chromadb agent",
+                status="discarding memory",
+                collection_name=collection_name,
+            )
             try:
                 self.db_client.delete_collection(collection_name)
             except ValueError as exc:
-                log.error("chromadb agent", error="failed to delete collection", details=exc)
+                log.error(
+                    "chromadb agent", error="failed to delete collection", details=exc
+                )
         elif not scene.saved:
             # scene was saved but memory was never persisted
             # so we need to remove the memory from the db
             self._remove_unsaved_memory()
-        
+
         self.db = None
-        
-    def _add(self, text, character=None, uid=None, ts:str=None, **kwargs):
+
+    def _add(self, text, character=None, uid=None, ts: str = None, **kwargs):
         metadatas = []
         ids = []
         scene = self.scene
-        
+
         if character:
-            meta = {"character": character.name, "source": "talemate", "session": scene.memory_session_id}
+            meta = {
+                "character": character.name,
+                "source": "talemate",
+                "session": scene.memory_session_id,
+            }
             if ts:
                 meta["ts"] = ts
             meta.update(kwargs)
@@ -501,7 +625,11 @@ class ChromaDBMemoryAgent(MemoryAgent):
             id = uid or f"{character.name}-{self.memory_tracker[character.name]}"
             ids = [id]
         else:
-            meta = {"character": "__narrator__", "source": "talemate", "session": scene.memory_session_id}
+            meta = {
+                "character": "__narrator__",
+                "source": "talemate",
+                "session": scene.memory_session_id,
+            }
             if ts:
                 meta["ts"] = ts
             meta.update(kwargs)
@@ -511,17 +639,16 @@ class ChromaDBMemoryAgent(MemoryAgent):
             id = uid or f"__narrator__-{self.memory_tracker['__narrator__']}"
             ids = [id]
 
-        #log.debug("chromadb agent add", text=text, meta=meta, id=id)
+        # log.debug("chromadb agent add", text=text, meta=meta, id=id)
 
         self.db.upsert(documents=[text], metadatas=metadatas, ids=ids)
 
     def _add_many(self, objects: list[dict]):
-        
         documents = []
         metadatas = []
         ids = []
         scene = self.scene
-        
+
         if not objects:
             return
 
@@ -540,52 +667,50 @@ class ChromaDBMemoryAgent(MemoryAgent):
             ids.append(uid)
         self.db.upsert(documents=documents, metadatas=metadatas, ids=ids)
 
-    def _delete(self, meta:dict):
-        
+    def _delete(self, meta: dict):
         if "ids" in meta:
             log.debug("chromadb agent delete", ids=meta["ids"])
             self.db.delete(ids=meta["ids"])
             return
-        
-        where = {"$and": [{k:v} for k,v in meta.items()]}
+
+        where = {"$and": [{k: v} for k, v in meta.items()]}
         self.db.delete(where=where)
         log.debug("chromadb agent delete", meta=meta, where=where)
 
-    def _get(self, text, character=None, limit:int=15, **kwargs):
+    def _get(self, text, character=None, limit: int = 15, **kwargs):
         where = {}
-        
+
         # this doesn't work because chromadb currently doesn't match
         # non existing fields with $ne (or so it seems)
         # where.setdefault("$and", [{"pin_only": {"$ne": True}}])
-        
+
         where.setdefault("$and", [])
-        
+
         character_filtered = False
-        
-        for k,v in kwargs.items():
+
+        for k, v in kwargs.items():
             if k == "character":
                 character_filtered = True
             where["$and"].append({k: v})
 
-
         if character and not character_filtered:
             where["$and"].append({"character": character.name})
-            
+
         if len(where["$and"]) == 1:
             where = where["$and"][0]
         elif not where["$and"]:
             where = None
 
-        #log.debug("crhomadb agent get", text=text, where=where)
+        # log.debug("crhomadb agent get", text=text, where=where)
 
         _results = self.db.query(query_texts=[text], where=where, n_results=limit)
-        
-        #import json
-        #print(json.dumps(_results["ids"], indent=2))
-        #print(json.dumps(_results["distances"], indent=2))
-        
+
+        # import json
+        # print(json.dumps(_results["ids"], indent=2))
+        # print(json.dumps(_results["distances"], indent=2))
+
         results = []
-        
+
         max_distance = 1.5
         if self.USE_INSTRUCTOR:
             max_distance = 1
@@ -594,24 +719,24 @@ class ChromaDBMemoryAgent(MemoryAgent):
 
         for i in range(len(_results["distances"][0])):
             distance = _results["distances"][0][i]
-            
+
             doc = _results["documents"][0][i]
             meta = _results["metadatas"][0][i]
             ts = meta.get("ts")
-            
+
             # skip pin_only entries
             if meta.get("pin_only", False):
                 continue
-            
+
             if distance < max_distance:
                 date_prefix = self.convert_ts_to_date_prefix(ts)
                 raw = doc
-                
+
                 if date_prefix:
                     doc = f"{date_prefix}: {doc}"
-                    
+
                 doc = MemoryDocument(doc, meta, _results["ids"][0][i], raw)
-                    
+
                 results.append(doc)
             else:
                 break
@@ -623,45 +748,55 @@ class ChromaDBMemoryAgent(MemoryAgent):
 
         return results
 
-
     def convert_ts_to_date_prefix(self, ts):
         if not ts:
             return None
         try:
             return util.iso8601_diff_to_human(ts, self.scene.ts)
         except Exception as e:
-            log.error("chromadb agent", error="failed to get date prefix", details=e, ts=ts, scene_ts=self.scene.ts)
+            log.error(
+                "chromadb agent",
+                error="failed to get date prefix",
+                details=e,
+                ts=ts,
+                scene_ts=self.scene.ts,
+            )
             return None
 
     def _get_document(self, id) -> dict:
         result = self.db.get(ids=[id] if isinstance(id, str) else id)
         documents = {}
-        
+
         for idx, doc in enumerate(result["documents"]):
-            date_prefix = self.convert_ts_to_date_prefix(result["metadatas"][idx].get("ts"))
+            date_prefix = self.convert_ts_to_date_prefix(
+                result["metadatas"][idx].get("ts")
+            )
             if date_prefix:
                 doc = f"{date_prefix}: {doc}"
-            documents[result["ids"][idx]] = MemoryDocument(doc, result["metadatas"][idx], result["ids"][idx], doc)
-            
+            documents[result["ids"][idx]] = MemoryDocument(
+                doc, result["metadatas"][idx], result["ids"][idx], doc
+            )
+
         return documents
 
     @set_processing
     async def remove_unsaved_memory(self):
         loop = asyncio.get_running_loop()
         await loop.run_in_executor(None, self._remove_unsaved_memory)
-        
+
     def _remove_unsaved_memory(self):
-    
         scene = self.scene
-        
+
         if not scene.memory_session_id:
             return
-            
+
         if scene.saved_memory_session_id == self.scene.memory_session_id:
             return
-        
-        log.info("chromadb agent", status="removing unsaved memory", session_id=scene.memory_session_id)
-        
+
+        log.info(
+            "chromadb agent",
+            status="removing unsaved memory",
+            session_id=scene.memory_session_id,
+        )
+
         self._delete({"session": scene.memory_session_id, "source": "talemate"})
-        
-    

src/talemate/agents/narrator.py

@@ -1,43 +1,48 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Callable, List, Optional, Union
 import dataclasses
-import structlog
 import random
-import talemate.util as util
-from talemate.emit import emit
-import talemate.emit.async_signals
-from talemate.prompts import Prompt
-from talemate.agents.base import set_processing as _set_processing, Agent, AgentAction, AgentActionConfig, AgentEmission
-from talemate.agents.world_state import TimePassageEmission
-from talemate.scene_message import NarratorMessage
-from talemate.events import GameLoopActorIterEvent
+from functools import wraps
+from typing import TYPE_CHECKING, Callable, List, Optional, Union
+
+import structlog
+
 import talemate.client as client
+import talemate.emit.async_signals
+import talemate.util as util
+from talemate.agents.base import Agent, AgentAction, AgentActionConfig, AgentEmission
+from talemate.agents.base import set_processing as _set_processing
+from talemate.agents.world_state import TimePassageEmission
+from talemate.emit import emit
+from talemate.events import GameLoopActorIterEvent
+from talemate.prompts import Prompt
+from talemate.scene_message import NarratorMessage
 
 from .registry import register
 
 if TYPE_CHECKING:
-    from talemate.tale_mate import Actor, Player, Character
+    from talemate.tale_mate import Actor, Character, Player
 
 log = structlog.get_logger("talemate.agents.narrator")
 
+
 @dataclasses.dataclass
 class NarratorAgentEmission(AgentEmission):
     generation: list[str] = dataclasses.field(default_factory=list)
-    
-talemate.emit.async_signals.register(
-    "agent.narrator.generated"
-)
+
+
+talemate.emit.async_signals.register("agent.narrator.generated")
+
 
 def set_processing(fn):
-    
     """
     Custom decorator that emits the agent status as processing while the function
     is running and then emits the result of the function as a NarratorAgentEmission
     """
-    
+
     @_set_processing
-    async def wrapper(self, *args, **kwargs):
+    @wraps(fn)
+    async def narration_wrapper(self, *args, **kwargs):
         response = await fn(self, *args, **kwargs)
         emission = NarratorAgentEmission(
             agent=self,
@@ -45,68 +50,68 @@ def set_processing(fn):
         )
         await talemate.emit.async_signals.get("agent.narrator.generated").send(emission)
         return emission.generation[0]
-    wrapper.__name__ = fn.__name__
-    return wrapper
+
+    return narration_wrapper
+
 
 @register()
 class NarratorAgent(Agent):
-    
     """
     Handles narration of the story
     """
-    
+
     agent_type = "narrator"
     verbose_name = "Narrator"
-    
+
     def __init__(
         self,
         client: client.TaleMateClient,
         **kwargs,
     ):
         self.client = client
-        
+
         # agent actions
-        
+
         self.actions = {
             "generation_override": AgentAction(
-                enabled = True,
-                label = "Generation Override",
-                description = "Override generation parameters",
-                config = {
+                enabled=True,
+                label="Generation Override",
+                description="Override generation parameters",
+                config={
                     "instructions": AgentActionConfig(
                         type="text",
                         label="Instructions",
                         value="Never wax poetic.",
                         description="Extra instructions to give to the AI for narrative generation.",
                     ),
-                }
+                },
             ),
             "auto_break_repetition": AgentAction(
-                enabled = True,
-                label = "Auto Break Repetition",
-                description = "Will attempt to automatically break AI repetition.",
+                enabled=True,
+                label="Auto Break Repetition",
+                description="Will attempt to automatically break AI repetition.",
             ),
             "narrate_time_passage": AgentAction(
-                enabled=True, 
-                label="Narrate Time Passage", 
+                enabled=True,
+                label="Narrate Time Passage",
                 description="Whenever you indicate passage of time, narrate right after",
-                config = {
+                config={
                     "ask_for_prompt": AgentActionConfig(
                         type="bool",
-                        label="Guide time narration via prompt", 
+                        label="Guide time narration via prompt",
                         description="Ask the user for a prompt to generate the time passage narration",
                         value=True,
                     )
-                }
+                },
             ),
             "narrate_dialogue": AgentAction(
-                enabled=False, 
-                label="Narrate after Dialogue", 
+                enabled=False,
+                label="Narrate after Dialogue",
                 description="Narrator will get a chance to narrate after every line of dialogue",
-                config = {
+                config={
                     "ai_dialog": AgentActionConfig(
                         type="number",
-                        label="AI Dialogue", 
+                        label="AI Dialogue",
                         description="Chance to narrate after every line of dialogue, 1 = always, 0 = never",
                         value=0.0,
                         min=0.0,
@@ -115,7 +120,7 @@ class NarratorAgent(Agent):
                     ),
                     "player_dialog": AgentActionConfig(
                         type="number",
-                        label="Player Dialogue", 
+                        label="Player Dialogue",
                         description="Chance to narrate after every line of dialogue, 1 = always, 0 = never",
                         value=0.1,
                         min=0.0,
@@ -124,34 +129,32 @@ class NarratorAgent(Agent):
                     ),
                     "generate_dialogue": AgentActionConfig(
                         type="bool",
-                        label="Allow Dialogue in Narration", 
+                        label="Allow Dialogue in Narration",
                         description="Allow the narrator to generate dialogue in narration",
                         value=False,
                     ),
-                }
+                },
             ),
         }
-        
+
     @property
     def extra_instructions(self):
         if self.actions["generation_override"].enabled:
             return self.actions["generation_override"].config["instructions"].value
         return ""
-        
+
     def clean_result(self, result):
-        
         """
         Cleans the result of a narration
         """
-        
+
         result = result.strip().strip(":").strip()
-        
+
         if "#" in result:
             result = result.split("#")[0]
-            
+
         character_names = [c.name for c in self.scene.get_characters()]
-    
-        
+
         cleaned = []
         for line in result.split("\n"):
             for character_name in character_names:
@@ -160,71 +163,83 @@ class NarratorAgent(Agent):
             cleaned.append(line)
 
         result = "\n".join(cleaned)
-        #result = util.strip_partial_sentences(result)
+        # result = util.strip_partial_sentences(result)
         return result
 
     def connect(self, scene):
-        
         """
         Connect to signals
         """
-        
+
         super().connect(scene)
-        talemate.emit.async_signals.get("agent.world_state.time").connect(self.on_time_passage)
+        talemate.emit.async_signals.get("agent.world_state.time").connect(
+            self.on_time_passage
+        )
         talemate.emit.async_signals.get("game_loop_actor_iter").connect(self.on_dialog)
 
-    async def on_time_passage(self, event:TimePassageEmission):
-        
+    async def on_time_passage(self, event: TimePassageEmission):
         """
         Handles time passage narration, if enabled
         """
-        
+
         if not self.actions["narrate_time_passage"].enabled:
             return
-        
-        response = await self.narrate_time_passage(event.duration, event.human_duration, event.narrative)
-        narrator_message = NarratorMessage(response, source=f"narrate_time_passage:{event.duration};{event.narrative}")
+
+        response = await self.narrate_time_passage(
+            event.duration, event.human_duration, event.narrative
+        )
+        narrator_message = NarratorMessage(
+            response, source=f"narrate_time_passage:{event.duration};{event.narrative}"
+        )
         emit("narrator", narrator_message)
         self.scene.push_history(narrator_message)
-        
-    async def on_dialog(self, event:GameLoopActorIterEvent):
-        
+
+    async def on_dialog(self, event: GameLoopActorIterEvent):
         """
         Handles dialogue narration, if enabled
         """
-        
+
         if not self.actions["narrate_dialogue"].enabled:
             return
-        
-        
+
         if event.game_loop.had_passive_narration:
-            log.debug("narrate on dialog", skip=True, had_passive_narration=event.game_loop.had_passive_narration)
+            log.debug(
+                "narrate on dialog",
+                skip=True,
+                had_passive_narration=event.game_loop.had_passive_narration,
+            )
             return
-        
-        narrate_on_ai_chance = self.actions["narrate_dialogue"].config["ai_dialog"].value
-        narrate_on_player_chance = self.actions["narrate_dialogue"].config["player_dialog"].value
+
+        narrate_on_ai_chance = (
+            self.actions["narrate_dialogue"].config["ai_dialog"].value
+        )
+        narrate_on_player_chance = (
+            self.actions["narrate_dialogue"].config["player_dialog"].value
+        )
         narrate_on_ai = random.random() < narrate_on_ai_chance
         narrate_on_player = random.random() < narrate_on_player_chance
 
         log.debug(
-            "narrate on dialog", 
-            narrate_on_ai=narrate_on_ai, 
-            narrate_on_ai_chance=narrate_on_ai_chance, 
+            "narrate on dialog",
+            narrate_on_ai=narrate_on_ai,
+            narrate_on_ai_chance=narrate_on_ai_chance,
             narrate_on_player=narrate_on_player,
             narrate_on_player_chance=narrate_on_player_chance,
         )
-        
+
         if event.actor.character.is_player and not narrate_on_player:
             return
-        
+
         if not event.actor.character.is_player and not narrate_on_ai:
             return
-        
+
         response = await self.narrate_after_dialogue(event.actor.character)
-        narrator_message = NarratorMessage(response, source=f"narrate_dialogue:{event.actor.character.name}")
+        narrator_message = NarratorMessage(
+            response, source=f"narrate_dialogue:{event.actor.character.name}"
+        )
         emit("narrator", narrator_message)
         self.scene.push_history(narrator_message)
-        
+
         event.game_loop.had_passive_narration = True
 
     @set_processing
@@ -237,22 +252,22 @@ class NarratorAgent(Agent):
             "narrator.narrate-scene",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
-        
+
         response = response.strip("*")
         response = util.strip_partial_sentences(response)
-        
+
         response = f"*{response.strip('*')}*"
 
         return response
 
     @set_processing
-    async def progress_story(self, narrative_direction:str=None):
+    async def progress_story(self, narrative_direction: str = None):
         """
         Narrate the scene
         """
@@ -260,18 +275,20 @@ class NarratorAgent(Agent):
         scene = self.scene
         pc = scene.get_player_character()
         npcs = list(scene.get_npc_characters())
-        npc_names=  ", ".join([npc.name for npc in npcs])
-        
+        npc_names = ", ".join([npc.name for npc in npcs])
+
         if narrative_direction is None:
             narrative_direction = "Slightly move the current scene forward."
-        
-        self.scene.log.info("narrative_direction", narrative_direction=narrative_direction)
+
+        self.scene.log.info(
+            "narrative_direction", narrative_direction=narrative_direction
+        )
 
         response = await Prompt.request(
             "narrator.narrate-progress",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "narrative_direction": narrative_direction,
@@ -279,7 +296,7 @@ class NarratorAgent(Agent):
                 "npcs": npcs,
                 "npc_names": npc_names,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
 
         self.scene.log.info("progress_story", response=response)
@@ -291,11 +308,13 @@ class NarratorAgent(Agent):
         if response.count("*") % 2 != 0:
             response = response.replace("*", "")
             response = f"*{response}*"
-        
+
         return response
 
     @set_processing
-    async def narrate_query(self, query:str, at_the_end:bool=False, as_narrative:bool=True):
+    async def narrate_query(
+        self, query: str, at_the_end: bool = False, as_narrative: bool = True
+    ):
         """
         Narrate a specific query
         """
@@ -303,21 +322,21 @@ class NarratorAgent(Agent):
             "narrator.narrate-query",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "query": query,
                 "at_the_end": at_the_end,
                 "as_narrative": as_narrative,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
         log.info("narrate_query", response=response)
         response = self.clean_result(response.strip())
         log.info("narrate_query (after clean)", response=response)
         if as_narrative:
             response = f"*{response}*"
-        
+
         return response
 
     @set_processing
@@ -330,12 +349,12 @@ class NarratorAgent(Agent):
             "narrator.narrate-character",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "character": character,
                 "max_tokens": self.client.max_token_length,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
 
         response = self.clean_result(response.strip())
@@ -345,54 +364,55 @@ class NarratorAgent(Agent):
 
     @set_processing
     async def augment_context(self):
-        
         """
         Takes a context history generated via scene.context_history() and augments it with additional information
         by asking and answering questions with help from the long term memory.
         """
         memory = self.scene.get_helper("memory").agent
-        
+
         questions = await Prompt.request(
             "narrator.context-questions",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
-        
+
         self.scene.log.info("context_questions", questions=questions)
-        
+
         questions = [q for q in questions.split("\n") if q.strip()]
-        
+
         memory_context = await memory.multi_query(
             questions, iterate=2, max_tokens=self.client.max_token_length - 1000
         )
-        
+
         answers = await Prompt.request(
             "narrator.context-answers",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "memory": memory_context,
                 "questions": questions,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
-        
+
         self.scene.log.info("context_answers", answers=answers)
-        
+
         answers = [a for a in answers.split("\n") if a.strip()]
-        
+
         # return questions and answers
         return list(zip(questions, answers))
-    
+
     @set_processing
-    async def narrate_time_passage(self, duration:str, time_passed:str, narrative:str):
+    async def narrate_time_passage(
+        self, duration: str, time_passed: str, narrative: str
+    ):
         """
         Narrate a specific character
         """
@@ -401,26 +421,25 @@ class NarratorAgent(Agent):
             "narrator.narrate-time-passage",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "duration": duration,
                 "time_passed": time_passed,
                 "narrative": narrative,
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
-        
+
         log.info("narrate_time_passage", response=response)
 
         response = self.clean_result(response.strip())
         response = f"*{response}*"
 
         return response
-    
-    
+
     @set_processing
-    async def narrate_after_dialogue(self, character:Character):
+    async def narrate_after_dialogue(self, character: Character):
         """
         Narrate after a line of dialogue
         """
@@ -429,22 +448,24 @@ class NarratorAgent(Agent):
             "narrator.narrate-after-dialogue",
             self.client,
             "narrate",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "character": character,
                 "last_line": str(self.scene.history[-1]),
                 "extra_instructions": self.extra_instructions,
-            }
+            },
         )
-        
+
         log.info("narrate_after_dialogue", response=response)
 
         response = self.clean_result(response.strip().strip("*"))
         response = f"*{response}*"
-        
-        allow_dialogue = self.actions["narrate_dialogue"].config["generate_dialogue"].value
-        
+
+        allow_dialogue = (
+            self.actions["narrate_dialogue"].config["generate_dialogue"].value
+        )
+
         if not allow_dialogue:
             response = response.split('"')[0].strip()
             response = response.replace("*", "")
@@ -452,18 +473,171 @@ class NarratorAgent(Agent):
             response = f"*{response}*"
 
         return response
-    
+
+    @set_processing
+    async def narrate_character_entry(
+        self, character: Character, direction: str = None
+    ):
+        """
+        Narrate a character entering the scene
+        """
+
+        response = await Prompt.request(
+            "narrator.narrate-character-entry",
+            self.client,
+            "narrate",
+            vars={
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "character": character,
+                "direction": direction,
+                "extra_instructions": self.extra_instructions,
+            },
+        )
+
+        response = self.clean_result(response.strip().strip("*"))
+        response = f"*{response}*"
+
+        return response
+
+    @set_processing
+    async def narrate_character_exit(self, character: Character, direction: str = None):
+        """
+        Narrate a character exiting the scene
+        """
+
+        response = await Prompt.request(
+            "narrator.narrate-character-exit",
+            self.client,
+            "narrate",
+            vars={
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "character": character,
+                "direction": direction,
+                "extra_instructions": self.extra_instructions,
+            },
+        )
+
+        response = self.clean_result(response.strip().strip("*"))
+        response = f"*{response}*"
+
+        return response
+
+    @set_processing
+    async def paraphrase(self, narration: str):
+        """
+        Paraphrase a narration
+        """
+
+        response = await Prompt.request(
+            "narrator.paraphrase",
+            self.client,
+            "narrate",
+            vars={
+                "text": narration,
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+            },
+        )
+
+        log.info("paraphrase", narration=narration, response=response)
+
+        response = self.clean_result(response.strip().strip("*"))
+        response = f"*{response}*"
+
+        return response
+
+    async def passthrough(self, narration: str) -> str:
+        """
+        Pass through narration message as is
+        """
+        narration = narration.replace("*", "")
+        narration = f"*{narration}*"
+        narration = util.ensure_dialog_format(narration)
+        return narration
+
+    def action_to_source(
+        self,
+        action_name: str,
+        parameters: dict,
+    ) -> str:
+        """
+        Generate a source string for a given action and parameters
+
+        The source string is used to identify the source of a NarratorMessage
+        and will also help regenerate the action and parameters from the source string
+        later on
+        """
+
+        args = []
+
+        if action_name == "paraphrase":
+            args.append(parameters.get("narration"))
+        elif action_name == "narrate_character_entry":
+            args.append(parameters.get("character").name)
+            # args.append(parameters.get("direction"))
+        elif action_name == "narrate_character_exit":
+            args.append(parameters.get("character").name)
+            # args.append(parameters.get("direction"))
+        elif action_name == "narrate_character":
+            args.append(parameters.get("character").name)
+        elif action_name == "narrate_query":
+            args.append(parameters.get("query"))
+        elif action_name == "narrate_time_passage":
+            args.append(parameters.get("duration"))
+            args.append(parameters.get("time_passed"))
+            args.append(parameters.get("narrative"))
+        elif action_name == "progress_story":
+            args.append(parameters.get("narrative_direction"))
+        elif action_name == "narrate_after_dialogue":
+            args.append(parameters.get("character"))
+
+        arg_str = ";".join(args) if args else ""
+
+        return f"{action_name}:{arg_str}".rstrip(":")
+
+    async def action_to_narration(
+        self,
+        action_name: str,
+        emit_message: bool = False,
+        **kwargs,
+    ):
+        # calls self[action_name] and returns the result as a NarratorMessage
+        # that is pushed to the history
+
+        fn = getattr(self, action_name)
+        narration = await fn(**kwargs)
+        source = self.action_to_source(action_name, kwargs)
+
+        narrator_message = NarratorMessage(narration, source=source)
+        self.scene.push_history(narrator_message)
+
+        if emit_message:
+            emit("narrator", narrator_message)
+
+        return narrator_message
+
     # LLM client related methods. These are called during or after the client
-    
-    def inject_prompt_paramters(self, prompt_param: dict, kind: str, agent_function_name: str):
-        log.debug("inject_prompt_paramters", prompt_param=prompt_param, kind=kind, agent_function_name=agent_function_name)
+
+    def inject_prompt_paramters(
+        self, prompt_param: dict, kind: str, agent_function_name: str
+    ):
+        log.debug(
+            "inject_prompt_paramters",
+            prompt_param=prompt_param,
+            kind=kind,
+            agent_function_name=agent_function_name,
+        )
         character_names = [f"\n{c.name}:" for c in self.scene.get_characters()]
         if prompt_param.get("extra_stopping_strings") is None:
             prompt_param["extra_stopping_strings"] = []
         prompt_param["extra_stopping_strings"] += character_names
-        
-    def allow_repetition_break(self, kind: str, agent_function_name: str, auto:bool=False):
+
+    def allow_repetition_break(
+        self, kind: str, agent_function_name: str, auto: bool = False
+    ):
         if auto and not self.actions["auto_break_repetition"].enabled:
             return False
-        
-        return True
+
+        return True

src/talemate/agents/summarize.py

@@ -1,26 +1,26 @@
 from __future__ import annotations
 
 import asyncio
+import re
+import time
 import traceback
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
+import structlog
+
 import talemate.data_objects as data_objects
 import talemate.emit.async_signals
 import talemate.util as util
+from talemate.events import GameLoopEvent
 from talemate.prompts import Prompt
 from talemate.scene_message import DirectorMessage, TimePassageMessage
-from talemate.events import GameLoopEvent
 
-from .base import Agent, set_processing, AgentAction, AgentActionConfig
+from .base import Agent, AgentAction, AgentActionConfig, set_processing
 from .registry import register
 
-import structlog
-
-import time
-import re
-
 log = structlog.get_logger("talemate.agents.summarize")
 
+
 @register()
 class SummarizeAgent(Agent):
     """
@@ -36,7 +36,7 @@ class SummarizeAgent(Agent):
 
     def __init__(self, client, **kwargs):
         self.client = client
-        
+
         self.actions = {
             "archive": AgentAction(
                 enabled=True,
@@ -63,24 +63,31 @@ class SummarizeAgent(Agent):
                             {"label": "Lengthy & Detailed", "value": "long"},
                         ],
                     ),
-                }
+                    "include_previous": AgentActionConfig(
+                        type="number",
+                        label="Use preceeding summaries to strengthen context",
+                        description="Number of entries",
+                        note="Help the AI summarize by including the last few summaries as additional context. Some models may incorporate this context into the new summary directly, so if you find yourself with a bunch of similar history entries, try setting this to 0.",
+                        value=3,
+                        min=0,
+                        max=10,
+                        step=1,
+                    ),
+                },
             )
         }
-        
-    
+
     def connect(self, scene):
         super().connect(scene)
         talemate.emit.async_signals.get("game_loop").connect(self.on_game_loop)
-        
-        
-    async def on_game_loop(self, emission:GameLoopEvent):
+
+    async def on_game_loop(self, emission: GameLoopEvent):
         """
         Called when a conversation is generated
         """
 
         await self.build_archive(self.scene)
-        
-        
+
     def clean_result(self, result):
         if "#" in result:
             result = result.split("#")[0]
@@ -94,10 +101,10 @@ class SummarizeAgent(Agent):
     @set_processing
     async def build_archive(self, scene):
         end = None
-        
+
         if not self.actions["archive"].enabled:
             return
-        
+
         if not scene.archived_history:
             start = 0
             recent_entry = None
@@ -108,42 +115,59 @@ class SummarizeAgent(Agent):
                 # meaning we are still at the beginning of the scene
                 start = 0
             else:
-                start = recent_entry.get("end", 0)+1
+                start = recent_entry.get("end", 0) + 1
+
+        # if there is a recent entry we also collect the 3 most recentries
+        # as extra context
+
+        num_previous = self.actions["archive"].config["include_previous"].value
+        if recent_entry and num_previous > 0:
+            extra_context = "\n\n".join(
+                [entry["text"] for entry in scene.archived_history[-num_previous:]]
+            )
+        else:
+            extra_context = None
 
         tokens = 0
         dialogue_entries = []
         ts = "PT0S"
         time_passage_termination = False
-        
+
         token_threshold = self.actions["archive"].config["threshold"].value
-        
+
         log.debug("build_archive", start=start, recent_entry=recent_entry)
-        
+
         if recent_entry:
             ts = recent_entry.get("ts", ts)
-        
+
         for i in range(start, len(scene.history)):
             dialogue = scene.history[i]
-            
-            #log.debug("build_archive", idx=i, content=str(dialogue)[:64]+"...")
-            
+
+            # log.debug("build_archive", idx=i, content=str(dialogue)[:64]+"...")
+
             if isinstance(dialogue, DirectorMessage):
                 if i == start:
                     start += 1
                 continue
-            
+
             if isinstance(dialogue, TimePassageMessage):
                 log.debug("build_archive", time_passage_message=dialogue)
                 if i == start:
                     ts = util.iso8601_add(ts, dialogue.ts)
-                    log.debug("build_archive", time_passage_message=dialogue, start=start, i=i, ts=ts)
+                    log.debug(
+                        "build_archive",
+                        time_passage_message=dialogue,
+                        start=start,
+                        i=i,
+                        ts=ts,
+                    )
                     start += 1
                     continue
                 log.debug("build_archive", time_passage_message_termination=dialogue)
                 time_passage_termination = True
                 end = i - 1
                 break
-            
+
             tokens += util.count_tokens(dialogue)
             dialogue_entries.append(dialogue)
             if tokens > token_threshold:  #
@@ -153,43 +177,44 @@ class SummarizeAgent(Agent):
         if end is None:
             # nothing to archive yet
             return
-        
-        log.debug("build_archive", start=start, end=end, ts=ts, time_passage_termination=time_passage_termination)
 
-        extra_context = None
-        if recent_entry:
-            extra_context = recent_entry["text"]
-            
+        log.debug(
+            "build_archive",
+            start=start,
+            end=end,
+            ts=ts,
+            time_passage_termination=time_passage_termination,
+        )
+
         # in order to summarize coherently, we need to determine if there is a favorable
         # cutoff point (e.g., the scene naturally ends or shifts meaninfully in the middle
         # of the  dialogue)
         #
         # One way to do this is to check if the last line is a TimePassageMessage, which
-        # indicates a scene change or a significant pause. 
-        # 
+        # indicates a scene change or a significant pause.
+        #
         # If not, we can ask the AI to find a good point of
         # termination.
-        
+
         if not time_passage_termination:
-            
             # No TimePassageMessage, so we need to ask the AI to find a good point of termination
-            
+
             terminating_line = await self.analyze_dialoge(dialogue_entries)
 
             if terminating_line:
                 adjusted_dialogue = []
-                for line in dialogue_entries:                    
+                for line in dialogue_entries:
                     if str(line) in terminating_line:
                         break
                     adjusted_dialogue.append(line)
                 dialogue_entries = adjusted_dialogue
-                end = start + len(dialogue_entries)-1
-            
+                end = start + len(dialogue_entries) - 1
+
         if dialogue_entries:
             summarized = await self.summarize(
                 "\n".join(map(str, dialogue_entries)), extra_context=extra_context
             )
-        
+
         else:
             # AI has likely identified the first line as a scene change, so we can't summarize
             # just use the first line
@@ -203,33 +228,178 @@ class SummarizeAgent(Agent):
 
     @set_processing
     async def analyze_dialoge(self, dialogue):
-        response = await Prompt.request("summarizer.analyze-dialogue", self.client, "analyze_freeform", vars={
-            "dialogue": "\n".join(map(str, dialogue)),
-            "scene": self.scene,
-            "max_tokens": self.client.max_token_length,
-        })
-        
+        response = await Prompt.request(
+            "summarizer.analyze-dialogue",
+            self.client,
+            "analyze_freeform",
+            vars={
+                "dialogue": "\n".join(map(str, dialogue)),
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+            },
+        )
+
         response = self.clean_result(response)
         return response
-        
+
     @set_processing
     async def summarize(
         self,
         text: str,
         extra_context: str = None,
         method: str = None,
+        extra_instructions: str = None,
     ):
         """
         Summarize the given text
         """
 
-        response = await Prompt.request("summarizer.summarize-dialogue", self.client, "summarize", vars={
-            "dialogue": text,
-            "scene": self.scene,
-            "max_tokens": self.client.max_token_length,
-            "summarization_method": self.actions["archive"].config["method"].value if method is None else method,
-        })
-        
-        self.scene.log.info("summarize", dialogue_length=len(text), summarized_length=len(response))
+        response = await Prompt.request(
+            "summarizer.summarize-dialogue",
+            self.client,
+            "summarize",
+            vars={
+                "dialogue": text,
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "summarization_method": (
+                    self.actions["archive"].config["method"].value
+                    if method is None
+                    else method
+                ),
+                "extra_context": extra_context or "",
+                "extra_instructions": extra_instructions or "",
+            },
+        )
 
-        return self.clean_result(response)
+        self.scene.log.info(
+            "summarize", dialogue_length=len(text), summarized_length=len(response)
+        )
+
+        return self.clean_result(response)
+
+    async def build_stepped_archive_for_level(self, level: int):
+        """
+        WIP - not yet used
+
+        This will iterate over existing archived_history entries
+        and stepped_archived_history entries and summarize based on time duration
+        indicated between the entries.
+
+        The lowest level of summarization (based on token threshold and any time passage)
+        happens in build_archive. This method is for summarizing furhter levels based on
+        long time pasages.
+
+        Level 0: small timestap summarize (summarizes all token summarizations when time advances +1 day)
+        Level 1: medium timestap summarize (summarizes all small timestep summarizations when time advances +1 week)
+        Level 2: large timestap summarize (summarizes all medium timestep summarizations when time advances +1 month)
+        Level 3: huge timestap summarize (summarizes all large timestep summarizations when time advances +1 year)
+        Level 4: massive timestap summarize (summarizes all huge timestep summarizations when time advances +10 years)
+        Level 5: epic timestap summarize (summarizes all massive timestep summarizations when time advances +100 years)
+        and so on (increasing by a factor of 10 each time)
+
+        ```
+        @dataclass
+        class ArchiveEntry:
+            text: str
+            start: int = None
+            end: int = None
+            ts: str = None
+        ```
+
+        Like token summarization this will use ArchiveEntry and start and end will refer to the entries in the
+        lower level of summarization.
+
+        Ts is the iso8601 timestamp of the start of the summarized period.
+        """
+
+        # select the list to use for the entries
+
+        if level == 0:
+            entries = self.scene.archived_history
+        else:
+            entries = self.scene.stepped_archived_history[level - 1]
+
+        # select the list to summarize new entries to
+
+        target = self.scene.stepped_archived_history[level]
+
+        if not target:
+            raise ValueError(f"Invalid level {level}")
+
+        # determine the start and end of the period to summarize
+
+        if not entries:
+            return
+
+        # determine the time threshold for this level
+
+        # first calculate all possible thresholds in iso8601 format, starting with 1 day
+        thresholds = [
+            "P1D",
+            "P1W",
+            "P1M",
+            "P1Y",
+        ]
+
+        # TODO: auto extend?
+
+        time_threshold_in_seconds = util.iso8601_to_seconds(thresholds[level])
+
+        if not time_threshold_in_seconds:
+            raise ValueError(f"Invalid level {level}")
+
+        # determine the most recent summarized entry time, and then find entries
+        # that are newer than that in the lower list
+
+        ts = target[-1].ts if target else entries[0].ts
+
+        # determine the most recent entry at the lower level, if its not newer or
+        # the difference is less than the threshold, then we don't need to summarize
+
+        recent_entry = entries[-1]
+
+        if util.iso8601_diff(recent_entry.ts, ts) < time_threshold_in_seconds:
+            return
+
+        log.debug("build_stepped_archive", level=level, ts=ts)
+
+        # if target is empty, start is 0
+        # otherwise start is the end of the last entry
+
+        start = 0 if not target else target[-1].end
+
+        # collect entries starting at start until the combined time duration
+        # exceeds the threshold
+
+        entries_to_summarize = []
+
+        for entry in entries[start:]:
+            entries_to_summarize.append(entry)
+            if util.iso8601_diff(entry.ts, ts) > time_threshold_in_seconds:
+                break
+
+        # summarize the entries
+        # we also collect N entries of previous summaries to use as context
+
+        num_previous = self.actions["archive"].config["include_previous"].value
+        if num_previous > 0:
+            extra_context = "\n\n".join(
+                [entry["text"] for entry in target[-num_previous:]]
+            )
+        else:
+            extra_context = None
+
+        summarized = await self.summarize(
+            "\n".join(map(str, entries_to_summarize)), extra_context=extra_context
+        )
+
+        # push summarized entry to target
+
+        ts = entries_to_summarize[-1].ts
+
+        target.append(
+            data_objects.ArchiveEntry(
+                summarized, start, len(entries_to_summarize) - 1, ts=ts
+            )
+        )

src/talemate/agents/tts.py

@@ -1,17 +1,21 @@
 from __future__ import annotations
 
-from typing import Union
 import asyncio
-import httpx
+import base64
+import functools
 import io
 import os
-import pydantic
-import nltk
 import tempfile
-import base64
+import time
 import uuid
-import functools
+from typing import Union
+
+import httpx
+import nltk
+import pydantic
+import structlog
 from nltk.tokenize import sent_tokenize
+from openai import AsyncOpenAI
 
 import talemate.config as config
 import talemate.emit.async_signals
@@ -21,91 +25,91 @@ from talemate.emit.signals import handlers
 from talemate.events import GameLoopNewMessageEvent
 from talemate.scene_message import CharacterMessage, NarratorMessage
 
-from .base import Agent, set_processing, AgentAction, AgentActionConfig
+from .base import (
+    Agent,
+    AgentAction,
+    AgentActionConditional,
+    AgentActionConfig,
+    AgentDetail,
+    set_processing,
+)
 from .registry import register
 
-import structlog
-
-import time
-
 try:
     from TTS.api import TTS
 except ImportError:
     TTS = None
 
-log = structlog.get_logger("talemate.agents.tts")#
+log = structlog.get_logger("talemate.agents.tts")  #
 
 if not TTS:
     # TTS installation is massive and requires a lot of dependencies
     # so we don't want to require it unless the user wants to use it
-    log.info("TTS (local) requires the TTS package, please install with `pip install TTS` if you want to use the local api")
+    log.info(
+        "TTS (local) requires the TTS package, please install with `pip install TTS` if you want to use the local api"
+    )
 
 
 def parse_chunks(text):
-    
     text = text.replace("...", "__ellipsis__")
-    
+
     chunks = sent_tokenize(text)
     cleaned_chunks = []
-    
+
     for chunk in chunks:
-        chunk = chunk.replace("*","")
+        chunk = chunk.replace("*", "")
         if not chunk:
             continue
         cleaned_chunks.append(chunk)
-    
-    
+
     for i, chunk in enumerate(cleaned_chunks):
         chunk = chunk.replace("__ellipsis__", "...")
 
         cleaned_chunks[i] = chunk
-    
+
     return cleaned_chunks
 
-def clean_quotes(chunk:str):
-    
+
+def clean_quotes(chunk: str):
     # if there is an uneven number of quotes, remove the last one if its
     # at the end of the chunk. If its in the middle, add a quote to the end
     if chunk.count('"') % 2 == 1:
-        
         if chunk.endswith('"'):
             chunk = chunk[:-1]
         else:
             chunk += '"'
-    
-    return chunk
-        
 
-def rejoin_chunks(chunks:list[str], chunk_size:int=250):
-    
+    return chunk
+
+
+def rejoin_chunks(chunks: list[str], chunk_size: int = 250):
     """
     Will combine chunks split by punctuation into a single chunk until
     max chunk size is reached
     """
-    
+
     joined_chunks = []
-    
+
     current_chunk = ""
-    
+
     for chunk in chunks:
-            
         if len(current_chunk) + len(chunk) > chunk_size:
             joined_chunks.append(clean_quotes(current_chunk))
             current_chunk = ""
-        
+
         current_chunk += chunk
-        
+
     if current_chunk:
         joined_chunks.append(clean_quotes(current_chunk))
     return joined_chunks
 
 
 class Voice(pydantic.BaseModel):
-    value:str
-    label:str
+    value: str
+    label: str
+
 
 class VoiceLibrary(pydantic.BaseModel):
-    
     api: str
     voices: list[Voice] = pydantic.Field(default_factory=list)
     last_synced: float = None
@@ -113,51 +117,50 @@ class VoiceLibrary(pydantic.BaseModel):
 
 @register()
 class TTSAgent(Agent):
-    
     """
     Text to speech agent
     """
-    
+
     agent_type = "tts"
     verbose_name = "Voice"
     requires_llm_client = False
-    
+    essential = False
+
     @classmethod
     def config_options(cls, agent=None):
         config_options = super().config_options(agent=agent)
-        
+
         if agent:
             config_options["actions"]["_config"]["config"]["voice_id"]["choices"] = [
                 voice.model_dump() for voice in agent.list_voices_sync()
             ]
-        
+
         return config_options
 
     def __init__(self, **kwargs):
-        
         self.is_enabled = False
         nltk.download("punkt", quiet=True)
-        
+
         self.voices = {
             "elevenlabs": VoiceLibrary(api="elevenlabs"),
-            "coqui": VoiceLibrary(api="coqui"),
             "tts": VoiceLibrary(api="tts"),
+            "openai": VoiceLibrary(api="openai"),
         }
         self.config = config.load_config()
         self.playback_done_event = asyncio.Event()
+        self.preselect_voice = None
         self.actions = {
             "_config": AgentAction(
-                enabled=True, 
-                label="Configure", 
+                enabled=True,
+                label="Configure",
                 description="TTS agent configuration",
                 config={
                     "api": AgentActionConfig(
                         type="text",
                         choices=[
-                            # TODO at local TTS support
                             {"value": "tts", "label": "TTS (Local)"},
                             {"value": "elevenlabs", "label": "Eleven Labs"},
-                            {"value": "coqui", "label": "Coqui Studio"},
+                            {"value": "openai", "label": "OpenAI"},
                         ],
                         value="tts",
                         label="API",
@@ -169,7 +172,7 @@ class TTSAgent(Agent):
                         value="default",
                         label="Narrator Voice",
                         description="Voice ID/Name to use for TTS",
-                        choices=[]
+                        choices=[],
                     ),
                     "generate_for_player": AgentActionConfig(
                         type="bool",
@@ -194,90 +197,125 @@ class TTSAgent(Agent):
                         value=False,
                         label="Split generation",
                         description="Generate audio chunks for each sentence - will be much more responsive but may loose context to inform inflection",
-                    )
-                }  
+                    ),
+                },
+            ),
+            "openai": AgentAction(
+                enabled=True,
+                condition=AgentActionConditional(
+                    attribute="_config.config.api", value="openai"
+                ),
+                label="OpenAI Settings",
+                config={
+                    "model": AgentActionConfig(
+                        type="text",
+                        value="tts-1",
+                        choices=[
+                            {"value": "tts-1", "label": "TTS 1"},
+                            {"value": "tts-1-hd", "label": "TTS 1 HD"},
+                        ],
+                        label="Model",
+                        description="TTS model to use",
+                    ),
+                },
             ),
         }
-        
+
         self.actions["_config"].model_dump()
         handlers["config_saved"].connect(self.on_config_saved)
 
-
     @property
     def enabled(self):
         return self.is_enabled
-    
+
     @property
     def has_toggle(self):
         return True
-        
+
     @property
     def experimental(self):
         return False
-    
+
     @property
     def not_ready_reason(self) -> str:
         """
         Returns a string explaining why the agent is not ready
         """
-        
+
         if self.ready:
             return ""
-        
+
         if self.api == "tts":
             if not TTS:
                 return "TTS not installed"
-        
+
         elif self.requires_token and not self.token:
             return "No API token"
-        
+
         elif not self.default_voice_id:
             return "No voice selected"
-            
+
     @property
     def agent_details(self):
-        suffix = ""
-        
-        if not self.ready:
-            suffix = f"  - {self.not_ready_reason}"
-        else:
-            suffix = f"  - {self.voice_id_to_label(self.default_voice_id)}"
-        
-        api = self.api
-        choices = self.actions["_config"].config["api"].choices
-        api_label = api
-        for choice in choices:
-            if choice["value"] == api:
-                api_label = choice["label"]
-                break
-        
-        return f"{api_label}{suffix}"
+
+        details = {
+            "api": AgentDetail(
+                icon="mdi-server-outline",
+                value=self.api_label,
+                description="The backend to use for TTS",
+            ).model_dump(),
+        }
+
+        if self.ready and self.enabled:
+            details["voice"] = AgentDetail(
+                icon="mdi-account-voice",
+                value=self.voice_id_to_label(self.default_voice_id) or "",
+                description="The voice to use for TTS",
+                color="info",
+            ).model_dump()
+        elif self.enabled:
+            details["error"] = AgentDetail(
+                icon="mdi-alert",
+                value=self.not_ready_reason,
+                description=self.not_ready_reason,
+                color="error",
+            ).model_dump()
+
+        return details
 
     @property
     def api(self):
         return self.actions["_config"].config["api"].value
-    
+
+    @property
+    def api_label(self):
+        choices = self.actions["_config"].config["api"].choices
+        api = self.api
+        for choice in choices:
+            if choice["value"] == api:
+                return choice["label"]
+        return api
+
     @property
     def token(self):
         api = self.api
-        return self.config.get(api,{}).get("api_key")
-    
+        return self.config.get(api, {}).get("api_key")
+
     @property
     def default_voice_id(self):
         return self.actions["_config"].config["voice_id"].value
-    
+
     @property
     def requires_token(self):
         return self.api != "tts"
-    
+
     @property
     def ready(self):
-        
         if self.api == "tts":
             if not TTS:
                 return False
             return True
-        
+
         return (not self.requires_token or self.token) and self.default_voice_id
 
     @property
@@ -285,6 +323,8 @@ class TTSAgent(Agent):
         if not self.enabled:
             return "disabled"
         if self.ready:
+            if getattr(self, "processing_bg", 0) > 0:
+                return "busy_bg" if not getattr(self, "processing", False) else "busy"
             return "active" if not getattr(self, "processing", False) else "busy"
         if self.requires_token and not self.token:
             return "error"
@@ -299,106 +339,139 @@ class TTSAgent(Agent):
             return 1024
         elif self.api == "coqui":
             return 250
-            
+
         return 250
 
-    def apply_config(self, *args, **kwargs):
-        
+    @property
+    def openai_api_key(self):
+        return self.config.get("openai", {}).get("api_key")
+
+    async def apply_config(self, *args, **kwargs):
         try:
             api = kwargs["actions"]["_config"]["config"]["api"]["value"]
         except KeyError:
             api = self.api
-        
-        api_changed = api != self.api
-        
-        log.debug("apply_config", api=api, api_changed=api != self.api, current_api=self.api)
 
-        super().apply_config(*args, **kwargs)
-        
-        
+        api_changed = api != self.api
+
+        log.debug(
+            "apply_config",
+            api=api,
+            api_changed=api != self.api,
+            current_api=self.api,
+            args=args,
+            kwargs=kwargs,
+        )
+
+        try:
+            self.preselect_voice = kwargs["actions"]["_config"]["config"]["voice_id"][
+                "value"
+            ]
+        except KeyError:
+            self.preselect_voice = self.default_voice_id
+
+        await super().apply_config(*args, **kwargs)
+
         if api_changed:
             try:
-                self.actions["_config"].config["voice_id"].value = self.voices[api].voices[0].value
+                self.actions["_config"].config["voice_id"].value = (
+                    self.voices[api].voices[0].value
+                )
             except IndexError:
                 self.actions["_config"].config["voice_id"].value = ""
 
-        
     def connect(self, scene):
         super().connect(scene)
-        talemate.emit.async_signals.get("game_loop_new_message").connect(self.on_game_loop_new_message)
-        
+        talemate.emit.async_signals.get("game_loop_new_message").connect(
+            self.on_game_loop_new_message
+        )
+
     def on_config_saved(self, event):
         config = event.data
         self.config = config
         instance.emit_agent_status(self.__class__, self)
-        
-    async def on_game_loop_new_message(self, emission:GameLoopNewMessageEvent):
+
+    async def on_game_loop_new_message(self, emission: GameLoopNewMessageEvent):
         """
         Called when a conversation is generated
         """
-        
+
         if not self.enabled or not self.ready:
             return
-        
+
         if not isinstance(emission.message, (CharacterMessage, NarratorMessage)):
             return
-    
-        if isinstance(emission.message, NarratorMessage) and not self.actions["_config"].config["generate_for_narration"].value:
+
+        if (
+            isinstance(emission.message, NarratorMessage)
+            and not self.actions["_config"].config["generate_for_narration"].value
+        ):
             return
-        
+
         if isinstance(emission.message, CharacterMessage):
-            
-            if emission.message.source == "player" and not self.actions["_config"].config["generate_for_player"].value:
+            if (
+                emission.message.source == "player"
+                and not self.actions["_config"].config["generate_for_player"].value
+            ):
                 return
-            elif emission.message.source == "ai" and not self.actions["_config"].config["generate_for_npc"].value:
+            elif (
+                emission.message.source == "ai"
+                and not self.actions["_config"].config["generate_for_npc"].value
+            ):
                 return
-        
+
         if isinstance(emission.message, CharacterMessage):
             character_prefix = emission.message.split(":", 1)[0]
         else:
             character_prefix = ""
-        
-        log.info("reactive tts", message=emission.message, character_prefix=character_prefix)
-        
-        await self.generate(str(emission.message).replace(character_prefix+": ", ""))
 
+        log.info(
+            "reactive tts", message=emission.message, character_prefix=character_prefix
+        )
 
-    def voice(self, voice_id:str) -> Union[Voice, None]:
+        await self.generate(str(emission.message).replace(character_prefix + ": ", ""))
+
+    def voice(self, voice_id: str) -> Union[Voice, None]:
         for voice in self.voices[self.api].voices:
             if voice.value == voice_id:
                 return voice
         return None
-    
-    def voice_id_to_label(self, voice_id:str):
+
+    def voice_id_to_label(self, voice_id: str):
         for voice in self.voices[self.api].voices:
             if voice.value == voice_id:
                 return voice.label
         return None
-    
+
     def list_voices_sync(self):
         loop = asyncio.get_event_loop()
         return loop.run_until_complete(self.list_voices())
-    
+
     async def list_voices(self):
         if self.requires_token and not self.token:
             return []
-        
+
         library = self.voices[self.api]
-        
+
         # TODO: allow re-syncing voices
         if library.last_synced:
             return library.voices
-        
+
         list_fn = getattr(self, f"_list_voices_{self.api}")
         log.info("Listing voices", api=self.api)
-        
+
         library.voices = await list_fn()
         library.last_synced = time.time()
-        
+
+        if self.preselect_voice:
+            if self.voice(self.preselect_voice):
+                self.actions["_config"].config["voice_id"].value = self.preselect_voice
+                self.preselect_voice = None
+
         # if the current voice cannot be found, reset it
         if not self.voice(self.default_voice_id):
             self.actions["_config"].config["voice_id"].value = ""
-                 
+
         # set loading to false
         return library.voices
 
@@ -407,11 +480,10 @@ class TTSAgent(Agent):
         if not self.enabled or not self.ready or not text:
             return
 
-
         self.playback_done_event.set()
-        
+
         generate_fn = getattr(self, f"_generate_{self.api}")
-        
+
         if self.actions["_config"].config["generate_chunks"].value:
             chunks = parse_chunks(text)
             chunks = rejoin_chunks(chunks)
@@ -421,65 +493,78 @@ class TTSAgent(Agent):
 
         # Start generating audio chunks in the background
         generation_task = asyncio.create_task(self.generate_chunks(generate_fn, chunks))
+        await self.set_background_processing(generation_task)
 
         # Wait for both tasks to complete
-        await asyncio.gather(generation_task)
+        # await asyncio.gather(generation_task)
 
     async def generate_chunks(self, generate_fn, chunks):
         for chunk in chunks:
-            chunk = chunk.replace("*","").strip()
+            chunk = chunk.replace("*", "").strip()
             log.info("Generating audio", api=self.api, chunk=chunk)
             audio_data = await generate_fn(chunk)
             self.play_audio(audio_data)
 
     def play_audio(self, audio_data):
         # play audio through the python audio player
-        #play(audio_data)
-        
-        emit("audio_queue", data={"audio_data": base64.b64encode(audio_data).decode("utf-8")})
-        
+        # play(audio_data)
+
+        emit(
+            "audio_queue",
+            data={"audio_data": base64.b64encode(audio_data).decode("utf-8")},
+        )
+
         self.playback_done_event.set()  # Signal that playback is finished
 
     # LOCAL
-    
+
     async def _generate_tts(self, text: str) -> Union[bytes, None]:
-        
         if not TTS:
             return
-        
-        tts_config = self.config.get("tts",{})
+
+        tts_config = self.config.get("tts", {})
         model = tts_config.get("model")
         device = tts_config.get("device", "cpu")
-        
+
         log.debug("tts local", model=model, device=device)
-        
+
         if not hasattr(self, "tts_instance"):
             self.tts_instance = TTS(model).to(device)
-        
+
         tts = self.tts_instance
-        
+
         loop = asyncio.get_event_loop()
-        
+
         voice = self.voice(self.default_voice_id)
-        
-        
+
         with tempfile.TemporaryDirectory() as temp_dir:
             file_path = os.path.join(temp_dir, f"tts-{uuid.uuid4()}.wav")
-            
-            await loop.run_in_executor(None, functools.partial(tts.tts_to_file, text=text, speaker_wav=voice.value, language="en", file_path=file_path))
-            #tts.tts_to_file(text=text, speaker_wav=voice.value, language="en", file_path=file_path)
-            
-            
+
+            await loop.run_in_executor(
+                None,
+                functools.partial(
+                    tts.tts_to_file,
+                    text=text,
+                    speaker_wav=voice.value,
+                    language="en",
+                    file_path=file_path,
+                ),
+            )
+            # tts.tts_to_file(text=text, speaker_wav=voice.value, language="en", file_path=file_path)
+
             with open(file_path, "rb") as f:
                 return f.read()
-        
-            
+
     async def _list_voices_tts(self) -> dict[str, str]:
-        return [Voice(**voice) for voice in self.config.get("tts",{}).get("voices",[])]
-        
+        return [
+            Voice(**voice) for voice in self.config.get("tts", {}).get("voices", [])
+        ]
+
     # ELEVENLABS
 
-    async def _generate_elevenlabs(self, text: str, chunk_size: int = 1024) -> Union[bytes, None]:
+    async def _generate_elevenlabs(
+        self, text: str, chunk_size: int = 1024
+    ) -> Union[bytes, None]:
         api_key = self.token
         if not api_key:
             return
@@ -493,11 +578,8 @@ class TTSAgent(Agent):
             }
             data = {
                 "text": text,
-                "model_id": self.config.get("elevenlabs",{}).get("model"),
-                "voice_settings": {
-                    "stability": 0.5,
-                    "similarity_boost": 0.5
-                }
+                "model_id": self.config.get("elevenlabs", {}).get("model"),
+                "voice_settings": {"stability": 0.5, "similarity_boost": 0.5},
             }
 
             response = await client.post(url, json=data, headers=headers, timeout=300)
@@ -514,104 +596,57 @@ class TTSAgent(Agent):
                 log.error(f"Error generating audio: {response.text}")
 
     async def _list_voices_elevenlabs(self) -> dict[str, str]:
-        
         url_voices = "https://api.elevenlabs.io/v1/voices"
-        
+
         voices = []
-        
+
         async with httpx.AsyncClient() as client:
             headers = {
                 "Accept": "application/json",
                 "xi-api-key": self.token,
             }
-            response = await client.get(url_voices, headers=headers, params={"per_page":1000})
+            response = await client.get(
+                url_voices, headers=headers, params={"per_page": 1000}
+            )
             speakers = response.json()["voices"]
-            voices.extend([Voice(value=speaker["voice_id"], label=speaker["name"]) for speaker in speakers])
-            
+            voices.extend(
+                [
+                    Voice(value=speaker["voice_id"], label=speaker["name"])
+                    for speaker in speakers
+                ]
+            )
+
         # sort by name
         voices.sort(key=lambda x: x.label)
-            
-        return voices    
-            
-    # COQUI STUDIO
-                
-    async def _generate_coqui(self, text: str) -> Union[bytes, None]:
-        api_key = self.token
-        if not api_key:
-            return
 
-        async with httpx.AsyncClient() as client:
-            url = "https://app.coqui.ai/api/v2/samples/xtts/render/"
-            headers = {
-                "Accept": "application/json",
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {api_key}"
-            }
-            data = {
-                "voice_id": self.default_voice_id,
-                "text": text,
-                "language": "en"  # Assuming English language for simplicity; this could be parameterized
-            }
+        return voices
 
-            # Make the POST request to Coqui API
-            response = await client.post(url, json=data, headers=headers, timeout=300)
-            if response.status_code in [200, 201]:
-                # Parse the JSON response to get the audio URL
-                response_data = response.json()
-                audio_url = response_data.get('audio_url')
-                if audio_url:
-                    # Make a GET request to download the audio file
-                    audio_response = await client.get(audio_url)
-                    if audio_response.status_code == 200:
-                        # delete the sample from Coqui Studio
-                        # await self._cleanup_coqui(response_data.get('id'))
-                        return audio_response.content
-                    else:
-                        log.error(f"Error downloading audio: {audio_response.text}")
-                else:
-                    log.error("No audio URL in response")
-            else:
-                log.error(f"Error generating audio: {response.text}")
-                
-    async def _cleanup_coqui(self, sample_id: str):
-        api_key = self.token
-        if not api_key or not sample_id:
-            return
+    # OPENAI
 
-        async with httpx.AsyncClient() as client:
-            url = f"https://app.coqui.ai/api/v2/samples/xtts/{sample_id}"
-            headers = {
-                "Authorization": f"Bearer {api_key}"
-            }
+    async def _generate_openai(self, text: str, chunk_size: int = 1024):
 
-            # Make the DELETE request to Coqui API
-            response = await client.delete(url, headers=headers)
+        client = AsyncOpenAI(api_key=self.openai_api_key)
 
-            if response.status_code == 204:
-                log.info(f"Successfully deleted sample with ID: {sample_id}")
-            else:
-                log.error(f"Error deleting sample with ID: {sample_id}: {response.text}")
+        model = self.actions["openai"].config["model"].value
 
-    async def _list_voices_coqui(self) -> dict[str, str]:
-        
-        url_speakers = "https://app.coqui.ai/api/v2/speakers"
-        url_custom_voices = "https://app.coqui.ai/api/v2/voices"
-        
-        voices = []
-        
-        async with httpx.AsyncClient() as client:
-            headers = {
-                "Authorization": f"Bearer {self.token}"
-            }
-            response = await client.get(url_speakers, headers=headers, params={"per_page":1000})
-            speakers = response.json()["result"]
-            voices.extend([Voice(value=speaker["id"], label=speaker["name"]) for speaker in speakers])
-            
-            response = await client.get(url_custom_voices, headers=headers, params={"per_page":1000})
-            custom_voices = response.json()["result"]
-            voices.extend([Voice(value=voice["id"], label=voice["name"]) for voice in custom_voices])
-            
-        # sort by name
-        voices.sort(key=lambda x: x.label)
-            
-        return voices
+        response = await client.audio.speech.create(
+            model=model, voice=self.default_voice_id, input=text
+        )
+
+        bytes_io = io.BytesIO()
+        for chunk in response.iter_bytes(chunk_size=chunk_size):
+            if chunk:
+                bytes_io.write(chunk)
+
+        # Put the audio data in the queue for playback
+        return bytes_io.getvalue()
+
+    async def _list_voices_openai(self) -> dict[str, str]:
+        return [
+            Voice(value="alloy", label="Alloy"),
+            Voice(value="echo", label="Echo"),
+            Voice(value="fable", label="Fable"),
+            Voice(value="onyx", label="Onyx"),
+            Voice(value="nova", label="Nova"),
+            Voice(value="shimmer", label="Shimmer"),
+        ]

src/talemate/agents/visual/__init__.py

@@ -0,0 +1,452 @@
+import asyncio
+import traceback
+
+import structlog
+
+import talemate.agents.visual.automatic1111
+import talemate.agents.visual.comfyui
+import talemate.agents.visual.openai_image
+from talemate.agents.base import (
+    Agent,
+    AgentAction,
+    AgentActionConditional,
+    AgentActionConfig,
+    AgentDetail,
+    set_processing,
+)
+from talemate.agents.registry import register
+from talemate.client.base import ClientBase
+from talemate.config import load_config
+from talemate.emit import emit
+from talemate.emit.signals import handlers as signal_handlers
+from talemate.prompts.base import Prompt
+
+from .commands import *  # noqa
+from .context import VIS_TYPES, VisualContext, visual_context
+from .handlers import HANDLERS
+from .schema import RESOLUTION_MAP, RenderSettings
+from .style import MAJOR_STYLES, STYLE_MAP, Style, combine_styles
+from .websocket_handler import VisualWebsocketHandler
+
+__all__ = [
+    "VisualAgent",
+]
+
+BACKENDS = [
+    {"value": mixin_backend, "label": mixin["label"]}
+    for mixin_backend, mixin in HANDLERS.items()
+]
+
+log = structlog.get_logger("talemate.agents.visual")
+
+
+class VisualBase(Agent):
+    """
+    The visual agent
+    """
+
+    agent_type = "visual"
+    verbose_name = "Visualizer"
+    essential = False
+    websocket_handler = VisualWebsocketHandler
+
+    ACTIONS = {}
+
+    def __init__(self, client: ClientBase, *kwargs):
+        self.client = client
+        self.is_enabled = False
+        self.backend_ready = False
+        self.initialized = False
+        self.config = load_config()
+        self.actions = {
+            "_config": AgentAction(
+                enabled=True,
+                label="Configure",
+                description="Visual agent configuration",
+                config={
+                    "backend": AgentActionConfig(
+                        type="text",
+                        choices=BACKENDS,
+                        value="automatic1111",
+                        label="Backend",
+                        description="The backend to use for visual processing",
+                    ),
+                    "default_style": AgentActionConfig(
+                        type="text",
+                        value="ink_illustration",
+                        choices=MAJOR_STYLES,
+                        label="Default Style",
+                        description="The default style to use for visual processing",
+                    ),
+                },
+            ),
+            "automatic_generation": AgentAction(
+                enabled=False,
+                label="Automatic Generation",
+                description="Allow automatic generation of visual content",
+            ),
+            "process_in_background": AgentAction(
+                enabled=True,
+                label="Process in Background",
+                description="Process renders in the background",
+            ),
+        }
+
+        for action_name, action in self.ACTIONS.items():
+            self.actions[action_name] = action
+
+        signal_handlers["config_saved"].connect(self.on_config_saved)
+
+    @property
+    def enabled(self):
+        return self.is_enabled
+
+    @property
+    def has_toggle(self):
+        return True
+
+    @property
+    def experimental(self):
+        return False
+
+    @property
+    def backend(self):
+        return self.actions["_config"].config["backend"].value
+
+    @property
+    def backend_name(self):
+        key = self.actions["_config"].config["backend"].value
+
+        for backend in BACKENDS:
+            if backend["value"] == key:
+                return backend["label"]
+
+    @property
+    def default_style(self):
+        return STYLE_MAP.get(
+            self.actions["_config"].config["default_style"].value, Style()
+        )
+
+    @property
+    def ready(self):
+        return self.backend_ready
+
+    @property
+    def api_url(self):
+        try:
+            return self.actions[self.backend].config["api_url"].value
+        except KeyError:
+            return None
+
+    @property
+    def agent_details(self):
+        details = {
+            "backend": AgentDetail(
+                icon="mdi-server-outline",
+                value=self.backend_name,
+                description="The backend to use for visual processing",
+            ).model_dump(),
+            "client": AgentDetail(
+                icon="mdi-network-outline",
+                value=self.client.name if self.client else None,
+                description="The client to use for prompt generation",
+            ).model_dump(),
+        }
+
+        if not self.ready and self.enabled:
+            details["status"] = AgentDetail(
+                icon="mdi-alert",
+                value=f"{self.backend_name} not ready",
+                color="error",
+                description=self.ready_check_error
+                or f"{self.backend_name} is not ready for processing",
+            ).model_dump()
+
+        return details
+
+    @property
+    def process_in_background(self):
+        return self.actions["process_in_background"].enabled
+
+    @property
+    def allow_automatic_generation(self):
+        return self.actions["automatic_generation"].enabled
+
+    def on_config_saved(self, event):
+        config = event.data
+        self.config = config
+        asyncio.create_task(self.emit_status())
+
+    async def on_ready_check_success(self):
+        prev_ready = self.backend_ready
+        self.backend_ready = True
+        if not prev_ready:
+            await self.emit_status()
+
+    async def on_ready_check_failure(self, error):
+        prev_ready = self.backend_ready
+        self.backend_ready = False
+        self.ready_check_error = str(error)
+        if prev_ready:
+            await self.emit_status()
+
+    async def ready_check(self):
+        if not self.enabled:
+            return
+        backend = self.backend
+        fn = getattr(self, f"{backend.lower()}_ready", None)
+        task = asyncio.create_task(fn())
+        await super().ready_check(task)
+
+    async def apply_config(self, *args, **kwargs):
+
+        try:
+            backend = kwargs["actions"]["_config"]["config"]["backend"]["value"]
+        except KeyError:
+            backend = self.backend
+
+        backend_changed = backend != self.backend
+
+        if backend_changed:
+            self.backend_ready = False
+
+        log.info(
+            "apply_config",
+            backend=backend,
+            backend_changed=backend_changed,
+            old_backend=self.backend,
+        )
+
+        await super().apply_config(*args, **kwargs)
+        backend_fn = getattr(self, f"{self.backend.lower()}_apply_config", None)
+        if backend_fn:
+            task = asyncio.create_task(
+                backend_fn(backend_changed=backend_changed, *args, **kwargs)
+            )
+            await self.set_background_processing(task)
+
+        if not self.backend_ready:
+            await self.ready_check()
+
+        self.initialized = True
+
+    def resolution_from_format(self, format: str, model_type: str = "sdxl"):
+        if model_type not in RESOLUTION_MAP:
+            raise ValueError(f"Model type {model_type} not found in resolution map")
+        return RESOLUTION_MAP[model_type].get(
+            format, RESOLUTION_MAP[model_type]["portrait"]
+        )
+
+    def prepare_prompt(self, prompt: str, styles: list[Style] = None) -> Style:
+
+        prompt_style = Style()
+        prompt_style.load(prompt)
+
+        if styles:
+            prompt_style.prepend(*styles)
+
+        return prompt_style
+
+    def vis_type_styles(self, vis_type: str):
+        if vis_type == VIS_TYPES.CHARACTER:
+            portrait_style = STYLE_MAP["character_portrait"].copy()
+            return portrait_style
+        elif vis_type == VIS_TYPES.ENVIRONMENT:
+            environment_style = STYLE_MAP["environment"].copy()
+            return environment_style
+        return Style()
+
+    async def apply_image(self, image: str):
+        context = visual_context.get()
+
+        log.debug("apply_image", image=image[:100], context=context)
+
+        if context.vis_type == VIS_TYPES.CHARACTER:
+            await self.apply_image_character(image, context.character_name)
+
+    async def apply_image_character(self, image: str, character_name: str):
+        character = self.scene.get_character(character_name)
+
+        if not character:
+            log.error("character not found", character_name=character_name)
+            return
+
+        if character.cover_image:
+            log.info("character cover image already set", character_name=character_name)
+            return
+
+        asset = self.scene.assets.add_asset_from_image_data(
+            f"data:image/png;base64,{image}"
+        )
+        character.cover_image = asset.id
+        self.scene.assets.cover_image = asset.id
+        self.scene.emit_status()
+
+    async def emit_image(self, image: str):
+        context = visual_context.get()
+        await self.apply_image(image)
+        emit(
+            "image_generated",
+            websocket_passthrough=True,
+            data={
+                "base64": image,
+                "context": context.model_dump() if context else None,
+            },
+        )
+
+    @set_processing
+    async def generate(
+        self, format: str = "portrait", prompt: str = None, automatic: bool = False
+    ):
+
+        context = visual_context.get()
+
+        if not self.enabled:
+            log.warning("generate", skipped="Visual agent not enabled")
+            return
+
+        if automatic and not self.allow_automatic_generation:
+            log.warning(
+                "generate",
+                skipped="Automatic generation disabled",
+                prompt=prompt,
+                format=format,
+                context=context,
+            )
+            return
+
+        if not context and not prompt:
+            log.error("generate", error="No context or prompt provided")
+            return
+
+        # Handle prompt generation based on context
+
+        if not prompt and context.prompt:
+            prompt = context.prompt
+
+        if context.vis_type == VIS_TYPES.ENVIRONMENT and not prompt:
+            prompt = await self.generate_environment_prompt(
+                instructions=context.instructions
+            )
+        elif context.vis_type == VIS_TYPES.CHARACTER and not prompt:
+            prompt = await self.generate_character_prompt(
+                context.character_name, instructions=context.instructions
+            )
+        else:
+            prompt = prompt or context.prompt
+
+        initial_prompt = prompt
+
+        # Augment the prompt with styles based on context
+
+        thematic_style = self.default_style
+        vis_type_styles = self.vis_type_styles(context.vis_type)
+        prompt = self.prepare_prompt(prompt, [vis_type_styles, thematic_style])
+
+        if not prompt:
+            log.error(
+                "generate", error="No prompt provided and no context to generate from"
+            )
+            return
+
+        context.prompt = initial_prompt
+        context.prepared_prompt = str(prompt)
+
+        # Handle format (can either come from context or be passed in)
+
+        if not format and context.format:
+            format = context.format
+        elif not format:
+            format = "portrait"
+
+        context.format = format
+
+        # Call the backend specific generate function
+
+        backend = self.backend
+        fn = f"{backend.lower()}_generate"
+
+        log.info(
+            "generate", backend=backend, prompt=prompt, format=format, context=context
+        )
+
+        if not hasattr(self, fn):
+            log.error("generate", error=f"Backend {backend} does not support generate")
+
+        # add the function call to the asyncio task queue
+
+        if self.process_in_background:
+            task = asyncio.create_task(getattr(self, fn)(prompt=prompt, format=format))
+            await self.set_background_processing(task)
+        else:
+            await getattr(self, fn)(prompt=prompt, format=format)
+
+    @set_processing
+    async def generate_environment_prompt(self, instructions: str = None):
+
+        response = await Prompt.request(
+            "visual.generate-environment-prompt",
+            self.client,
+            "visualize",
+            {
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+            },
+        )
+
+        return response.strip()
+
+    @set_processing
+    async def generate_character_prompt(
+        self, character_name: str, instructions: str = None
+    ):
+
+        character = self.scene.get_character(character_name)
+
+        response = await Prompt.request(
+            "visual.generate-character-prompt",
+            self.client,
+            "visualize",
+            {
+                "scene": self.scene,
+                "character_name": character_name,
+                "character": character,
+                "max_tokens": self.client.max_token_length,
+                "instructions": instructions or "",
+            },
+        )
+
+        return response.strip()
+
+    async def generate_environment_background(self, instructions: str = None):
+        with VisualContext(vis_type=VIS_TYPES.ENVIRONMENT, instructions=instructions):
+            await self.generate(format="landscape")
+
+    async def generate_character_portrait(
+        self,
+        character_name: str,
+        instructions: str = None,
+    ):
+        with VisualContext(
+            vis_type=VIS_TYPES.CHARACTER,
+            character_name=character_name,
+            instructions=instructions,
+        ):
+            await self.generate(format="portrait")
+
+
+# apply mixins to the agent (from HANDLERS dict[str, cls])
+
+for mixin_backend, mixin in HANDLERS.items():
+    mixin_cls = mixin["cls"]
+    VisualBase = type("VisualAgent", (mixin_cls, VisualBase), {})
+
+    extend_actions = getattr(mixin_cls, "EXTEND_ACTIONS", {})
+
+    for action_name, action in extend_actions.items():
+        VisualBase.ACTIONS[action_name] = action
+
+
+@register()
+class VisualAgent(VisualBase):
+    pass

src/talemate/agents/visual/automatic1111.py

@@ -0,0 +1,117 @@
+import base64
+import io
+
+import httpx
+import structlog
+from PIL import Image
+
+from talemate.agents.base import (
+    Agent,
+    AgentAction,
+    AgentActionConditional,
+    AgentActionConfig,
+    AgentDetail,
+    set_processing,
+)
+
+from .handlers import register
+from .schema import RenderSettings, Resolution
+from .style import STYLE_MAP, Style
+
+log = structlog.get_logger("talemate.agents.visual.automatic1111")
+
+
+@register(backend_name="automatic1111", label="AUTOMATIC1111")
+class Automatic1111Mixin:
+
+    automatic1111_default_render_settings = RenderSettings()
+
+    EXTEND_ACTIONS = {
+        "automatic1111": AgentAction(
+            enabled=True,
+            condition=AgentActionConditional(
+                attribute="_config.config.backend", value="automatic1111"
+            ),
+            label="Automatic1111 Settings",
+            description="Setting overrides for the automatic1111 backend",
+            config={
+                "api_url": AgentActionConfig(
+                    type="text",
+                    value="http://localhost:7860",
+                    label="API URL",
+                    description="The URL of the backend API",
+                ),
+                "steps": AgentActionConfig(
+                    type="number",
+                    value=40,
+                    label="Steps",
+                    min=5,
+                    max=150,
+                    step=1,
+                    description="number of render steps",
+                ),
+                "model_type": AgentActionConfig(
+                    type="text",
+                    value="sdxl",
+                    choices=[
+                        {"value": "sdxl", "label": "SDXL"},
+                        {"value": "sd15", "label": "SD1.5"},
+                    ],
+                    label="Model Type",
+                    description="Right now just differentiates between sdxl and sd15 - affect generation resolution",
+                ),
+            },
+        )
+    }
+
+    @property
+    def automatic1111_render_settings(self):
+        if self.actions["automatic1111"].enabled:
+            return RenderSettings(
+                steps=self.actions["automatic1111"].config["steps"].value,
+                type_model=self.actions["automatic1111"].config["model_type"].value,
+            )
+        else:
+            return self.automatic1111_default_render_settings
+
+    async def automatic1111_generate(self, prompt: Style, format: str):
+        url = self.api_url
+        resolution = self.resolution_from_format(
+            format, self.automatic1111_render_settings.type_model
+        )
+        render_settings = self.automatic1111_render_settings
+        payload = {
+            "prompt": prompt.positive_prompt,
+            "negative_prompt": prompt.negative_prompt,
+            "steps": render_settings.steps,
+            "width": resolution.width,
+            "height": resolution.height,
+        }
+
+        log.info("automatic1111_generate", payload=payload, url=url)
+
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                url=f"{url}/sdapi/v1/txt2img", json=payload, timeout=90
+            )
+
+        r = response.json()
+
+        # image = Image.open(io.BytesIO(base64.b64decode(r['images'][0])))
+        # image.save('a1111-test.png')
+
+        #'log.info("automatic1111_generate", saved_to="a1111-test.png")
+
+        for image in r["images"]:
+            await self.emit_image(image)
+
+    async def automatic1111_ready(self) -> bool:
+        """
+        Will send a GET to /sdapi/v1/memory and on 200 will return True
+        """
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(
+                url=f"{self.api_url}/sdapi/v1/memory", timeout=2
+            )
+            return response.status_code == 200

src/talemate/agents/visual/comfyui.py

@@ -0,0 +1,324 @@
+import asyncio
+import base64
+import io
+import json
+import os
+import random
+import time
+import urllib.parse
+
+import httpx
+import pydantic
+import structlog
+from PIL import Image
+
+from talemate.agents.base import AgentAction, AgentActionConditional, AgentActionConfig
+
+from .handlers import register
+from .schema import RenderSettings, Resolution
+from .style import STYLE_MAP, Style
+
+log = structlog.get_logger("talemate.agents.visual.comfyui")
+
+
+class Workflow(pydantic.BaseModel):
+    nodes: dict
+
+    def set_resolution(self, resolution: Resolution):
+
+        # will collect all latent image nodes
+        # if there is multiple will look for the one with the
+        # title "Talemate Resolution"
+
+        # if there is no latent image node with the title "Talemate Resolution"
+        # the first latent image node will be used
+
+        # resolution will be updated on the selected node
+
+        # if no latent image node is found a warning will be logged
+
+        latent_image_node = None
+
+        for node_id, node in self.nodes.items():
+            if node["class_type"] == "EmptyLatentImage":
+                if not latent_image_node:
+                    latent_image_node = node
+                elif node["_meta"]["title"] == "Talemate Resolution":
+                    latent_image_node = node
+                    break
+
+        if not latent_image_node:
+            log.warning("set_resolution", error="No latent image node found")
+            return
+
+        latent_image_node["inputs"]["width"] = resolution.width
+        latent_image_node["inputs"]["height"] = resolution.height
+
+    def set_prompt(self, prompt: str, negative_prompt: str = None):
+
+        # will collect all CLIPTextEncode nodes
+
+        # if there is multiple will look for the one with the
+        # title "Talemate Positive Prompt" and "Talemate Negative Prompt"
+        #
+        # if there is no CLIPTextEncode node with the title "Talemate Positive Prompt"
+        # the first CLIPTextEncode node will be used
+        #
+        # if there is no CLIPTextEncode node with the title "Talemate Negative Prompt"
+        # the second CLIPTextEncode node will be used
+        #
+        # prompt will be updated on the selected node
+
+        # if no CLIPTextEncode node is found an exception will be raised for
+        # the positive prompt
+
+        # if no CLIPTextEncode node is found an exception will be raised for
+        # the negative prompt if it is not None
+
+        positive_prompt_node = None
+        negative_prompt_node = None
+
+        for node_id, node in self.nodes.items():
+
+            if node["class_type"] == "CLIPTextEncode":
+                if not positive_prompt_node:
+                    positive_prompt_node = node
+                elif node["_meta"]["title"] == "Talemate Positive Prompt":
+                    positive_prompt_node = node
+                elif not negative_prompt_node:
+                    negative_prompt_node = node
+                elif node["_meta"]["title"] == "Talemate Negative Prompt":
+                    negative_prompt_node = node
+
+        if not positive_prompt_node:
+            raise ValueError("No positive prompt node found")
+
+        positive_prompt_node["inputs"]["text"] = prompt
+
+        if negative_prompt and not negative_prompt_node:
+            raise ValueError("No negative prompt node found")
+
+        if negative_prompt:
+            negative_prompt_node["inputs"]["text"] = negative_prompt
+
+    def set_checkpoint(self, checkpoint: str):
+
+        # will collect all CheckpointLoaderSimple nodes
+        # if there is multiple will look for the one with the
+        # title "Talemate Load Checkpoint"
+
+        # if there is no CheckpointLoaderSimple node with the title "Talemate Load Checkpoint"
+        # the first CheckpointLoaderSimple node will be used
+
+        # checkpoint will be updated on the selected node
+
+        # if no CheckpointLoaderSimple node is found a warning will be logged
+
+        checkpoint_node = None
+
+        for node_id, node in self.nodes.items():
+            if node["class_type"] == "CheckpointLoaderSimple":
+                if not checkpoint_node:
+                    checkpoint_node = node
+                elif node["_meta"]["title"] == "Talemate Load Checkpoint":
+                    checkpoint_node = node
+                    break
+
+        if not checkpoint_node:
+            log.warning("set_checkpoint", error="No checkpoint node found")
+            return
+
+        checkpoint_node["inputs"]["ckpt_name"] = checkpoint
+
+    def set_seeds(self):
+        for node in self.nodes.values():
+            for field in node.get("inputs", {}).keys():
+                if field == "noise_seed":
+                    node["inputs"]["noise_seed"] = random.randint(0, 999999999999999)
+
+
+@register(backend_name="comfyui", label="ComfyUI")
+class ComfyUIMixin:
+
+    comfyui_default_render_settings = RenderSettings()
+
+    EXTEND_ACTIONS = {
+        "comfyui": AgentAction(
+            enabled=True,
+            condition=AgentActionConditional(
+                attribute="_config.config.backend", value="comfyui"
+            ),
+            label="ComfyUI Settings",
+            description="Setting overrides for the comfyui backend",
+            config={
+                "api_url": AgentActionConfig(
+                    type="text",
+                    value="http://localhost:8188",
+                    label="API URL",
+                    description="The URL of the backend API",
+                ),
+                "workflow": AgentActionConfig(
+                    type="text",
+                    value="default-sdxl.json",
+                    label="Workflow",
+                    description="The workflow to use for comfyui (workflow file name inside ./templates/comfyui-workflows)",
+                ),
+                "checkpoint": AgentActionConfig(
+                    type="text",
+                    value="default",
+                    label="Checkpoint",
+                    choices=[],
+                    description="The main checkpoint to use.",
+                ),
+            },
+        )
+    }
+
+    @property
+    def comfyui_workflow_filename(self):
+        base_name = self.actions["comfyui"].config["workflow"].value
+
+        # make absolute path
+        abs_path = os.path.join(
+            os.path.dirname(__file__),
+            "..",
+            "..",
+            "..",
+            "..",
+            "templates",
+            "comfyui-workflows",
+            base_name,
+        )
+
+        return abs_path
+
+    @property
+    def comfyui_workflow_is_sdxl(self) -> bool:
+        """
+        Returns true if `sdxl` is in worhflow file name (case insensitive)
+        """
+
+        return "sdxl" in self.comfyui_workflow_filename.lower()
+
+    @property
+    def comfyui_workflow(self) -> Workflow:
+        workflow = self.comfyui_workflow_filename
+        if not workflow:
+            raise ValueError("No comfyui workflow file specified")
+
+        with open(workflow, "r") as f:
+            return Workflow(nodes=json.load(f))
+
+    @property
+    async def comfyui_object_info(self):
+        if hasattr(self, "_comfyui_object_info"):
+            return self._comfyui_object_info
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url=f"{self.api_url}/object_info")
+            self._comfyui_object_info = response.json()
+
+        return self._comfyui_object_info
+
+    @property
+    async def comfyui_checkpoints(self):
+        loader_node = (await self.comfyui_object_info)["CheckpointLoaderSimple"]
+        _checkpoints = loader_node["input"]["required"]["ckpt_name"][0]
+        return [
+            {"label": checkpoint, "value": checkpoint} for checkpoint in _checkpoints
+        ]
+
+    async def comfyui_get_image(self, filename: str, subfolder: str, folder_type: str):
+        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+        url_values = urllib.parse.urlencode(data)
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url=f"{self.api_url}/view?{url_values}")
+            return response.content
+
+    async def comfyui_get_history(self, prompt_id: str):
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url=f"{self.api_url}/history/{prompt_id}")
+            return response.json()
+
+    async def comfyui_get_images(self, prompt_id: str, max_wait: int = 60.0):
+        output_images = {}
+        history = {}
+
+        start = time.time()
+
+        while not history:
+            log.info(
+                "comfyui_get_images", waiting_for_history=True, prompt_id=prompt_id
+            )
+            history = await self.comfyui_get_history(prompt_id)
+            await asyncio.sleep(1.0)
+            if time.time() - start > max_wait:
+                raise TimeoutError("Max wait time exceeded")
+
+        for node_id, node_output in history[prompt_id]["outputs"].items():
+            if "images" in node_output:
+                images_output = []
+                for image in node_output["images"]:
+                    image_data = await self.comfyui_get_image(
+                        image["filename"], image["subfolder"], image["type"]
+                    )
+                    images_output.append(image_data)
+            output_images[node_id] = images_output
+
+        return output_images
+
+    async def comfyui_generate(self, prompt: Style, format: str):
+        url = self.api_url
+        workflow = self.comfyui_workflow
+        is_sdxl = self.comfyui_workflow_is_sdxl
+
+        resolution = self.resolution_from_format(format, "sdxl" if is_sdxl else "sd15")
+
+        workflow.set_resolution(resolution)
+        workflow.set_prompt(prompt.positive_prompt, prompt.negative_prompt)
+        workflow.set_seeds()
+        workflow.set_checkpoint(self.actions["comfyui"].config["checkpoint"].value)
+
+        payload = {"prompt": workflow.model_dump().get("nodes")}
+
+        log.info("comfyui_generate", payload=payload, url=url)
+
+        async with httpx.AsyncClient() as client:
+            response = await client.post(url=f"{url}/prompt", json=payload, timeout=90)
+
+        log.info("comfyui_generate", response=response.text)
+
+        r = response.json()
+
+        prompt_id = r["prompt_id"]
+
+        images = await self.comfyui_get_images(prompt_id)
+        for node_id, node_images in images.items():
+            for i, image in enumerate(node_images):
+                await self.emit_image(base64.b64encode(image).decode("utf-8"))
+                # image = Image.open(io.BytesIO(image))
+                # image.save(f'comfyui-test.png')
+
+    async def comfyui_apply_config(
+        self, backend_changed: bool = False, *args, **kwargs
+    ):
+        log.debug(
+            "comfyui_apply_config",
+            backend_changed=backend_changed,
+            enabled=self.enabled,
+        )
+        if (not self.initialized or backend_changed) and self.enabled:
+            checkpoints = await self.comfyui_checkpoints
+            selected_checkpoint = self.actions["comfyui"].config["checkpoint"].value
+            self.actions["comfyui"].config["checkpoint"].choices = checkpoints
+            self.actions["comfyui"].config["checkpoint"].value = selected_checkpoint
+
+    async def comfyui_ready(self) -> bool:
+        """
+        Will send a GET to /system_stats and on 200 will return True
+        """
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(url=f"{self.api_url}/system_stats", timeout=2)
+            return response.status_code == 200

src/talemate/agents/visual/commands.py

@@ -0,0 +1,68 @@
+from talemate.agents.visual.context import VIS_TYPES, VisualContext
+from talemate.commands.base import TalemateCommand
+from talemate.commands.manager import register
+from talemate.instance import get_agent
+
+__all__ = [
+    "CmdVisualizeTestGenerate",
+]
+
+
+@register
+class CmdVisualizeTestGenerate(TalemateCommand):
+    """
+    Generates a visual test
+    """
+
+    name = "visual_test_generate"
+    description = "Will generate a visual test"
+    aliases = ["vis_test", "vtg"]
+
+    label = "Visualize test"
+
+    async def run(self):
+        visual = get_agent("visual")
+        prompt = self.args[0]
+        with VisualContext(vis_type=VIS_TYPES.UNSPECIFIED):
+            await visual.generate(prompt)
+        return True
+
+
+@register
+class CmdVisualizeEnvironment(TalemateCommand):
+    """
+    Shows the environment
+    """
+
+    name = "visual_environment"
+    description = "Will show the environment"
+    aliases = ["vis_env"]
+
+    label = "Visualize environment"
+
+    async def run(self):
+        visual = get_agent("visual")
+        await visual.generate_environment_background(
+            instructions=self.args[0] if len(self.args) > 0 else None
+        )
+        return True
+
+
+@register
+class CmdVisualizeCharacter(TalemateCommand):
+    """
+    Shows a character
+    """
+
+    name = "visual_character"
+    description = "Will show a character"
+    aliases = ["vis_char"]
+
+    label = "Visualize character"
+
+    async def run(self):
+        visual = get_agent("visual")
+        character_name = self.args[0]
+        instructions = self.args[1] if len(self.args) > 1 else None
+        await visual.generate_character_portrait(character_name, instructions)
+        return True

src/talemate/agents/visual/context.py

@@ -0,0 +1,55 @@
+import contextvars
+import enum
+from typing import Union
+
+import pydantic
+
+__all__ = [
+    "VIS_TYPES",
+    "visual_context",
+    "VisualContext",
+]
+
+
+class VIS_TYPES(str, enum.Enum):
+    UNSPECIFIED = "UNSPECIFIED"
+    ENVIRONMENT = "ENVIRONMENT"
+    CHARACTER = "CHARACTER"
+    ITEM = "ITEM"
+
+
+visual_context = contextvars.ContextVar("visual_context", default=None)
+
+
+class VisualContextState(pydantic.BaseModel):
+    character_name: Union[str, None] = None
+    instructions: Union[str, None] = None
+    vis_type: VIS_TYPES = VIS_TYPES.ENVIRONMENT
+    prompt: Union[str, None] = None
+    prepared_prompt: Union[str, None] = None
+    format: Union[str, None] = None
+
+
+class VisualContext:
+    def __init__(
+        self,
+        character_name: Union[str, None] = None,
+        instructions: Union[str, None] = None,
+        vis_type: VIS_TYPES = VIS_TYPES.ENVIRONMENT,
+        prompt: Union[str, None] = None,
+        **kwargs,
+    ):
+        self.state = VisualContextState(
+            character_name=character_name,
+            instructions=instructions,
+            vis_type=vis_type,
+            prompt=prompt,
+            **kwargs,
+        )
+
+    def __enter__(self):
+        self.token = visual_context.set(self.state)
+
+    def __exit__(self, *args, **kwargs):
+        visual_context.reset(self.token)
+        return False

src/talemate/agents/visual/handlers.py

@@ -0,0 +1,17 @@
+__all__ = [
+    "HANDLERS",
+    "register",
+]
+
+HANDLERS = {}
+
+
+class register:
+
+    def __init__(self, backend_name: str, label: str):
+        self.backend_name = backend_name
+        self.label = label
+
+    def __call__(self, mixin_cls):
+        HANDLERS[self.backend_name] = {"label": self.label, "cls": mixin_cls}
+        return mixin_cls

src/talemate/agents/visual/openai_image.py

@@ -0,0 +1,127 @@
+import base64
+import io
+
+import httpx
+import structlog
+from openai import AsyncOpenAI
+from PIL import Image
+
+from talemate.agents.base import (
+    Agent,
+    AgentAction,
+    AgentActionConditional,
+    AgentActionConfig,
+    AgentDetail,
+    set_processing,
+)
+
+from .handlers import register
+from .schema import RenderSettings, Resolution
+from .style import STYLE_MAP, Style
+
+log = structlog.get_logger("talemate.agents.visual.openai_image")
+
+
+@register(backend_name="openai_image", label="OpenAI")
+class OpenAIImageMixin:
+
+    openai_image_default_render_settings = RenderSettings()
+
+    EXTEND_ACTIONS = {
+        "openai_image": AgentAction(
+            enabled=False,
+            condition=AgentActionConditional(
+                attribute="_config.config.backend", value="openai_image"
+            ),
+            label="OpenAI Image Generation Advanced Settings",
+            description="Setting overrides for the openai backend",
+            config={
+                "model_type": AgentActionConfig(
+                    type="text",
+                    value="dall-e-3",
+                    choices=[
+                        {"value": "dall-e-3", "label": "DALL-E 3"},
+                        {"value": "dall-e-2", "label": "DALL-E 2"},
+                    ],
+                    label="Model Type",
+                    description="Image generation model",
+                ),
+                "quality": AgentActionConfig(
+                    type="text",
+                    value="standard",
+                    choices=[
+                        {"value": "standard", "label": "Standard"},
+                        {"value": "hd", "label": "HD"},
+                    ],
+                    label="Quality",
+                    description="Image generation quality",
+                ),
+            },
+        )
+    }
+
+    @property
+    def openai_api_key(self):
+        return self.config.get("openai", {}).get("api_key")
+
+    @property
+    def openai_model_type(self):
+        return self.actions["openai_image"].config["model_type"].value
+
+    @property
+    def openai_quality(self):
+        return self.actions["openai_image"].config["quality"].value
+
+    async def openai_image_generate(self, prompt: Style, format: str):
+        """
+        #
+        from openai import OpenAI
+        client = OpenAI()
+
+        response = client.images.generate(
+            model="dall-e-3",
+            prompt="a white siamese cat",
+            size="1024x1024",
+            quality="standard",
+            n=1,
+        )
+
+        image_url = response.data[0].url
+        """
+
+        client = AsyncOpenAI(api_key=self.openai_api_key)
+
+        # When using DALL·E 3, images can have a size of 1024x1024, 1024x1792 or 1792x1024 pixels.#
+
+        if format == "portrait":
+            resolution = Resolution(width=1024, height=1792)
+        elif format == "landscape":
+            resolution = Resolution(width=1792, height=1024)
+        else:
+            resolution = Resolution(width=1024, height=1024)
+
+        response = await client.images.generate(
+            model=self.openai_model_type,
+            prompt=prompt.positive_prompt,
+            size=f"{resolution.width}x{resolution.height}",
+            quality=self.openai_quality,
+            n=1,
+        )
+
+        download_url = response.data[0].url
+
+        async with httpx.AsyncClient() as client:
+            response = await client.get(download_url, timeout=90)
+            # bytes to base64encoded
+            image = base64.b64encode(response.content).decode("utf-8")
+            await self.emit_image(image)
+
+    async def openai_image_ready(self) -> bool:
+        """
+        Will send a GET to /sdapi/v1/memory and on 200 will return True
+        """
+
+        if not self.openai_api_key:
+            raise ValueError("OpenAI API Key not set")
+
+        return True

src/talemate/agents/visual/schema.py

@@ -0,0 +1,32 @@
+import pydantic
+
+__all__ = [
+    "RenderSettings",
+    "Resolution",
+    "RESOLUTION_MAP",
+]
+
+RESOLUTION_MAP = {}
+
+
+class RenderSettings(pydantic.BaseModel):
+    type_model: str = "sdxl"
+    steps: int = 40
+
+
+class Resolution(pydantic.BaseModel):
+    width: int
+    height: int
+
+
+RESOLUTION_MAP["sdxl"] = {
+    "portrait": Resolution(width=832, height=1216),
+    "landscape": Resolution(width=1216, height=832),
+    "square": Resolution(width=1024, height=1024),
+}
+
+RESOLUTION_MAP["sd15"] = {
+    "portrait": Resolution(width=512, height=768),
+    "landscape": Resolution(width=768, height=512),
+    "square": Resolution(width=768, height=768),
+}

src/talemate/agents/visual/style.py

@@ -0,0 +1,112 @@
+import pydantic
+
+__all__ = [
+    "Style",
+    "STYLE_MAP",
+    "THEME_MAP",
+    "MAJOR_STYLES",
+    "combine_styles",
+]
+
+STYLE_MAP = {}
+THEME_MAP = {}
+MAJOR_STYLES = {}
+
+
+class Style(pydantic.BaseModel):
+    keywords: list[str] = pydantic.Field(default_factory=list)
+    negative_keywords: list[str] = pydantic.Field(default_factory=list)
+
+    @property
+    def positive_prompt(self):
+        return ", ".join(self.keywords)
+
+    @property
+    def negative_prompt(self):
+        return ", ".join(self.negative_keywords)
+
+    def __str__(self):
+        return f"POSITIVE: {self.positive_prompt}\nNEGATIVE: {self.negative_prompt}"
+
+    def load(self, prompt: str, negative_prompt: str = ""):
+        self.keywords = prompt.split(", ")
+        self.negative_keywords = negative_prompt.split(", ")
+        return self
+
+    def prepend(self, *styles):
+        for style in styles:
+            for idx in range(len(style.keywords) - 1, -1, -1):
+                kw = style.keywords[idx]
+                if kw not in self.keywords:
+                    self.keywords.insert(0, kw)
+
+            for idx in range(len(style.negative_keywords) - 1, -1, -1):
+                kw = style.negative_keywords[idx]
+                if kw not in self.negative_keywords:
+                    self.negative_keywords.insert(0, kw)
+
+        return self
+
+    def append(self, *styles):
+        for style in styles:
+            for kw in style.keywords:
+                if kw not in self.keywords:
+                    self.keywords.append(kw)
+
+            for kw in style.negative_keywords:
+                if kw not in self.negative_keywords:
+                    self.negative_keywords.append(kw)
+
+        return self
+
+    def copy(self):
+        return Style(
+            keywords=self.keywords.copy(),
+            negative_keywords=self.negative_keywords.copy(),
+        )
+
+
+# Almost taken straight from some of the fooocus style presets, credit goes to the original author
+
+STYLE_MAP["digital_art"] = Style(
+    keywords="digital artwork, masterpiece, best quality, high detail".split(", "),
+    negative_keywords="text, watermark, low quality, blurry, photo".split(", "),
+)
+
+STYLE_MAP["concept_art"] = Style(
+    keywords="concept art, conceptual sketch, masterpiece, best quality, high detail".split(
+        ", "
+    ),
+    negative_keywords="text, watermark, low quality, blurry, photo".split(", "),
+)
+
+STYLE_MAP["ink_illustration"] = Style(
+    keywords="ink illustration, painting, masterpiece, best quality".split(", "),
+    negative_keywords="text, watermark, low quality, blurry, photo".split(", "),
+)
+
+STYLE_MAP["anime"] = Style(
+    keywords="anime, masterpiece, best quality, illustration".split(", "),
+    negative_keywords="text, watermark, low quality, blurry, photo, 3d".split(", "),
+)
+
+STYLE_MAP["character_portrait"] = Style(keywords="solo, looking at viewer".split(", "))
+
+STYLE_MAP["environment"] = Style(
+    keywords="scenery, environment, background, postcard".split(", "),
+    negative_keywords="character, portrait, looking at viewer, people".split(", "),
+)
+
+MAJOR_STYLES = [
+    {"value": "digital_art", "label": "Digital Art"},
+    {"value": "concept_art", "label": "Concept Art"},
+    {"value": "ink_illustration", "label": "Ink Illustration"},
+    {"value": "anime", "label": "Anime"},
+]
+
+
+def combine_styles(*styles):
+    keywords = []
+    for style in styles:
+        keywords.extend(style.keywords)
+    return Style(keywords=list(set(keywords)))

src/talemate/agents/visual/websocket_handler.py

@@ -0,0 +1,84 @@
+from typing import Union
+
+import pydantic
+import structlog
+
+from talemate.instance import get_agent
+from talemate.server.websocket_plugin import Plugin
+
+from .context import VisualContext, VisualContextState
+
+__all__ = [
+    "VisualWebsocketHandler",
+]
+
+log = structlog.get_logger("talemate.server.visual")
+
+
+class SetCoverImagePayload(pydantic.BaseModel):
+    base64: str
+    context: Union[VisualContextState, None] = None
+
+
+class RegeneratePayload(pydantic.BaseModel):
+    context: Union[VisualContextState, None] = None
+
+
+class VisualWebsocketHandler(Plugin):
+    router = "visual"
+
+    async def handle_regenerate(self, data: dict):
+        """
+        Regenerate the image based on the context.
+        """
+
+        payload = RegeneratePayload(**data)
+
+        context = payload.context
+
+        visual = get_agent("visual")
+
+        with VisualContext(**context.model_dump()):
+            await visual.generate(format="")
+
+    async def handle_cover_image(self, data: dict):
+        """
+        Sets the cover image for a character and the scene.
+        """
+
+        payload = SetCoverImagePayload(**data)
+
+        context = payload.context
+        scene = self.scene
+
+        if context and context.character_name:
+
+            character = scene.get_character(context.character_name)
+
+            if not character:
+                log.error("character not found", character_name=context.character_name)
+                return
+
+            asset = scene.assets.add_asset_from_image_data(payload.base64)
+
+            log.info("setting scene cover image", character_name=context.character_name)
+            scene.assets.cover_image = asset.id
+
+            log.info(
+                "setting character cover image", character_name=context.character_name
+            )
+            character.cover_image = asset.id
+
+            scene.emit_status()
+            self.websocket_handler.request_scene_assets([asset.id])
+
+            self.websocket_handler.queue_put(
+                {
+                    "type": "scene_asset_character_cover_image",
+                    "asset_id": asset.id,
+                    "asset": self.scene.assets.get_asset_bytes_as_base64(asset.id),
+                    "media_type": asset.media_type,
+                    "character": character.name,
+                }
+            )
+            return

src/talemate/agents/world_state.py

@@ -1,46 +1,54 @@
 from __future__ import annotations
+
 import dataclasses
 import json
+import time
 import uuid
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
+import isodate
+import structlog
+
 import talemate.emit.async_signals
 import talemate.util as util
-from talemate.world_state import InsertionMode
-from talemate.prompts import Prompt
-from talemate.scene_message import DirectorMessage, TimePassageMessage, ReinforcementMessage
 from talemate.emit import emit
 from talemate.events import GameLoopEvent
 from talemate.instance import get_agent
+from talemate.prompts import Prompt
+from talemate.scene_message import (
+    DirectorMessage,
+    ReinforcementMessage,
+    TimePassageMessage,
+)
+from talemate.world_state import InsertionMode
 
-from .base import Agent, set_processing, AgentAction, AgentActionConfig, AgentEmission
+from .base import Agent, AgentAction, AgentActionConfig, AgentEmission, set_processing
 from .registry import register
 
-import structlog
-import isodate
-import time
-
-
 log = structlog.get_logger("talemate.agents.world_state")
 
 talemate.emit.async_signals.register("agent.world_state.time")
 
+
 @dataclasses.dataclass
 class WorldStateAgentEmission(AgentEmission):
     """
     Emission class for world state agent
     """
+
     pass
 
+
 @dataclasses.dataclass
 class TimePassageEmission(WorldStateAgentEmission):
     """
     Emission class for time passage
     """
+
     duration: str
     narrative: str
     human_duration: str = None
-    
+
 
 @register()
 class WorldStateAgent(Agent):
@@ -55,26 +63,57 @@ class WorldStateAgent(Agent):
         self.client = client
         self.is_enabled = True
         self.actions = {
-            "update_world_state": AgentAction(enabled=True, label="Update world state", description="Will attempt to update the world state based on the current scene. Runs automatically every N turns.", config={
-                "turns": AgentActionConfig(type="number", label="Turns", description="Number of turns to wait before updating the world state.", value=5, min=1, max=100, step=1)
-            }),
-            "update_reinforcements": AgentAction(enabled=True, label="Update state reinforcements", description="Will attempt to update any due state reinforcements.", config={}),
-            "check_pin_conditions": AgentAction(enabled=True, label="Update conditional context pins", description="Will evaluate context pins conditions and toggle those pins accordingly. Runs automatically every N turns.", config={
-                "turns": AgentActionConfig(type="number", label="Turns", description="Number of turns to wait before checking conditions.", value=2, min=1, max=100, step=1)
-            }),
+            "update_world_state": AgentAction(
+                enabled=True,
+                label="Update world state",
+                description="Will attempt to update the world state based on the current scene. Runs automatically every N turns.",
+                config={
+                    "turns": AgentActionConfig(
+                        type="number",
+                        label="Turns",
+                        description="Number of turns to wait before updating the world state.",
+                        value=5,
+                        min=1,
+                        max=100,
+                        step=1,
+                    )
+                },
+            ),
+            "update_reinforcements": AgentAction(
+                enabled=True,
+                label="Update state reinforcements",
+                description="Will attempt to update any due state reinforcements.",
+                config={},
+            ),
+            "check_pin_conditions": AgentAction(
+                enabled=True,
+                label="Update conditional context pins",
+                description="Will evaluate context pins conditions and toggle those pins accordingly. Runs automatically every N turns.",
+                config={
+                    "turns": AgentActionConfig(
+                        type="number",
+                        label="Turns",
+                        description="Number of turns to wait before checking conditions.",
+                        value=2,
+                        min=1,
+                        max=100,
+                        step=1,
+                    )
+                },
+            ),
         }
-        
+
         self.next_update = 0
         self.next_pin_check = 0
-        
+
     @property
     def enabled(self):
         return self.is_enabled
-    
+
     @property
     def has_toggle(self):
         return True
-        
+
     @property
     def experimental(self):
         return True
@@ -83,110 +122,121 @@ class WorldStateAgent(Agent):
         super().connect(scene)
         talemate.emit.async_signals.get("game_loop").connect(self.on_game_loop)
 
-    async def advance_time(self, duration:str, narrative:str=None):
+    async def advance_time(self, duration: str, narrative: str = None):
         """
         Emit a time passage message
         """
-        
+
         isodate.parse_duration(duration)
         human_duration = util.iso8601_duration_to_human(duration, suffix=" later")
         message = TimePassageMessage(ts=duration, message=human_duration)
-        
+
         log.debug("world_state.advance_time", message=message)
         self.scene.push_history(message)
         self.scene.emit_status()
-        
-        emit("time", message)
-        
-        await talemate.emit.async_signals.get("agent.world_state.time").send(
-            TimePassageEmission(agent=self, duration=duration, narrative=narrative, human_duration=human_duration)
-        )
-        
 
-    async def on_game_loop(self, emission:GameLoopEvent):
+        emit("time", message)
+
+        await talemate.emit.async_signals.get("agent.world_state.time").send(
+            TimePassageEmission(
+                agent=self,
+                duration=duration,
+                narrative=narrative,
+                human_duration=human_duration,
+            )
+        )
+
+    async def on_game_loop(self, emission: GameLoopEvent):
         """
         Called when a conversation is generated
         """
-        
+
         if not self.enabled:
             return
-        
+
         await self.update_world_state()
         await self.auto_update_reinforcments()
         await self.auto_check_pin_conditions()
-            
-            
+
     async def auto_update_reinforcments(self):
         if not self.enabled:
             return
-        
+
         if not self.actions["update_reinforcements"].enabled:
             return
-        
+
         await self.update_reinforcements()
-        
+
     async def auto_check_pin_conditions(self):
-        
         if not self.enabled:
             return
-        
+
         if not self.actions["check_pin_conditions"].enabled:
             return
-        
-        if self.next_pin_check % self.actions["check_pin_conditions"].config["turns"].value != 0 or self.next_pin_check == 0:
-            
+
+        if (
+            self.next_pin_check
+            % self.actions["check_pin_conditions"].config["turns"].value
+            != 0
+            or self.next_pin_check == 0
+        ):
             self.next_pin_check += 1
             return
-        
-        self.next_pin_check = 0
-        
-        await self.check_pin_conditions()
-        
 
-    async def update_world_state(self):
+        self.next_pin_check = 0
+
+        await self.check_pin_conditions()
+
+    async def update_world_state(self, force: bool = False):
         if not self.enabled:
             return
-        
+
         if not self.actions["update_world_state"].enabled:
             return
-        
-        log.debug("update_world_state", next_update=self.next_update, turns=self.actions["update_world_state"].config["turns"].value)
-        
+
+        log.debug(
+            "update_world_state",
+            next_update=self.next_update,
+            turns=self.actions["update_world_state"].config["turns"].value,
+        )
+
         scene = self.scene
-        
-        if self.next_update % self.actions["update_world_state"].config["turns"].value != 0 or self.next_update == 0:
+
+        if (
+            self.next_update % self.actions["update_world_state"].config["turns"].value
+            != 0
+            or self.next_update == 0
+        ) and not force:
             self.next_update += 1
             return
-        
+
         self.next_update = 0
         await scene.world_state.request_update()
-        
 
     @set_processing
     async def request_world_state(self):
-
         t1 = time.time()
 
         _, world_state = await Prompt.request(
             "world_state.request-world-state-v2",
             self.client,
             "analyze_long",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "object_type": "character",
                 "object_type_plural": "characters",
-            }
+            },
         )
-        
-        self.scene.log.debug("request_world_state", response=world_state, time=time.time() - t1)
-        
+
+        self.scene.log.debug(
+            "request_world_state", response=world_state, time=time.time() - t1
+        )
+
         return world_state
-    
 
     @set_processing
     async def request_world_state_inline(self):
-        
         """
         EXPERIMENTAL, Overall the one shot request seems about as coherent as the inline request, but the inline request is is about twice as slow and would need to run on every dialogue line.
         """
@@ -199,14 +249,18 @@ class WorldStateAgent(Agent):
             "world_state.request-world-state-inline-items",
             self.client,
             "analyze_long",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
-            }
+            },
         )
-        
-        self.scene.log.debug("request_world_state_inline", marked_items=marked_items_response, time=time.time() - t1)
-        
+
+        self.scene.log.debug(
+            "request_world_state_inline",
+            marked_items=marked_items_response,
+            time=time.time() - t1,
+        )
+
         return marked_items_response
 
     @set_processing
@@ -214,99 +268,111 @@ class WorldStateAgent(Agent):
         self,
         text: str,
     ):
-        
         response = await Prompt.request(
             "world_state.analyze-time-passage",
             self.client,
             "analyze_freeform_short",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
-            }
+            },
         )
-        
+
         duration = response.split("\n")[0].split(" ")[0].strip()
-        
+
         if not duration.startswith("P"):
-            duration = "P"+duration
-        
+            duration = "P" + duration
+
         return duration
-    
-    
+
     @set_processing
     async def analyze_text_and_extract_context(
         self,
         text: str,
         goal: str,
     ):
-        
         response = await Prompt.request(
             "world_state.analyze-text-and-extract-context",
             self.client,
             "analyze_freeform",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
                 "goal": goal,
-            }
+            },
         )
-        
-        log.debug("analyze_text_and_extract_context", goal=goal, text=text, response=response)
-        
+
+        log.debug(
+            "analyze_text_and_extract_context", goal=goal, text=text, response=response
+        )
+
         return response
-    
+
     @set_processing
     async def analyze_text_and_extract_context_via_queries(
         self,
         text: str,
         goal: str,
     ) -> list[str]:
-        
         response = await Prompt.request(
             "world_state.analyze-text-and-generate-rag-queries",
             self.client,
             "analyze_freeform",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
                 "goal": goal,
-            }
+            },
         )
-        
+
         queries = response.split("\n")
-        
+
         memory_agent = get_agent("memory")
-        
+
         context = await memory_agent.multi_query(queries, iterate=3)
-        
-        log.debug("analyze_text_and_extract_context_via_queries", goal=goal, text=text, queries=queries, context=context)
-        
+
+        log.debug(
+            "analyze_text_and_extract_context_via_queries",
+            goal=goal,
+            text=text,
+            queries=queries,
+            context=context,
+        )
+
         return context
-    
+
     @set_processing
     async def analyze_and_follow_instruction(
         self,
         text: str,
         instruction: str,
+        short: bool = False,
     ):
-        
+
+        kind = "analyze_freeform_short" if short else "analyze_freeform"
+
         response = await Prompt.request(
             "world_state.analyze-text-and-follow-instruction",
             self.client,
-            "analyze_freeform",
-            vars = {
+            kind,
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
                 "instruction": instruction,
-            }
+            },
         )
-        
-        log.debug("analyze_and_follow_instruction", instruction=instruction, text=text, response=response)
-        
+
+        log.debug(
+            "analyze_and_follow_instruction",
+            instruction=instruction,
+            text=text,
+            response=response,
+        )
+
         return response
 
     @set_processing
@@ -314,51 +380,55 @@ class WorldStateAgent(Agent):
         self,
         text: str,
         query: str,
+        short: bool = False,
     ):
-        
+        kind = "analyze_freeform_short" if short else "analyze_freeform"
         response = await Prompt.request(
             "world_state.analyze-text-and-answer-question",
             self.client,
-            "analyze_freeform",
-            vars = {
+            kind,
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
                 "query": query,
-            }
+            },
         )
-        
-        log.debug("analyze_text_and_answer_question", query=query, text=text, response=response)
-        
+
+        log.debug(
+            "analyze_text_and_answer_question",
+            query=query,
+            text=text,
+            response=response,
+        )
+
         return response
-    
+
     @set_processing
     async def identify_characters(
         self,
         text: str = None,
     ):
-        
         """
         Attempts to identify characters in the given text.
         """
-        
+
         _, data = await Prompt.request(
             "world_state.identify-characters",
             self.client,
             "analyze",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
-            }
+            },
         )
-        
+
         log.debug("identify_characters", text=text, data=data)
-        
+
         return data
-    
+
     def _parse_character_sheet(self, response):
-        
         data = {}
         for line in response.split("\n"):
             if not line.strip():
@@ -367,124 +437,148 @@ class WorldStateAgent(Agent):
                 break
             name, value = line.split(":", 1)
             data[name.strip()] = value.strip()
-        
+
         return data
-    
+
     @set_processing
     async def extract_character_sheet(
         self,
-        name:str,
-        text:str = None,
+        name: str,
+        text: str = None,
+        alteration_instructions: str = None,
     ):
-        
         """
         Attempts to extract a character sheet from the given text.
         """
-        
+
         response = await Prompt.request(
             "world_state.extract-character-sheet",
             self.client,
             "create",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "text": text,
                 "name": name,
-            }
+                "character": self.scene.get_character(name),
+                "alteration_instructions": alteration_instructions or "",
+            },
         )
-        
+
         # loop through each line in response and if it contains a : then extract
         # the left side as an attribute name and the right side as the value
         #
         # break as soon as a non-empty line is found that doesn't contain a :
-        
+
         return self._parse_character_sheet(response)
-        
-    
+
     @set_processing
-    async def match_character_names(self, names:list[str]):
-        
+    async def match_character_names(self, names: list[str]):
         """
         Attempts to match character names.
         """
-        
+
         _, response = await Prompt.request(
             "world_state.match-character-names",
             self.client,
             "analyze_long",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "names": names,
-            }
+            },
         )
-        
+
         log.debug("match_character_names", names=names, response=response)
-        
+
         return response
-    
-    
+
     @set_processing
-    async def update_reinforcements(self, force:bool=False):
-        
+    async def update_reinforcements(self, force: bool = False):
         """
         Queries due worldstate re-inforcements
         """
-        
+
         for reinforcement in self.scene.world_state.reinforce:
             if reinforcement.due <= 0 or force:
-                await self.update_reinforcement(reinforcement.question, reinforcement.character)
+                await self.update_reinforcement(
+                    reinforcement.question, reinforcement.character
+                )
             else:
                 reinforcement.due -= 1
-                
-    
+
     @set_processing
-    async def update_reinforcement(self, question:str, character:str=None):
-        
+    async def update_reinforcement(
+        self, question: str, character: str = None, reset: bool = False
+    ):
         """
         Queries a single re-inforcement
         """
         message = None
-        idx, reinforcement = await self.scene.world_state.find_reinforcement(question, character)
-        
+        idx, reinforcement = await self.scene.world_state.find_reinforcement(
+            question, character
+        )
+
         if not reinforcement:
             return
-        
+
+        source = f"{reinforcement.question}:{reinforcement.character if reinforcement.character else ''}"
+
+        if reset and reinforcement.insert == "sequential":
+            self.scene.pop_history(typ="reinforcement", source=source, all=True)
+
+        if reinforcement.insert == "sequential":
+            kind = "analyze_freeform_medium_short"
+        else:
+            kind = "analyze_freeform"
+
         answer = await Prompt.request(
             "world_state.update-reinforcements",
             self.client,
-            "analyze_freeform",
-            vars = {
+            kind,
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "question": reinforcement.question,
                 "instructions": reinforcement.instructions or "",
-                "character": self.scene.get_character(reinforcement.character) if reinforcement.character else None,
-                "answer": reinforcement.answer or "",
+                "character": (
+                    self.scene.get_character(reinforcement.character)
+                    if reinforcement.character
+                    else None
+                ),
+                "answer": (reinforcement.answer if not reset else None) or "",
                 "reinforcement": reinforcement,
-            }
+            },
         )
-        
+
+        # sequential reinforcment should be single sentence so we
+        # split on line breaks and take the first line in case the
+        # LLM did not understand the request and returned a longer response
+
+        if reinforcement.insert == "sequential":
+            answer = answer.split("\n")[0]
+
         reinforcement.answer = answer
         reinforcement.due = reinforcement.interval
-        
-        source = f"{reinforcement.question}:{reinforcement.character if reinforcement.character else ''}"
-        
+
         # remove any recent previous reinforcement message with same question
         # to avoid overloading the near history with reinforcement messages
-        self.scene.pop_history(typ="reinforcement", source=source, max_iterations=10)
-        
+        if not reset:
+            self.scene.pop_history(
+                typ="reinforcement", source=source, max_iterations=10
+            )
+
         if reinforcement.insert == "sequential":
             # insert the reinforcement message at the current position
             message = ReinforcementMessage(message=answer, source=source)
-            log.debug("update_reinforcement", message=message)
+            log.debug("update_reinforcement", message=message, reset=reset)
             self.scene.push_history(message)
-        
+
         # if reinforcement has a character name set, update the character detail
         if reinforcement.character:
             character = self.scene.get_character(reinforcement.character)
             await character.set_detail(reinforcement.question, answer)
-            
+
         else:
             # set world entry
             await self.scene.world_state_manager.save_world_entry(
@@ -492,20 +586,19 @@ class WorldStateAgent(Agent):
                 reinforcement.as_context_line,
                 {},
             )
-            
+
         self.scene.world_state.emit()
-        
-        return message  
-    
+
+        return message
+
     @set_processing
     async def check_pin_conditions(
         self,
     ):
-        
         """
         Checks if any context pin conditions
         """
-        
+
         pins_with_condition = {
             entry_id: {
                 "condition": pin.condition,
@@ -514,41 +607,47 @@ class WorldStateAgent(Agent):
             for entry_id, pin in self.scene.world_state.pins.items()
             if pin.condition
         }
-        
+
         if not pins_with_condition:
             return
-        
+
         first_entry_id = list(pins_with_condition.keys())[0]
-        
+
         _, answers = await Prompt.request(
             "world_state.check-pin-conditions",
             self.client,
             "analyze",
-            vars = {
+            vars={
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
-                "previous_states": json.dumps(pins_with_condition,indent=2),
-                "coercion": {first_entry_id:{ "condition": "" }},
-            }
+                "previous_states": json.dumps(pins_with_condition, indent=2),
+                "coercion": {first_entry_id: {"condition": ""}},
+            },
         )
-        
+
         world_state = self.scene.world_state
-        state_change = False  
-        
+        state_change = False
+
         for entry_id, answer in answers.items():
-            
             if entry_id not in world_state.pins:
-                log.warning("check_pin_conditions", entry_id=entry_id, answer=answer, msg="entry_id not found in world_state.pins (LLM failed to produce a clean response)")
+                log.warning(
+                    "check_pin_conditions",
+                    entry_id=entry_id,
+                    answer=answer,
+                    msg="entry_id not found in world_state.pins (LLM failed to produce a clean response)",
+                )
                 continue
-            
+
             log.info("check_pin_conditions", entry_id=entry_id, answer=answer)
             state = answer.get("state")
-            if state is True or (isinstance(state, str) and state.lower() in ["true", "yes", "y"]):
+            if state is True or (
+                isinstance(state, str) and state.lower() in ["true", "yes", "y"]
+            ):
                 prev_state = world_state.pins[entry_id].condition_state
-                
+
                 world_state.pins[entry_id].condition_state = True
                 world_state.pins[entry_id].active = True
-                
+
                 if prev_state != world_state.pins[entry_id].condition_state:
                     state_change = True
             else:
@@ -556,42 +655,50 @@ class WorldStateAgent(Agent):
                     world_state.pins[entry_id].condition_state = False
                     world_state.pins[entry_id].active = False
                     state_change = True
-                
+
         if state_change:
             await self.scene.load_active_pins()
             self.scene.emit_status()
-            
+
     @set_processing
-    async def summarize_and_pin(self, message_id:int, num_messages:int=3) -> str:
-        
+    async def summarize_and_pin(self, message_id: int, num_messages: int = 3) -> str:
         """
         Will take a message index and then walk back N messages
         summarizing the scene and pinning it to the context.
         """
-        
+
         creator = get_agent("creator")
         summarizer = get_agent("summarizer")
-        
+
         message_index = self.scene.message_index(message_id)
-        
+
         text = self.scene.snapshot(lines=num_messages, start=message_index)
-        
-        summary = await summarizer.summarize(text, method="short")
-        
+
+        extra_context = self.scene.snapshot(
+            lines=50, start=message_index - num_messages
+        )
+
+        summary = await summarizer.summarize(
+            text,
+            extra_context=extra_context,
+            method="short",
+            extra_instructions="Pay particularly close attention to decisions, agreements or promises made.",
+        )
+
         entry_id = util.clean_id(await creator.generate_title(summary))
-        
+
         ts = self.scene.ts
-        
+
         log.debug(
             "summarize_and_pin",
             message_id=message_id,
             message_index=message_index,
-            num_messages=num_messages, 
+            num_messages=num_messages,
             summary=summary,
             entry_id=entry_id,
             ts=ts,
         )
-        
+
         await self.scene.world_state_manager.save_world_entry(
             entry_id,
             summary,
@@ -599,11 +706,80 @@ class WorldStateAgent(Agent):
                 "ts": ts,
             },
         )
-        
+
         await self.scene.world_state_manager.set_pin(
             entry_id,
             active=True,
         )
-        
+
         await self.scene.load_active_pins()
-        self.scene.emit_status()
+        self.scene.emit_status()
+
+    @set_processing
+    async def is_character_present(self, character: str) -> bool:
+        """
+        Check if a character is present in the scene
+
+        Arguments:
+
+        - `character`: The character to check.
+        """
+
+        if len(self.scene.history) < 10:
+            text = self.scene.intro + "\n\n" + self.scene.snapshot(lines=50)
+        else:
+            text = self.scene.snapshot(lines=50)
+
+        is_present = await self.analyze_text_and_answer_question(
+            text=text,
+            query=f"Is {character} present AND active in the current scene? Answert with 'yes' or 'no'.",
+        )
+
+        return is_present.lower().startswith("y")
+
+    @set_processing
+    async def is_character_leaving(self, character: str) -> bool:
+        """
+        Check if a character is leaving the scene
+
+        Arguments:
+
+        - `character`: The character to check.
+        """
+
+        if len(self.scene.history) < 10:
+            text = self.scene.intro + "\n\n" + self.scene.snapshot(lines=50)
+        else:
+            text = self.scene.snapshot(lines=50)
+
+        is_leaving = await self.analyze_text_and_answer_question(
+            text=text,
+            query=f"Is {character} leaving the current scene? Answert with 'yes' or 'no'.",
+        )
+
+        return is_leaving.lower().startswith("y")
+
+    @set_processing
+    async def manager(self, action_name: str, *args, **kwargs):
+        """
+        Executes a world state manager action through self.scene.world_state_manager
+        """
+
+        manager = self.scene.world_state_manager
+
+        try:
+            fn = getattr(manager, action_name, None)
+
+            if not fn:
+                raise ValueError(f"Unknown action: {action_name}")
+
+            return await fn(*args, **kwargs)
+        except Exception as e:
+            log.error(
+                "worldstate.manager",
+                action_name=action_name,
+                args=args,
+                kwargs=kwargs,
+                error=e,
+            )
+            raise

src/talemate/automated_action.py

@@ -1,10 +1,11 @@
 from __future__ import annotations
-from typing import TYPE_CHECKING, Any
+
 import dataclasses
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     from talemate import Scene
-    
+
 import structlog
 
 __all__ = ["AutomatedAction", "register", "initialize_for_scene"]
@@ -13,50 +14,64 @@ log = structlog.get_logger("talemate.automated_action")
 
 AUTOMATED_ACTIONS = {}
 
-def initialize_for_scene(scene:Scene):
-    
+
+def initialize_for_scene(scene: Scene):
     for uid, config in AUTOMATED_ACTIONS.items():
         scene.automated_actions[uid] = config.cls(
             scene,
             uid=uid,
             frequency=config.frequency,
             call_initially=config.call_initially,
-            enabled=config.enabled
+            enabled=config.enabled,
         )
 
 
 @dataclasses.dataclass
 class AutomatedActionConfig:
-    uid:str
-    cls:AutomatedAction
-    frequency:int=5
-    call_initially:bool=False
-    enabled:bool=True
-    
+    uid: str
+    cls: AutomatedAction
+    frequency: int = 5
+    call_initially: bool = False
+    enabled: bool = True
+
+
 class register:
-    
-    def __init__(self, uid:str, frequency:int=5, call_initially:bool=False, enabled:bool=True):
+    def __init__(
+        self,
+        uid: str,
+        frequency: int = 5,
+        call_initially: bool = False,
+        enabled: bool = True,
+    ):
         self.uid = uid
         self.frequency = frequency
         self.call_initially = call_initially
         self.enabled = enabled
-        
-    def __call__(self, action:AutomatedAction):
+
+    def __call__(self, action: AutomatedAction):
         AUTOMATED_ACTIONS[self.uid] = AutomatedActionConfig(
-            self.uid, 
-            action, 
-            frequency=self.frequency, 
-            call_initially=self.call_initially, 
-            enabled=self.enabled
+            self.uid,
+            action,
+            frequency=self.frequency,
+            call_initially=self.call_initially,
+            enabled=self.enabled,
         )
         return action
-    
+
+
 class AutomatedAction:
     """
     An action that will be executed every n turns
     """
-    
-    def __init__(self, scene:Scene, frequency:int=5, call_initially:bool=False, uid:str=None, enabled:bool=True):
+
+    def __init__(
+        self,
+        scene: Scene,
+        frequency: int = 5,
+        call_initially: bool = False,
+        uid: str = None,
+        enabled: bool = True,
+    ):
         self.scene = scene
         self.enabled = enabled
         self.frequency = frequency
@@ -64,14 +79,19 @@ class AutomatedAction:
         self.uid = uid
         if call_initially:
             self.turns = frequency
-        
+
     async def __call__(self):
-        
-        log.debug("automated_action", uid=self.uid, enabled=self.enabled, frequency=self.frequency, turns=self.turns)
-        
+        log.debug(
+            "automated_action",
+            uid=self.uid,
+            enabled=self.enabled,
+            frequency=self.frequency,
+            turns=self.turns,
+        )
+
         if not self.enabled:
             return False
-        
+
         if self.turns % self.frequency == 0:
             result = await self.action()
             log.debug("automated_action", result=result)
@@ -79,10 +99,9 @@ class AutomatedAction:
                 # action could not be performed at this turn, we will try again next turn
                 return False
         self.turns += 1
-    
-    
+
     async def action(self) -> Any:
         """
         Override this method to implement your action.
         """
-        raise NotImplementedError()
+        raise NotImplementedError()

src/talemate/character.py

@@ -0,0 +1,59 @@
+from typing import TYPE_CHECKING, Union
+
+from talemate.instance import get_agent
+
+if TYPE_CHECKING:
+    from talemate.tale_mate import Actor, Character, Scene
+
+
+__all__ = [
+    "deactivate_character",
+    "activate_character",
+]
+
+
+async def deactivate_character(scene: "Scene", character: Union[str, "Character"]):
+    """
+    Deactivates a character
+
+    Arguments:
+
+    - `scene`: The scene to deactivate the character from
+    - `character`: The character to deactivate. Can be a string (the character's name) or a Character object
+    """
+
+    if isinstance(character, str):
+        character = scene.get_character(character)
+
+    if character.is_player:
+        # can't deactivate the player
+        return False
+
+    if character.name in scene.inactive_characters:
+        # already deactivated
+        return False
+
+    await scene.remove_actor(character.actor)
+    scene.inactive_characters[character.name] = character
+
+
+async def activate_character(scene: "Scene", character: Union[str, "Character"]):
+    """
+    Activates a character
+
+    Arguments:
+
+    - `scene`: The scene to activate the character in
+    - `character`: The character to activate. Can be a string (the character's name) or a Character object
+    """
+
+    if isinstance(character, str):
+        character = scene.get_character(character)
+
+    if character.name not in scene.inactive_characters:
+        # already activated
+        return False
+
+    actor = scene.Actor(character, get_agent("conversation"))
+    await scene.add_actor(actor)
+    del scene.inactive_characters[character.name]

src/talemate/cli.py

@@ -2,15 +2,13 @@ import argparse
 import asyncio
 import glob
 import os
-import structlog
 
+import structlog
 from dotenv import load_dotenv
 
 import talemate.instance as instance
 from talemate import Actor, Character, Helper, Player, Scene
-from talemate.agents import (
-    ConversationAgent,
-)
+from talemate.agents import ConversationAgent
 from talemate.client import OpenAIClient, TextGeneratorWebuiClient
 from talemate.emit.console import Console
 from talemate.load import (
@@ -129,7 +127,6 @@ async def run_console_session(parser, args):
     default_client = None
 
     if "textgenwebui" in clients.values() or args.client == "textgenwebui":
-
         # Init the TextGeneratorWebuiClient with ConversationAgent and create an actor
         textgenwebui_api_url = args.textgenwebui_url
 
@@ -145,7 +142,6 @@ async def run_console_session(parser, args):
                 clients[client_name] = text_generator_webui_client
 
     if "openai" in clients.values() or args.client == "openai":
-
         openai_client = OpenAIClient()
 
         for client_name, client_typ in clients.items():

src/talemate/client/__init__.py

@@ -1,6 +1,8 @@
 import os
+
+import talemate.client.runpod
+from talemate.client.lmstudio import LMStudioClient
 from talemate.client.openai import OpenAIClient
+from talemate.client.openai_compat import OpenAICompatibleClient
 from talemate.client.registry import CLIENT_CLASSES, get_client_class, register
 from talemate.client.textgenwebui import TextGeneratorWebuiClient
-from talemate.client.lmstudio import LMStudioClient
-import talemate.client.runpod

src/talemate/client/base.py

@@ -1,26 +1,29 @@
 """
 A unified client base, based on the openai API
 """
-import copy
+
+import logging
 import random
 import time
-from typing import Callable
+from typing import Callable, Union
 
+import pydantic
 import structlog
-import logging
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, PermissionDeniedError
 
-from talemate.emit import emit
-import talemate.instance as instance
 import talemate.client.presets as presets
 import talemate.client.system_prompts as system_prompts
+import talemate.instance as instance
 import talemate.util as util
+from talemate.agents.context import active_agent
 from talemate.client.context import client_context_attribute
 from talemate.client.model_prompts import model_prompt
-from talemate.agents.context import active_agent
+from talemate.emit import emit
 
 # Set up logging level for httpx to WARNING to suppress debug logs.
-logging.getLogger('httpx').setLevel(logging.WARNING)
+logging.getLogger("httpx").setLevel(logging.WARNING)
+
+log = structlog.get_logger("client.base")
 
 REMOTE_SERVICES = [
     # TODO: runpod.py should add this to the list
@@ -29,11 +32,45 @@ REMOTE_SERVICES = [
 
 STOPPING_STRINGS = ["<|im_end|>", "</s>"]
 
+
+class PromptData(pydantic.BaseModel):
+    kind: str
+    prompt: str
+    response: str
+    prompt_tokens: int
+    response_tokens: int
+    client_name: str
+    client_type: str
+    time: Union[float, int]
+    agent_stack: list[str] = pydantic.Field(default_factory=list)
+    generation_parameters: dict = pydantic.Field(default_factory=dict)
+
+
+class ErrorAction(pydantic.BaseModel):
+    title: str
+    action_name: str
+    icon: str = "mdi-error"
+    arguments: list = []
+
+
+class Defaults(pydantic.BaseModel):
+    api_url: str = "http://localhost:5000"
+    max_token_length: int = 4096
+
+
+class ExtraField(pydantic.BaseModel):
+    name: str
+    type: str
+    label: str
+    required: bool
+    description: str
+
+
 class ClientBase:
-    
     api_url: str
     model_name: str
-    name:str = None
+    api_key: str = None
+    name: str = None
     enabled: bool = True
     current_status: str = None
     max_token_length: int = 4096
@@ -41,138 +78,170 @@ class ClientBase:
     connected: bool = False
     conversation_retries: int = 2
     auto_break_repetition_enabled: bool = True
-
+    decensor_enabled: bool = True
     client_type = "base"
 
-    
+    class Meta(pydantic.BaseModel):
+        experimental: Union[None, str] = None
+        defaults: Defaults = Defaults()
+        title: str = "Client"
+        name_prefix: str = "Client"
+        enable_api_auth: bool = False
+        requires_prompt_template: bool = True
+
     def __init__(
         self,
         api_url: str = None,
-        name = None,
+        name=None,
         **kwargs,
     ):
         self.api_url = api_url
         self.name = name or self.client_type
         self.log = structlog.get_logger(f"client.{self.client_type}")
         if "max_token_length" in kwargs:
-            self.max_token_length = kwargs["max_token_length"]
+            self.max_token_length = (
+                int(kwargs["max_token_length"]) if kwargs["max_token_length"] else 4096
+            )
         self.set_client(max_token_length=self.max_token_length)
-        
+
     def __str__(self):
         return f"{self.client_type}Client[{self.api_url}][{self.model_name or ''}]"
-        
+
+    @property
+    def experimental(self):
+        return False
+
     def set_client(self, **kwargs):
         self.client = AsyncOpenAI(base_url=self.api_url, api_key="sk-1111")
 
     def prompt_template(self, sys_msg, prompt):
-        
         """
         Applies the appropriate prompt template for the model.
         """
-        
+
         if not self.model_name:
             self.log.warning("prompt template not applied", reason="no model loaded")
             return f"{sys_msg}\n{prompt}"
-        
-        return model_prompt(self.model_name, sys_msg, prompt)   
-    
-    def has_prompt_template(self):
-        if not self.model_name:
-            return False
-        
-        return model_prompt.exists(self.model_name)
-    
+
+        return model_prompt(self.model_name, sys_msg, prompt)[0]
+
     def prompt_template_example(self):
-        if not self.model_name:
-            return None
+        if not getattr(self, "model_name", None):
+            return None, None
         return model_prompt(self.model_name, "sysmsg", "prompt<|BOT|>{LLM coercion}")
-    
+
     def reconfigure(self, **kwargs):
-        
         """
         Reconfigures the client.
-        
+
         Keyword Arguments:
-        
+
         - api_url: the API URL to use
         - max_token_length: the max token length to use
         - enabled: whether the client is enabled
         """
-        
+
         if "api_url" in kwargs:
             self.api_url = kwargs["api_url"]
 
-        if "max_token_length" in kwargs:
-            self.max_token_length = kwargs["max_token_length"]
-            
+        if kwargs.get("max_token_length"):
+            self.max_token_length = int(kwargs["max_token_length"])
+
         if "enabled" in kwargs:
             self.enabled = bool(kwargs["enabled"])
 
-
     def toggle_disabled_if_remote(self):
-        
         """
         If the client is targeting a remote recognized service, this
         will disable the client.
         """
-        
+
         for service in REMOTE_SERVICES:
             if service in self.api_url:
                 if self.enabled:
-                    self.log.warn("remote service unreachable, disabling client", client=self.name)
+                    self.log.warn(
+                        "remote service unreachable, disabling client", client=self.name
+                    )
                 self.enabled = False
-                
+
                 return True
-            
+
         return False
-            
 
     def get_system_message(self, kind: str) -> str:
-        
         """
         Returns the appropriate system message for the given kind of generation
-        
+
         Arguments:
-        
+
         - kind: the kind of generation
         """
-        
-        # TODO: make extensible
-       
-        if "narrate" in kind:
-            return system_prompts.NARRATOR
-        if "story" in kind:
-            return system_prompts.NARRATOR
-        if "director" in kind:
-            return system_prompts.DIRECTOR
-        if "create" in kind:
-            return system_prompts.CREATOR
-        if "roleplay" in kind:
-            return system_prompts.ROLEPLAY
-        if "conversation" in kind:
-            return system_prompts.ROLEPLAY
-        if "editor" in kind:
-            return system_prompts.EDITOR
-        if "world_state" in kind:
-            return system_prompts.WORLD_STATE
-        if "analyze_freeform" in kind:
-            return system_prompts.ANALYST_FREEFORM
-        if "analyst" in kind:
-            return system_prompts.ANALYST
-        if "analyze" in kind:
-            return system_prompts.ANALYST
-        if "summarize" in kind:
-            return system_prompts.SUMMARIZE
-       
+
+        if self.decensor_enabled:
+
+            if "narrate" in kind:
+                return system_prompts.NARRATOR
+            if "story" in kind:
+                return system_prompts.NARRATOR
+            if "director" in kind:
+                return system_prompts.DIRECTOR
+            if "create" in kind:
+                return system_prompts.CREATOR
+            if "roleplay" in kind:
+                return system_prompts.ROLEPLAY
+            if "conversation" in kind:
+                return system_prompts.ROLEPLAY
+            if "editor" in kind:
+                return system_prompts.EDITOR
+            if "world_state" in kind:
+                return system_prompts.WORLD_STATE
+            if "analyze_freeform" in kind:
+                return system_prompts.ANALYST_FREEFORM
+            if "analyst" in kind:
+                return system_prompts.ANALYST
+            if "analyze" in kind:
+                return system_prompts.ANALYST
+            if "summarize" in kind:
+                return system_prompts.SUMMARIZE
+            if "visualize" in kind:
+                return system_prompts.VISUALIZE
+
+        else:
+
+            if "narrate" in kind:
+                return system_prompts.NARRATOR_NO_DECENSOR
+            if "story" in kind:
+                return system_prompts.NARRATOR_NO_DECENSOR
+            if "director" in kind:
+                return system_prompts.DIRECTOR_NO_DECENSOR
+            if "create" in kind:
+                return system_prompts.CREATOR_NO_DECENSOR
+            if "roleplay" in kind:
+                return system_prompts.ROLEPLAY_NO_DECENSOR
+            if "conversation" in kind:
+                return system_prompts.ROLEPLAY_NO_DECENSOR
+            if "editor" in kind:
+                return system_prompts.EDITOR_NO_DECENSOR
+            if "world_state" in kind:
+                return system_prompts.WORLD_STATE_NO_DECENSOR
+            if "analyze_freeform" in kind:
+                return system_prompts.ANALYST_FREEFORM_NO_DECENSOR
+            if "analyst" in kind:
+                return system_prompts.ANALYST_NO_DECENSOR
+            if "analyze" in kind:
+                return system_prompts.ANALYST_NO_DECENSOR
+            if "summarize" in kind:
+                return system_prompts.SUMMARIZE_NO_DECENSOR
+            if "visualize" in kind:
+                return system_prompts.VISUALIZE_NO_DECENSOR
+
         return system_prompts.BASIC
-    
-    
+
     def emit_status(self, processing: bool = None):
-        
         """
         Sets and emits the client status.
         """
-        
+
         if processing is not None:
             self.processing = processing
 
@@ -188,33 +257,45 @@ class ClientBase:
         else:
             model_name = "No model loaded"
             status = "warning"
-        
+
         status_change = status != self.current_status
         self.current_status = status
-        
+
+        prompt_template_example, prompt_template_file = self.prompt_template_example()
+
+        data = {
+            "api_key": self.api_key,
+            "prompt_template_example": prompt_template_example,
+            "has_prompt_template": (
+                prompt_template_file and prompt_template_file != "default.jinja2"
+            ),
+            "template_file": prompt_template_file,
+            "meta": self.Meta().model_dump(),
+            "error_action": None,
+        }
+
+        for field_name in getattr(self.Meta(), "extra_fields", {}).keys():
+            data[field_name] = getattr(self, field_name, None)
+
         emit(
             "client_status",
             message=self.client_type,
             id=self.name,
             details=model_name,
             status=status,
-            data={
-                "prompt_template_example": self.prompt_template_example(),
-                "has_prompt_template": self.has_prompt_template(),
-            }
+            data=data,
         )
 
         if status_change:
             instance.emit_agent_status_by_client(self)
-            
-            
+
     async def get_model_name(self):
         models = await self.client.models.list()
         try:
             return models.data[0].id
         except IndexError:
             return None
-            
+
     async def status(self):
         """
         Send a request to the API to retrieve the loaded AI model name.
@@ -223,12 +304,12 @@ class ClientBase:
         """
         if self.processing:
             return
-        
+
         if not self.enabled:
             self.connected = False
             self.emit_status()
             return
-        
+
         try:
             self.model_name = await self.get_model_name()
         except Exception as e:
@@ -238,143 +319,170 @@ class ClientBase:
             self.toggle_disabled_if_remote()
             self.emit_status()
             return
-        
+
         self.connected = True
-        
+
         if not self.model_name or self.model_name == "None":
             self.log.warning("client model not loaded", client=self)
             self.emit_status()
             return
-        
+
         self.emit_status()
-        
-    
-    def generate_prompt_parameters(self, kind:str):
+
+    def generate_prompt_parameters(self, kind: str):
         parameters = {}
         self.tune_prompt_parameters(
-            presets.configure(parameters, kind, self.max_token_length),
-            kind
+            presets.configure(parameters, kind, self.max_token_length), kind
         )
         return parameters
-    
-    def tune_prompt_parameters(self, parameters:dict, kind:str):
+
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
         parameters["stream"] = False
-        if client_context_attribute("nuke_repetition") > 0.0 and self.jiggle_enabled_for(kind):
-            self.jiggle_randomness(parameters, offset=client_context_attribute("nuke_repetition"))
-            
+        if client_context_attribute(
+            "nuke_repetition"
+        ) > 0.0 and self.jiggle_enabled_for(kind):
+            self.jiggle_randomness(
+                parameters, offset=client_context_attribute("nuke_repetition")
+            )
+
         fn_tune_kind = getattr(self, f"tune_prompt_parameters_{kind}", None)
         if fn_tune_kind:
             fn_tune_kind(parameters)
 
         agent_context = active_agent.get()
         if agent_context.agent:
-            agent_context.agent.inject_prompt_paramters(parameters, kind, agent_context.action)
-        
-    def tune_prompt_parameters_conversation(self, parameters:dict):
+            agent_context.agent.inject_prompt_paramters(
+                parameters, kind, agent_context.action
+            )
+
+    def tune_prompt_parameters_conversation(self, parameters: dict):
         conversation_context = client_context_attribute("conversation")
         parameters["max_tokens"] = conversation_context.get("length", 96)
-        
+
         dialog_stopping_strings = [
             f"{character}:" for character in conversation_context["other_characters"]
         ]
-        
+
         if "extra_stopping_strings" in parameters:
             parameters["extra_stopping_strings"] += dialog_stopping_strings
         else:
             parameters["extra_stopping_strings"] = dialog_stopping_strings
-            
-            
-    async def generate(self, prompt:str, parameters:dict, kind:str):
-        
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
         """
-        
-        self.log.debug("generate", prompt=prompt[:128]+" ...", parameters=parameters)
-        
+
+        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
+
         try:
-            response = await self.client.completions.create(prompt=prompt.strip(" "), **parameters)
+            response = await self.client.completions.create(
+                prompt=prompt.strip(" "), **parameters
+            )
             return response.get("choices", [{}])[0].get("text", "")
+        except PermissionDeniedError as e:
+            self.log.error("generate error", e=e)
+            emit("status", message="Client API: Permission Denied", status="error")
+            return ""
         except Exception as e:
             self.log.error("generate error", e=e)
+            emit(
+                "status", message="Error during generation (check logs)", status="error"
+            )
             return ""
-        
+
     async def send_prompt(
-        self, prompt: str, kind: str = "conversation", finalize: Callable = lambda x: x, retries:int=2
+        self,
+        prompt: str,
+        kind: str = "conversation",
+        finalize: Callable = lambda x: x,
+        retries: int = 2,
     ) -> str:
         """
         Send a prompt to the AI and return its response.
         :param prompt: The text prompt to send.
         :return: The AI's response text.
         """
-        
+
         try:
             self.emit_status(processing=True)
             await self.status()
 
             prompt_param = self.generate_prompt_parameters(kind)
 
-            finalized_prompt = self.prompt_template(self.get_system_message(kind), prompt).strip(" ")
+            finalized_prompt = self.prompt_template(
+                self.get_system_message(kind), prompt
+            ).strip(" ")
             prompt_param = finalize(prompt_param)
 
             token_length = self.count_tokens(finalized_prompt)
 
-            
             time_start = time.time()
             extra_stopping_strings = prompt_param.pop("extra_stopping_strings", [])
 
-            self.log.debug("send_prompt", token_length=token_length, max_token_length=self.max_token_length, parameters=prompt_param)
-            response = await self.generate(
-                self.repetition_adjustment(finalized_prompt), 
-                prompt_param, 
-                kind
+            self.log.debug(
+                "send_prompt",
+                token_length=token_length,
+                max_token_length=self.max_token_length,
+                parameters=prompt_param,
             )
-            
-            response, finalized_prompt = await self.auto_break_repetition(finalized_prompt, prompt_param, response, kind, retries)
-            
+            response = await self.generate(
+                self.repetition_adjustment(finalized_prompt), prompt_param, kind
+            )
+
+            response, finalized_prompt = await self.auto_break_repetition(
+                finalized_prompt, prompt_param, response, kind, retries
+            )
+
             time_end = time.time()
-            
+
             # stopping strings sometimes get appended to the end of the response anyways
             # split the response by the first stopping string and take the first part
-            
-            
+
             for stopping_string in STOPPING_STRINGS + extra_stopping_strings:
                 if stopping_string in response:
                     response = response.split(stopping_string)[0]
                     break
-            
-            emit("prompt_sent", data={
-                "kind": kind,
-                "prompt": finalized_prompt,
-                "response": response,
-                "prompt_tokens": token_length,
-                "response_tokens": self.count_tokens(response),
-                "time": time_end - time_start,
-            })
-            
+
+            agent_context = active_agent.get()
+
+            emit(
+                "prompt_sent",
+                data=PromptData(
+                    kind=kind,
+                    prompt=finalized_prompt,
+                    response=response,
+                    prompt_tokens=token_length,
+                    response_tokens=self.count_tokens(response),
+                    agent_stack=agent_context.agent_stack if agent_context else [],
+                    client_name=self.name,
+                    client_type=self.client_type,
+                    time=time_end - time_start,
+                    generation_parameters=prompt_param,
+                ).model_dump(),
+            )
+
             return response
         finally:
             self.emit_status(processing=False)
-            
-            
+
     async def auto_break_repetition(
-        self, 
-        finalized_prompt:str, 
-        prompt_param:dict, 
-        response:str, 
-        kind:str, 
-        retries:int,
-        pad_max_tokens:int=32,
+        self,
+        finalized_prompt: str,
+        prompt_param: dict,
+        response: str,
+        kind: str,
+        retries: int,
+        pad_max_tokens: int = 32,
     ) -> str:
-        
         """
         If repetition breaking is enabled, this will retry the prompt if its
         response is too similar to other messages in the prompt
-        
+
         This requires the agent to have the allow_repetition_break method
         and the jiggle_enabled_for method and the client to have the
         auto_break_repetition_enabled attribute set to True
-        
+
         Arguments:
 
         - finalized_prompt: the prompt that was sent
@@ -383,47 +491,46 @@ class ClientBase:
         - kind: the kind of generation
         - retries: the number of retries left
         - pad_max_tokens: increase response max_tokens by this amount per iteration
-                
+
         Returns:
-        
+
         - the response
         """
-        
+
         if not self.auto_break_repetition_enabled:
             return response, finalized_prompt
-        
+
         agent_context = active_agent.get()
         if self.jiggle_enabled_for(kind, auto=True):
-            
             # check if the response is a repetition
             # using the default similarity threshold of 98, meaning it needs
             # to be really similar to be considered a repetition
-            
+
             is_repetition, similarity_score, matched_line = util.similarity_score(
-                response, 
-                finalized_prompt.split("\n"), 
-                similarity_threshold=80
+                response, finalized_prompt.split("\n"), similarity_threshold=80
             )
-            
+
             if not is_repetition:
-                
                 # not a repetition, return the response
-                
-                self.log.debug("send_prompt no similarity", similarity_score=similarity_score)
-                finalized_prompt = self.repetition_adjustment(finalized_prompt, is_repetitive=False)
-                return response, finalized_prompt
-            
-            while is_repetition and retries > 0:
-                
-                # it's a repetition, retry the prompt with adjusted parameters
-                
-                self.log.warn(
-                    "send_prompt similarity retry", 
-                    agent=agent_context.agent.agent_type, 
-                    similarity_score=similarity_score, 
-                    retries=retries
+
+                self.log.debug(
+                    "send_prompt no similarity", similarity_score=similarity_score
                 )
-                
+                finalized_prompt = self.repetition_adjustment(
+                    finalized_prompt, is_repetitive=False
+                )
+                return response, finalized_prompt
+
+            while is_repetition and retries > 0:
+                # it's a repetition, retry the prompt with adjusted parameters
+
+                self.log.warn(
+                    "send_prompt similarity retry",
+                    agent=agent_context.agent.agent_type,
+                    similarity_score=similarity_score,
+                    retries=retries,
+                )
+
                 # first we apply the client's randomness jiggle which will adjust
                 # parameters like temperature and repetition_penalty, depending
                 # on the client
@@ -431,90 +538,94 @@ class ClientBase:
                 # this is a cumulative adjustment, so it will add to the previous
                 # iteration's adjustment, this also means retries should be kept low
                 # otherwise it will get out of hand and start generating nonsense
-                
+
                 self.jiggle_randomness(prompt_param, offset=0.5)
-                
+
                 # then we pad the max_tokens by the pad_max_tokens amount
-                
+
                 prompt_param["max_tokens"] += pad_max_tokens
-                
+
                 # send the prompt again
                 # we use the repetition_adjustment method to further encourage
                 # the AI to break the repetition on its own as well.
-                
-                finalized_prompt = self.repetition_adjustment(finalized_prompt, is_repetitive=True)
-                
-                response = retried_response = await self.generate(
-                    finalized_prompt, 
-                    prompt_param, 
-                    kind
+
+                finalized_prompt = self.repetition_adjustment(
+                    finalized_prompt, is_repetitive=True
                 )
-                
-                self.log.debug("send_prompt dedupe sentences", response=response, matched_line=matched_line)
-                
+
+                response = retried_response = await self.generate(
+                    finalized_prompt, prompt_param, kind
+                )
+
+                self.log.debug(
+                    "send_prompt dedupe sentences",
+                    response=response,
+                    matched_line=matched_line,
+                )
+
                 # a lot of the times the response will now contain the repetition + something new
                 # so we dedupe the response to remove the repetition on sentences level
-                
-                response = util.dedupe_sentences(response, matched_line, similarity_threshold=85, debug=True)
-                self.log.debug("send_prompt dedupe sentences (after)", response=response)
-                
+
+                response = util.dedupe_sentences(
+                    response, matched_line, similarity_threshold=85, debug=True
+                )
+                self.log.debug(
+                    "send_prompt dedupe sentences (after)", response=response
+                )
+
                 # deduping may have removed the entire response, so we check for that
-                
+
                 if not util.strip_partial_sentences(response).strip():
-                    
                     # if the response is empty, we set the response to the original
                     # and try again next loop
-                    
+
                     response = retried_response
-                
+
                 # check if the response is a repetition again
-                
+
                 is_repetition, similarity_score, matched_line = util.similarity_score(
-                    response, 
-                    finalized_prompt.split("\n"), 
-                    similarity_threshold=80
+                    response, finalized_prompt.split("\n"), similarity_threshold=80
                 )
                 retries -= 1
-                
+
         return response, finalized_prompt
-        
-    def count_tokens(self, content:str):
+
+    def count_tokens(self, content: str):
         return util.count_tokens(content)
 
-    def jiggle_randomness(self, prompt_config:dict, offset:float=0.3) -> dict:
+    def jiggle_randomness(self, prompt_config: dict, offset: float = 0.3) -> dict:
         """
         adjusts temperature and repetition_penalty
         by random values using the base value as a center
         """
-        
+
         temp = prompt_config["temperature"]
         min_offset = offset * 0.3
         prompt_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
-        
-    def jiggle_enabled_for(self, kind:str, auto:bool=False) -> bool:
-        
+
+    def jiggle_enabled_for(self, kind: str, auto: bool = False) -> bool:
         agent_context = active_agent.get()
         agent = agent_context.agent
-        
+
         if not agent:
             return False
-        
+
         return agent.allow_repetition_break(kind, agent_context.action, auto=auto)
-    
-    def repetition_adjustment(self, prompt:str, is_repetitive:bool=False):
+
+    def repetition_adjustment(self, prompt: str, is_repetitive: bool = False):
         """
         Breaks the prompt into lines and checkse each line for a match with
         [$REPETITION|{repetition_adjustment}].
-        
+
         On match and if is_repetitive is True, the line is removed from the prompt and
         replaced with the repetition_adjustment.
-        
-        On match and if is_repetitive is False, the line is removed from the prompt.        
+
+        On match and if is_repetitive is False, the line is removed from the prompt.
         """
-        
+
         lines = prompt.split("\n")
         new_lines = []
-        
+
         for line in lines:
             if line.startswith("[$REPETITION|"):
                 if is_repetitive:
@@ -523,5 +634,5 @@ class ClientBase:
                     new_lines.append("")
             else:
                 new_lines.append(line)
-        
-        return "\n".join(new_lines)
+
+        return "\n".join(new_lines)

src/talemate/client/bootstrap.py

@@ -1,6 +1,7 @@
-import pydantic
 from enum import Enum
 
+import pydantic
+
 __all__ = [
     "ClientType",
     "ClientBootstrap",
@@ -10,8 +11,10 @@ __all__ = [
 
 LISTS = {}
 
+
 class ClientType(str, Enum):
     """Client type enum."""
+
     textgen = "textgenwebui"
     automatic1111 = "automatic1111"
 
@@ -20,43 +23,42 @@ class ClientBootstrap(pydantic.BaseModel):
     """Client bootstrap model."""
 
     # client type, currently supports "textgen" and "automatic1111"
-    
+
     client_type: ClientType
-    
+
     # unique client identifier
-    
+
     uid: str
-    
+
     # connection name
-    
+
     name: str
-    
+
     # connection information for the client
     # REST api url
-    
+
     api_url: str
-    
+
     # service name (for example runpod)
-    
+
     service_name: str
-    
+
 
 class register_list:
-    
-    def __init__(self, service_name:str):
+    def __init__(self, service_name: str):
         self.service_name = service_name
-        
+
     def __call__(self, func):
         LISTS[self.service_name] = func
         return func
-    
+
 
 async def list_all(exclude_urls: list[str] = list()):
     """
     Return a list of client bootstrap objects.
     """
-    
+
     for service_name, func in LISTS.items():
         async for item in func():
             if item.api_url not in exclude_urls:
-                yield item.dict()
+                yield item.dict()

src/talemate/client/context.py

@@ -3,19 +3,20 @@ Context managers for various client-side operations.
 """
 
 from contextvars import ContextVar
-from pydantic import BaseModel, Field
 from copy import deepcopy
 
 import structlog
+from pydantic import BaseModel, Field
 
 __all__ = [
-    'context_data',
-    'client_context_attribute',
-    'ContextModel',
+    "context_data",
+    "client_context_attribute",
+    "ContextModel",
 ]
 
 log = structlog.get_logger()
 
+
 def model_to_dict_without_defaults(model_instance):
     model_dict = model_instance.dict()
     for field_name, field in model_instance.__class__.__fields__.items():
@@ -23,20 +24,25 @@ def model_to_dict_without_defaults(model_instance):
             del model_dict[field_name]
     return model_dict
 
+
 class ConversationContext(BaseModel):
     talking_character: str = None
     other_characters: list[str] = Field(default_factory=list)
 
+
 class ContextModel(BaseModel):
     """
     Pydantic model for the context data.
     """
+
     nuke_repetition: float = Field(0.0, ge=0.0, le=3.0)
     conversation: ConversationContext = Field(default_factory=ConversationContext)
     length: int = 96
 
+
 # Define the context variable as an empty dictionary
-context_data = ContextVar('context_data', default=ContextModel().model_dump())
+context_data = ContextVar("context_data", default=ContextModel().model_dump())
+
 
 def client_context_attribute(name, default=None):
     """
@@ -47,6 +53,7 @@ def client_context_attribute(name, default=None):
     # Return the value of the key if it exists, otherwise return the default value
     return data.get(name, default)
 
+
 def set_client_context_attribute(name, value):
     """
     Set the value of the context variable `context_data` for the given key.
@@ -55,7 +62,8 @@ def set_client_context_attribute(name, value):
     data = context_data.get()
     # Set the value of the key
     data[name] = value
-    
+
+
 def set_conversation_context_attribute(name, value):
     """
     Set the value of the context variable `context_data.conversation` for the given key.
@@ -65,6 +73,7 @@ def set_conversation_context_attribute(name, value):
     # Set the value of the key
     data["conversation"][name] = value
 
+
 class ClientContext:
     """
     A context manager to set values to the context variable `context_data`.
@@ -82,10 +91,10 @@ class ClientContext:
         Set the key-value pairs to the context variable `context_data` when entering the context.
         """
         # Get the current context data
-        
+
         data = deepcopy(context_data.get()) if context_data.get() else {}
         data.update(self.values)
-        
+
         # Update the context data
         self.token = context_data.set(data)
 
@@ -93,5 +102,5 @@ class ClientContext:
         """
         Reset the context variable `context_data` to its previous values when exiting the context.
         """
-        
+
         context_data.reset(self.token)

src/talemate/client/custom/__init__.py

@@ -0,0 +1,34 @@
+import importlib
+import os
+
+import structlog
+
+log = structlog.get_logger("talemate.client.custom")
+
+# import every submodule in this directory
+#
+# each directory in this directory is a submodule
+
+# get the current directory
+current_directory = os.path.dirname(__file__)
+
+# get all subdirectories
+subdirectories = [
+    os.path.join(current_directory, name)
+    for name in os.listdir(current_directory)
+    if os.path.isdir(os.path.join(current_directory, name))
+]
+
+# import every submodule
+
+for subdirectory in subdirectories:
+    # get the name of the submodule
+    submodule_name = os.path.basename(subdirectory)
+
+    if submodule_name.startswith("__"):
+        continue
+
+    log.info("activating custom client", module=submodule_name)
+
+    # import the submodule
+    importlib.import_module(f".{submodule_name}", __package__)

src/talemate/client/custom/third_party_clients_go_here.txt

@@ -0,0 +1,5 @@
+Each client should be in its own subdirectory.
+
+The subdirectory itself must be a valid python module.
+
+Check out docs/dev/client/example/test for a very simplistic custom client example.

src/talemate/client/koboldccp.py

@@ -1,16 +1,16 @@
 import asyncio
-import random
 import json
 import logging
+import random
 from abc import ABC, abstractmethod
 from typing import Callable, Union
 
 import requests
 
+import talemate.client.system_prompts as system_prompts
 import talemate.util as util
 from talemate.client.registry import register
-import talemate.client.system_prompts as system_prompts
 from talemate.client.textgenwebui import RESTTaleMateClient
 from talemate.emit import Emission, emit
 
-# NOT IMPLEMENTED AT THIS POINT
+# NOT IMPLEMENTED AT THIS POINT

src/talemate/client/lmstudio.py

@@ -1,56 +1,63 @@
+import pydantic
+from openai import AsyncOpenAI
+
 from talemate.client.base import ClientBase
 from talemate.client.registry import register
 
-from openai import AsyncOpenAI
+
+class Defaults(pydantic.BaseModel):
+    api_url: str = "http://localhost:1234"
+    max_token_length: int = 4096
 
 
 @register()
 class LMStudioClient(ClientBase):
-    
     client_type = "lmstudio"
-    conversation_retries = 5
+
+    class Meta(ClientBase.Meta):
+        name_prefix: str = "LMStudio"
+        title: str = "LMStudio"
+        defaults: Defaults = Defaults()
 
     def set_client(self, **kwargs):
-        self.client = AsyncOpenAI(base_url=self.api_url+"/v1", api_key="sk-1111")
-        
-    def tune_prompt_parameters(self, parameters:dict, kind:str):
+        self.client = AsyncOpenAI(base_url=self.api_url + "/v1", api_key="sk-1111")
+
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
         super().tune_prompt_parameters(parameters, kind)
-        
+
         keys = list(parameters.keys())
-        
+
         valid_keys = ["temperature", "top_p"]
-        
+
         for key in keys:
             if key not in valid_keys:
                 del parameters[key]
-        
-        
+
     async def get_model_name(self):
         model_name = await super().get_model_name()
 
         # model name comes back as a file path, so we need to extract the model name
         # the path could be windows or linux so it needs to handle both backslash and forward slash
-        
+
         if model_name:
             model_name = model_name.replace("\\", "/").split("/")[-1]
 
         return model_name
 
-    async def generate(self, prompt:str, parameters:dict, kind:str):
-        
+    async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
         """
-        human_message = {'role': 'user', 'content': prompt.strip()}
-        
-        self.log.debug("generate", prompt=prompt[:128]+" ...", parameters=parameters)
-        
+        human_message = {"role": "user", "content": prompt.strip()}
+
+        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
+
         try:
             response = await self.client.chat.completions.create(
                 model=self.model_name, messages=[human_message], **parameters
             )
-            
+
             return response.choices[0].message.content
         except Exception as e:
             self.log.error("generate error", e=e)
-            return ""
+            return ""

src/talemate/client/model_prompts.py

@@ -1,51 +1,99 @@
-from jinja2 import Environment, FileSystemLoader
 import os
+import shutil
+import tempfile
+
+import huggingface_hub
 import structlog
+from jinja2 import Environment, FileSystemLoader
 
 __all__ = ["model_prompt"]
 
 BASE_TEMPLATE_PATH = os.path.join(
-    os.path.dirname(os.path.abspath(__file__)), "..", "..", "..", "templates", "llm-prompt"
+    os.path.dirname(os.path.abspath(__file__)),
+    "..",
+    "..",
+    "..",
+    "templates",
+    "llm-prompt",
 )
 
+# holds the default templates
+STD_TEMPLATE_PATH = os.path.join(BASE_TEMPLATE_PATH, "std")
+
+# llm prompt templates provided by talemate
+TALEMATE_TEMPLATE_PATH = os.path.join(BASE_TEMPLATE_PATH, "talemate")
+
+# user overrides
+USER_TEMPLATE_PATH = os.path.join(BASE_TEMPLATE_PATH, "user")
+
+TEMPLATE_IDENTIFIERS = []
+
+
+def register_template_identifier(cls):
+    TEMPLATE_IDENTIFIERS.append(cls)
+    return cls
+
+
 log = structlog.get_logger("talemate.model_prompts")
 
+
 class ModelPrompt:
-    
     """
     Will attempt to load an LLM prompt template based on the model name
-    
+
     If the model name is not found, it will default to the 'default' template
     """
-    
+
     template_map = {}
 
     @property
     def env(self):
         if not hasattr(self, "_env"):
             log.info("modal prompt", base_template_path=BASE_TEMPLATE_PATH)
-            self._env = Environment(loader=FileSystemLoader(BASE_TEMPLATE_PATH))
-            
+            self._env = Environment(
+                loader=FileSystemLoader(
+                    [
+                        USER_TEMPLATE_PATH,
+                        TALEMATE_TEMPLATE_PATH,
+                    ]
+                )
+            )
+
         return self._env
-    
-    def __call__(self, model_name:str, system_message:str, prompt:str):
-        template = self.get_template(model_name)
+
+    @property
+    def std_templates(self) -> list[str]:
+        env = Environment(loader=FileSystemLoader(STD_TEMPLATE_PATH))
+        return sorted(env.list_templates())
+
+    def __call__(self, model_name: str, system_message: str, prompt: str):
+        template, template_file = self.get_template(model_name)
         if not template:
-            template = self.env.get_template("default.jinja2")
-            
-        return template.render({
-            "system_message": system_message,
-            "prompt": prompt,
-            "set_response" : self.set_response
-        })
-        
-    def exists(self, model_name:str):
-        return bool(self.get_template(model_name))
-        
-    def set_response(self, prompt:str, response_str:str):
-        
+            template_file = "default.jinja2"
+            template = self.env.get_template(template_file)
+
+        if "<|BOT|>" in prompt:
+            user_message, coercion_message = prompt.split("<|BOT|>", 1)
+        else:
+            user_message = prompt
+            coercion_message = ""
+
+        return (
+            template.render(
+                {
+                    "system_message": system_message,
+                    "prompt": prompt,
+                    "user_message": user_message,
+                    "coercion_message": coercion_message,
+                    "set_response": self.set_response,
+                }
+            ),
+            template_file,
+        )
+
+    def set_response(self, prompt: str, response_str: str):
         prompt = prompt.strip("\n").strip()
-        
+
         if "<|BOT|>" in prompt:
             if "\n<|BOT|>" in prompt:
                 prompt = prompt.replace("\n<|BOT|>", response_str)
@@ -53,17 +101,17 @@ class ModelPrompt:
                 prompt = prompt.replace("<|BOT|>", response_str)
         else:
             prompt = prompt.rstrip("\n") + response_str
-            
+
         return prompt
 
-    def get_template(self, model_name:str):
+    def get_template(self, model_name: str):
         """
         Will attempt to load an LLM prompt template - this supports
         partial filename matching on the template file name.
         """
-        
+
         matches = []
-        
+
         # Iterate over all templates in the loader's directory
         for template_name in self.env.list_templates():
             # strip extension
@@ -71,16 +119,208 @@ class ModelPrompt:
             # Check if the model name is in the template filename
             if template_name_match.lower() in model_name.lower():
                 matches.append(template_name)
-               
+
         # If there are no matches, return None
         if not matches:
-            return None
-        
+            return None, None
+
         # If there is only one match, return it
         if len(matches) == 1:
-            return self.env.get_template(matches[0])
-        
+            return self.env.get_template(matches[0]), matches[0]
+
         # If there are multiple matches, return the one with the longest name
-        return self.env.get_template(sorted(matches, key=lambda x: len(x), reverse=True)[0])
-    
-model_prompt = ModelPrompt()
+        sorted_matches = sorted(matches, key=lambda x: len(x), reverse=True)
+        return self.env.get_template(sorted_matches[0]), sorted_matches[0]
+
+    def create_user_override(self, template_name: str, model_name: str):
+        """
+        Will copy STD_TEMPLATE_PATH/template_name to USER_TEMPLATE_PATH/model_name.jinja2
+        """
+
+        template_name = template_name.split(".jinja2")[0]
+
+        shutil.copyfile(
+            os.path.join(STD_TEMPLATE_PATH, template_name + ".jinja2"),
+            os.path.join(USER_TEMPLATE_PATH, model_name + ".jinja2"),
+        )
+
+        return os.path.join(USER_TEMPLATE_PATH, model_name + ".jinja2")
+
+    def query_hf_for_prompt_template_suggestion(self, model_name: str):
+        print("query_hf_for_prompt_template_suggestion", model_name)
+        api = huggingface_hub.HfApi()
+
+        try:
+            author, model_name = model_name.split("_", 1)
+        except ValueError:
+            return None
+
+        models = list(
+            api.list_models(
+                filter=huggingface_hub.ModelFilter(model_name=model_name, author=author)
+            )
+        )
+
+        if not models:
+            return None
+
+        model = models[0]
+
+        repo_id = f"{author}/{model_name}"
+        with tempfile.TemporaryDirectory() as tmpdir:
+            readme_path = huggingface_hub.hf_hub_download(
+                repo_id=repo_id, filename="README.md", cache_dir=tmpdir
+            )
+            if not readme_path:
+                return None
+            with open(readme_path) as f:
+                readme = f.read()
+                for identifer_cls in TEMPLATE_IDENTIFIERS:
+                    identifier = identifer_cls()
+                    if identifier(readme):
+                        return f"{identifier.template_str}.jinja2"
+
+
+model_prompt = ModelPrompt()
+
+
+class TemplateIdentifier:
+    def __call__(self, content: str):
+        return False
+
+
+@register_template_identifier
+class Llama2Identifier(TemplateIdentifier):
+    template_str = "Llama2"
+
+    def __call__(self, content: str):
+        return "[INST]" in content and "[/INST]" in content
+
+
+@register_template_identifier
+class ChatMLIdentifier(TemplateIdentifier):
+    template_str = "ChatML"
+
+    def __call__(self, content: str):
+        """
+        <|im_start|>system
+        {{ system_message }}<|im_end|>
+        <|im_start|>user
+        {{ user_message }}<|im_end|>
+        <|im_start|>assistant
+        {{ coercion_message }}
+        """
+
+        return (
+            "<|im_start|>system" in content
+            and "<|im_end|>" in content
+            and "<|im_start|>user" in content
+            and "<|im_start|>assistant" in content
+        )
+
+
+@register_template_identifier
+class InstructionInputResponseIdentifier(TemplateIdentifier):
+    template_str = "InstructionInputResponse"
+
+    def __call__(self, content: str):
+        return (
+            "### Instruction:" in content
+            and "### Input:" in content
+            and "### Response:" in content
+        )
+
+
+@register_template_identifier
+class AlpacaIdentifier(TemplateIdentifier):
+    template_str = "Alpaca"
+
+    def __call__(self, content: str):
+        """
+        {{ system_message }}
+
+        ### Instruction:
+        {{ user_message }}
+
+        ### Response:
+        {{ coercion_message }}
+        """
+
+        return "### Instruction:" in content and "### Response:" in content
+
+
+@register_template_identifier
+class OpenChatIdentifier(TemplateIdentifier):
+    template_str = "OpenChat"
+
+    def __call__(self, content: str):
+        """
+        GPT4 Correct System: {{ system_message }}<|end_of_turn|>GPT4 Correct User: {{ user_message }}<|end_of_turn|>GPT4 Correct Assistant: {{ coercion_message }}
+        """
+
+        return (
+            "<|end_of_turn|>" in content
+            and "GPT4 Correct System:" in content
+            and "GPT4 Correct User:" in content
+            and "GPT4 Correct Assistant:" in content
+        )
+
+
+@register_template_identifier
+class VicunaIdentifier(TemplateIdentifier):
+    template_str = "Vicuna"
+
+    def __call__(self, content: str):
+        """
+        SYSTEM: {{ system_message }}
+        USER: {{ user_message }}
+        ASSISTANT: {{ coercion_message }}
+        """
+
+        return "SYSTEM:" in content and "USER:" in content and "ASSISTANT:" in content
+
+
+@register_template_identifier
+class USER_ASSISTANTIdentifier(TemplateIdentifier):
+    template_str = "USER_ASSISTANT"
+
+    def __call__(self, content: str):
+        """
+        USER: {{ system_message }} {{ user_message }} ASSISTANT: {{ coercion_message }}
+        """
+
+        return "USER:" in content and "ASSISTANT:" in content
+
+
+@register_template_identifier
+class UserAssistantIdentifier(TemplateIdentifier):
+    template_str = "UserAssistant"
+
+    def __call__(self, content: str):
+        """
+        User: {{ system_message }} {{ user_message }}
+        Assistant: {{ coercion_message }}
+        """
+
+        return "User:" in content and "Assistant:" in content
+
+
+@register_template_identifier
+class ZephyrIdentifier(TemplateIdentifier):
+    template_str = "Zephyr"
+
+    def __call__(self, content: str):
+        """
+        <|system|>
+        {{ system_message }}</s>
+        <|user|>
+        {{ user_message }}</s>
+        <|assistant|>
+        {{ coercion_message }}
+        """
+
+        return (
+            "<|system|>" in content
+            and "<|user|>" in content
+            and "<|assistant|>" in content
+        )

src/talemate/client/openai.py

@@ -1,26 +1,42 @@
-import os
 import json
-import traceback
-from openai import AsyncOpenAI
 
-
-from talemate.client.base import ClientBase
-from talemate.client.registry import register
-from talemate.emit import emit
-from talemate.emit.signals import handlers
-import talemate.emit.async_signals as async_signals
-from talemate.config import load_config
-import talemate.instance as instance
-import talemate.client.system_prompts as system_prompts
+import pydantic
 import structlog
 import tiktoken
+from openai import AsyncOpenAI, PermissionDeniedError
+
+from talemate.client.base import ClientBase, ErrorAction
+from talemate.client.registry import register
+from talemate.config import load_config
+from talemate.emit import emit
+from talemate.emit.signals import handlers
 
 __all__ = [
     "OpenAIClient",
 ]
 log = structlog.get_logger("talemate")
 
-def num_tokens_from_messages(messages:list[dict], model:str="gpt-3.5-turbo-0613"):
+# Edit this to add new models / remove old models
+SUPPORTED_MODELS = [
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo",
+    "gpt-4",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-turbo-preview",
+]
+
+JSON_OBJECT_RESPONSE_MODELS = [
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-turbo-preview",
+    "gpt-3.5-turbo-0125",
+]
+
+
+def num_tokens_from_messages(messages: list[dict], model: str = "gpt-3.5-turbo-0613"):
     """Return the number of tokens used by a list of messages."""
     try:
         encoding = tiktoken.encoding_for_model(model)
@@ -71,6 +87,12 @@ def num_tokens_from_messages(messages:list[dict], model:str="gpt-3.5-turbo-0613"
     num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
     return num_tokens
 
+
+class Defaults(pydantic.BaseModel):
+    max_token_length: int = 16384
+    model: str = "gpt-4-turbo-preview"
+
+
 @register()
 class OpenAIClient(ClientBase):
     """
@@ -80,22 +102,31 @@ class OpenAIClient(ClientBase):
     client_type = "openai"
     conversation_retries = 0
     auto_break_repetition_enabled = False
+    # TODO: make this configurable?
+    decensor_enabled = False
 
-    def __init__(self, model="gpt-4-1106-preview", **kwargs):
-        
+    class Meta(ClientBase.Meta):
+        name_prefix: str = "OpenAI"
+        title: str = "OpenAI"
+        manual_model: bool = True
+        manual_model_choices: list[str] = SUPPORTED_MODELS
+        requires_prompt_template: bool = False
+        defaults: Defaults = Defaults()
+
+    def __init__(self, model="gpt-4-turbo-preview", **kwargs):
         self.model_name = model
         self.api_key_status = None
         self.config = load_config()
         super().__init__(**kwargs)
-        
+
         handlers["config_saved"].connect(self.on_config_saved)
 
     @property
     def openai_api_key(self):
-        return self.config.get("openai",{}).get("api_key")
-
+        return self.config.get("openai", {}).get("api_key")
 
     def emit_status(self, processing: bool = None):
+        error_action = None
         if processing is not None:
             self.processing = processing
 
@@ -105,11 +136,20 @@ class OpenAIClient(ClientBase):
         else:
             status = "error"
             model_name = "No API key set"
-            
+            error_action = ErrorAction(
+                title="Set API Key",
+                action_name="openAppConfig",
+                icon="mdi-key-variant",
+                arguments=[
+                    "application",
+                    "openai_api",
+                ],
+            )
+
         if not self.model_name:
             status = "error"
             model_name = "No model loaded"
-            
+
         self.current_status = status
 
         emit(
@@ -118,24 +158,30 @@ class OpenAIClient(ClientBase):
             id=self.name,
             details=model_name,
             status=status,
+            data={
+                "error_action": error_action.model_dump() if error_action else None,
+                "meta": self.Meta().model_dump(),
+            },
         )
 
-    def set_client(self, max_token_length:int=None):
-
+    def set_client(self, max_token_length: int = None):
         if not self.openai_api_key:
             self.client = AsyncOpenAI(api_key="sk-1111")
             log.error("No OpenAI API key set")
             if self.api_key_status:
                 self.api_key_status = False
-                emit('request_client_status')
-                emit('request_agent_status')
+                emit("request_client_status")
+                emit("request_agent_status")
             return
-        
+
         if not self.model_name:
             self.model_name = "gpt-3.5-turbo-16k"
-        
+
+        if max_token_length and not isinstance(max_token_length, int):
+            max_token_length = int(max_token_length)
+
         model = self.model_name
-        
+
         self.client = AsyncOpenAI(api_key=self.openai_api_key)
         if model == "gpt-3.5-turbo":
             self.max_token_length = min(max_token_length or 4096, 4096)
@@ -147,16 +193,20 @@ class OpenAIClient(ClientBase):
             self.max_token_length = min(max_token_length or 128000, 128000)
         else:
             self.max_token_length = max_token_length or 2048
-        
-        
+
         if not self.api_key_status:
             if self.api_key_status is False:
-                emit('request_client_status')
-                emit('request_agent_status')
+                emit("request_client_status")
+                emit("request_agent_status")
             self.api_key_status = True
-        
-        log.info("openai set client", max_token_length=self.max_token_length, provided_max_token_length=max_token_length, model=model)
-            
+
+        log.info(
+            "openai set client",
+            max_token_length=self.max_token_length,
+            provided_max_token_length=max_token_length,
+            model=model,
+        )
+
     def reconfigure(self, **kwargs):
         if kwargs.get("model"):
             self.model_name = kwargs["model"]
@@ -175,66 +225,98 @@ class OpenAIClient(ClientBase):
     async def status(self):
         self.emit_status()
 
-
-    def prompt_template(self, system_message:str, prompt:str):
+    def prompt_template(self, system_message: str, prompt: str):
         # only gpt-4-1106-preview supports json_object response coersion
-        
+
         if "<|BOT|>" in prompt:
             _, right = prompt.split("<|BOT|>", 1)
             if right:
-                prompt = prompt.replace("<|BOT|>",  "\nContinue this response: ")
+                prompt = prompt.replace("<|BOT|>", "\nStart your response with: ")
             else:
                 prompt = prompt.replace("<|BOT|>", "")
-                
+
         return prompt
 
-    def tune_prompt_parameters(self, parameters:dict, kind:str):
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
         super().tune_prompt_parameters(parameters, kind)
-        
+
         keys = list(parameters.keys())
-        
+
         valid_keys = ["temperature", "top_p"]
-        
+
+        # GPT-3.5 models tend to run away with the generated
+        # response size so we allow talemate to set the max_tokens
+        #
+        # GPT-4 on the other hand seems to benefit from letting it
+        # decide the generation length naturally and it will generally
+        # produce reasonably sized responses
+        if self.model_name.startswith("gpt-3.5-"):
+            valid_keys.append("max_tokens")
+
         for key in keys:
             if key not in valid_keys:
                 del parameters[key]
 
-    async def generate(self, prompt:str, parameters:dict, kind:str):
-        
+    async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
         """
-        
+
         if not self.openai_api_key:
             raise Exception("No OpenAI API key set")
-        
-        # only gpt-4-1106-preview supports json_object response coersion
-        supports_json_object = self.model_name in ["gpt-4-1106-preview"]
+
+        # only gpt-4-* supports enforcing json object
+        supports_json_object = (
+            self.model_name.startswith("gpt-4-")
+            or self.model_name in JSON_OBJECT_RESPONSE_MODELS
+        )
         right = None
+        expected_response = None
         try:
-            _, right = prompt.split("\nContinue this response: ")
+            _, right = prompt.split("\nStart your response with: ")
             expected_response = right.strip()
             if expected_response.startswith("{") and supports_json_object:
                 parameters["response_format"] = {"type": "json_object"}
         except (IndexError, ValueError):
             pass
-        
-        human_message = {'role': 'user', 'content': prompt.strip()}
-        system_message = {'role': 'system', 'content': self.get_system_message(kind)}
-        
-        self.log.debug("generate", prompt=prompt[:128]+" ...", parameters=parameters)
-        
+
+        human_message = {"role": "user", "content": prompt.strip()}
+        system_message = {"role": "system", "content": self.get_system_message(kind)}
+
+        self.log.debug(
+            "generate",
+            prompt=prompt[:128] + " ...",
+            parameters=parameters,
+            system_message=system_message,
+        )
+
         try:
             response = await self.client.chat.completions.create(
-                model=self.model_name, messages=[system_message, human_message], **parameters
+                model=self.model_name,
+                messages=[system_message, human_message],
+                **parameters,
             )
-            
+
             response = response.choices[0].message.content
-            
+
+            # older models don't support json_object response coersion
+            # and often like to return the response wrapped in ```json
+            # so we strip that out if the expected response is a json object
+            if (
+                not supports_json_object
+                and expected_response
+                and expected_response.startswith("{")
+            ):
+                if response.startswith("```json") and response.endswith("```"):
+                    response = response[7:-3].strip()
+
             if right and response.startswith(right):
-                response = response[len(right):].strip()
-                
+                response = response[len(right) :].strip()
+
             return response
-                
+        except PermissionDeniedError as e:
+            self.log.error("generate error", e=e)
+            emit("status", message="OpenAI API: Permission Denied", status="error")
+            return ""
         except Exception as e:
-            raise
+            raise

src/talemate/client/openai_compat.py

@@ -0,0 +1,126 @@
+import pydantic
+import structlog
+from openai import AsyncOpenAI, NotFoundError, PermissionDeniedError
+
+from talemate.client.base import ClientBase
+from talemate.client.registry import register
+from talemate.emit import emit
+
+log = structlog.get_logger("talemate.client.openai_compat")
+
+EXPERIMENTAL_DESCRIPTION = """Use this client if you want to connect to a service implementing an OpenAI-compatible API. Success is going to depend on the level of compatibility. Use the actual OpenAI client if you want to connect to OpenAI's API."""
+
+
+class Defaults(pydantic.BaseModel):
+    api_url: str = "http://localhost:5000"
+    api_key: str = ""
+    max_token_length: int = 4096
+    model: str = ""
+
+
+@register()
+class OpenAICompatibleClient(ClientBase):
+    client_type = "openai_compat"
+    conversation_retries = 5
+
+    class Meta(ClientBase.Meta):
+        title: str = "OpenAI Compatible API"
+        name_prefix: str = "OpenAI Compatible API"
+        experimental: str = EXPERIMENTAL_DESCRIPTION
+        enable_api_auth: bool = True
+        manual_model: bool = True
+        defaults: Defaults = Defaults()
+
+    def __init__(self, model=None, api_key=None, **kwargs):
+        self.model_name = model
+        self.api_key = api_key
+        super().__init__(**kwargs)
+
+    @property
+    def experimental(self):
+        return EXPERIMENTAL_DESCRIPTION
+
+    def set_client(self, **kwargs):
+        self.api_key = kwargs.get("api_key", self.api_key)
+
+        url = self.api_url
+        if not url.endswith("/v1"):
+            url = url + "/v1"
+
+        self.client = AsyncOpenAI(base_url=url, api_key=self.api_key)
+        self.model_name = (
+            kwargs.get("model") or kwargs.get("model_name") or self.model_name
+        )
+
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
+        super().tune_prompt_parameters(parameters, kind)
+
+        keys = list(parameters.keys())
+
+        valid_keys = ["temperature", "top_p", "max_tokens"]
+
+        for key in keys:
+            if key not in valid_keys:
+                del parameters[key]
+
+    async def get_model_name(self):
+        try:
+            model_name = await super().get_model_name()
+        except NotFoundError as e:
+            # api does not implement model listing
+            return self.model_name
+        except Exception as e:
+            self.log.error("get_model_name error", e=e)
+            return self.model_name
+
+        # model name may be a file path, so we need to extract the model name
+        # the path could be windows or linux so it needs to handle both backslash and forward slash
+
+        is_filepath = "/" in model_name
+        is_filepath_windows = "\\" in model_name
+
+        if is_filepath or is_filepath_windows:
+            model_name = model_name.replace("\\", "/").split("/")[-1]
+
+        return model_name
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
+        """
+        Generates text from the given prompt and parameters.
+        """
+        human_message = {"role": "user", "content": prompt.strip()}
+
+        self.log.debug("generate", prompt=prompt[:128] + " ...", parameters=parameters)
+
+        try:
+            response = await self.client.chat.completions.create(
+                model=self.model_name, messages=[human_message], **parameters
+            )
+
+            return response.choices[0].message.content
+        except PermissionDeniedError as e:
+            self.log.error("generate error", e=e)
+            emit("status", message="Client API: Permission Denied", status="error")
+            return ""
+        except Exception as e:
+            self.log.error("generate error", e=e)
+            emit(
+                "status", message="Error during generation (check logs)", status="error"
+            )
+            return ""
+
+    def reconfigure(self, **kwargs):
+        if kwargs.get("model"):
+            self.model_name = kwargs["model"]
+        if "api_url" in kwargs:
+            self.api_url = kwargs["api_url"]
+        if "max_token_length" in kwargs:
+            self.max_token_length = (
+                int(kwargs["max_token_length"]) if kwargs["max_token_length"] else 4096
+            )
+        if "api_key" in kwargs:
+            self.api_auth = kwargs["api_key"]
+
+        log.warning("reconfigure", kwargs=kwargs)
+
+        self.set_client(**kwargs)

src/talemate/client/presets.py

@@ -28,18 +28,18 @@ PRESET_TALEMATE_CREATOR = {
 }
 
 PRESET_LLAMA_PRECISE = {
-    'temperature': 0.7,
-    'top_p': 0.1,
-    'top_k': 40,
-    'repetition_penalty': 1.18,
+    "temperature": 0.7,
+    "top_p": 0.1,
+    "top_k": 40,
+    "repetition_penalty": 1.18,
 }
 
 PRESET_DIVINE_INTELLECT = {
-    'temperature': 1.31,
-    'top_p': 0.14,
-    'top_k': 49,
+    "temperature": 1.31,
+    "top_p": 0.14,
+    "top_k": 49,
     "repetition_penalty_range": 1024,
-    'repetition_penalty': 1.17,
+    "repetition_penalty": 1.17,
 }
 
 PRESET_SIMPLE_1 = {
@@ -49,7 +49,8 @@ PRESET_SIMPLE_1 = {
     "repetition_penalty": 1.15,
 }
 
-def configure(config:dict, kind:str, total_budget:int):
+
+def configure(config: dict, kind: str, total_budget: int):
     """
     Sets the config based on the kind of text to generate.
     """
@@ -57,19 +58,22 @@ def configure(config:dict, kind:str, total_budget:int):
     set_max_tokens(config, kind, total_budget)
     return config
 
-def set_max_tokens(config:dict, kind:str, total_budget:int):
+
+def set_max_tokens(config: dict, kind: str, total_budget: int):
     """
     Sets the max_tokens in the config based on the kind of text to generate.
     """
     config["max_tokens"] = max_tokens_for_kind(kind, total_budget)
     return config
 
-def set_preset(config:dict, kind:str):
+
+def set_preset(config: dict, kind: str):
     """
     Sets the preset in the config based on the kind of text to generate.
     """
     config.update(preset_for_kind(kind))
 
+
 def preset_for_kind(kind: str):
     if kind == "conversation":
         return PRESET_TALEMATE_CONVERSATION
@@ -104,62 +108,75 @@ def preset_for_kind(kind: str):
     elif kind == "director":
         return PRESET_SIMPLE_1
     elif kind == "director_short":
-        return PRESET_SIMPLE_1  # Assuming short direction uses the same preset as simple
+        return (
+            PRESET_SIMPLE_1  # Assuming short direction uses the same preset as simple
+        )
     elif kind == "director_yesno":
-        return PRESET_SIMPLE_1  # Assuming yes/no direction uses the same preset as simple
+        return (
+            PRESET_SIMPLE_1  # Assuming yes/no direction uses the same preset as simple
+        )
     elif kind == "edit_dialogue":
         return PRESET_DIVINE_INTELLECT
     elif kind == "edit_add_detail":
         return PRESET_DIVINE_INTELLECT  # Assuming adding detail uses the same preset as divine intellect
     elif kind == "edit_fix_exposition":
         return PRESET_DIVINE_INTELLECT  # Assuming fixing exposition uses the same preset as divine intellect
+    elif kind == "visualize":
+        return PRESET_SIMPLE_1
     else:
         return PRESET_SIMPLE_1  # Default preset if none of the kinds match
 
+
 def max_tokens_for_kind(kind: str, total_budget: int):
     if kind == "conversation":
-        return 75  # Example value, adjust as needed
+        return 75
     elif kind == "conversation_old":
-        return 75  # Example value, adjust as needed
+        return 75
     elif kind == "conversation_long":
-        return 300  # Example value, adjust as needed
+        return 300
     elif kind == "conversation_select_talking_actor":
-        return 30  # Example value, adjust as needed
+        return 30
     elif kind == "summarize":
-        return 500  # Example value, adjust as needed
+        return 500
     elif kind == "analyze":
-        return 500  # Example value, adjust as needed
+        return 500
     elif kind == "analyze_creative":
-        return 1024  # Example value, adjust as needed
+        return 1024
     elif kind == "analyze_long":
-        return 2048  # Example value, adjust as needed
+        return 2048
     elif kind == "analyze_freeform":
-        return 500  # Example value, adjust as needed
+        return 500
+    elif kind == "analyze_freeform_medium":
+        return 192
+    elif kind == "analyze_freeform_medium_short":
+        return 128
     elif kind == "analyze_freeform_short":
-        return 10  # Example value, adjust as needed
+        return 10
     elif kind == "narrate":
-        return 500  # Example value, adjust as needed
+        return 500
     elif kind == "story":
-        return 300  # Example value, adjust as needed
+        return 300
     elif kind == "create":
-        return min(1024, int(total_budget * 0.35))  # Example calculation, adjust as needed
+        return min(1024, int(total_budget * 0.35))
     elif kind == "create_concise":
-        return min(400, int(total_budget * 0.25))  # Example calculation, adjust as needed
+        return min(400, int(total_budget * 0.25))
     elif kind == "create_precise":
-        return min(400, int(total_budget * 0.25))  # Example calculation, adjust as needed
+        return min(400, int(total_budget * 0.25))
     elif kind == "create_short":
         return 25
     elif kind == "director":
-        return min(192, int(total_budget * 0.25))  # Example calculation, adjust as needed
+        return min(192, int(total_budget * 0.25))
     elif kind == "director_short":
-        return 25  # Example value, adjust as needed
+        return 25
     elif kind == "director_yesno":
-        return 2  # Example value, adjust as needed
+        return 2
     elif kind == "edit_dialogue":
-        return 100  # Example value, adjust as needed
+        return 100
     elif kind == "edit_add_detail":
-        return 200  # Example value, adjust as needed
+        return 200
     elif kind == "edit_fix_exposition":
-        return 1024  # Example value, adjust as needed
+        return 1024
+    elif kind == "visualize":
+        return 150
     else:
-        return 150  # Default value if none of the kinds match
+        return 150  # Default value if none of the kinds match

src/talemate/client/runpod.py

@@ -3,17 +3,17 @@ Retrieve pod information from the server which can then be used to bootstrap tal
 connection for the pod.  This is a simple wrapper around the runpod module.
 """
 
+import asyncio
+import json
+import os
+
 import dotenv
 import runpod
-import os
-import json
-import asyncio
-
-from .bootstrap import ClientBootstrap, ClientType, register_list
+import structlog
 
 from talemate.config import load_config
 
-import structlog
+from .bootstrap import ClientBootstrap, ClientType, register_list
 
 log = structlog.get_logger("talemate.client.runpod")
 
@@ -21,73 +21,75 @@ dotenv.load_dotenv()
 
 runpod.api_key = load_config().get("runpod", {}).get("api_key", "")
 
+
 def is_textgen_pod(pod):
-    
     name = pod["name"].lower()
-    
+
     if "textgen" in name or "thebloke llms" in name:
         return True
-    
+
     return False
 
+
 async def _async_get_pods():
     """
     asyncio wrapper around get_pods.
     """
-    
+
     loop = asyncio.get_event_loop()
     return await loop.run_in_executor(None, runpod.get_pods)
 
+
 async def get_textgen_pods():
     """
     Return a list of text generation pods.
     """
-    
+
     if not runpod.api_key:
         return
-    
+
     for pod in await _async_get_pods():
         if not pod["desiredStatus"] == "RUNNING":
             continue
         if is_textgen_pod(pod):
             yield pod
-            
+
 
 async def get_automatic1111_pods():
     """
     Return a list of automatic1111 pods.
     """
-    
+
     if not runpod.api_key:
         return
-    
+
     for pod in await _async_get_pods():
         if not pod["desiredStatus"] == "RUNNING":
             continue
         if "automatic1111" in pod["name"].lower():
             yield pod
-            
+
 
 def _client_bootstrap(client_type: ClientType, pod):
     """
     Return a client bootstrap object for the given client type and pod.
     """
-    
+
     id = pod["id"]
-    
+
     if client_type == ClientType.textgen:
         api_url = f"https://{id}-5000.proxy.runpod.net"
     elif client_type == ClientType.automatic1111:
         api_url = f"https://{id}-5000.proxy.runpod.net"
-    
+
     return ClientBootstrap(
         client_type=client_type,
         uid=pod["id"],
         name=pod["name"],
         api_url=api_url,
-        service_name="runpod"
+        service_name="runpod",
     )
-            
+
 
 @register_list("runpod")
 async def client_bootstrap_list():
@@ -97,13 +99,13 @@ async def client_bootstrap_list():
     textgen_pods = []
     async for pod in get_textgen_pods():
         textgen_pods.append(pod)
-        
+
     automatic1111_pods = []
     async for pod in get_automatic1111_pods():
         automatic1111_pods.append(pod)
-    
+
     for pod in textgen_pods:
         yield _client_bootstrap(ClientType.textgen, pod)
-    
+
     for pod in automatic1111_pods:
-        yield _client_bootstrap(ClientType.automatic1111, pod)
+        yield _client_bootstrap(ClientType.automatic1111, pod)

src/talemate/client/system_prompts.py

@@ -18,4 +18,30 @@ EDITOR = str(Prompt.get("editor.system"))
 
 WORLD_STATE = str(Prompt.get("world_state.system-analyst"))
 
-SUMMARIZE = str(Prompt.get("summarizer.system"))
+SUMMARIZE = str(Prompt.get("summarizer.system"))
+
+VISUALIZE = str(Prompt.get("visual.system"))
+
+# CAREBEAR PROMPTS
+
+ROLEPLAY_NO_DECENSOR = str(Prompt.get("conversation.system-no-decensor"))
+
+NARRATOR_NO_DECENSOR = str(Prompt.get("narrator.system-no-decensor"))
+
+CREATOR_NO_DECENSOR = str(Prompt.get("creator.system-no-decensor"))
+
+DIRECTOR_NO_DECENSOR = str(Prompt.get("director.system-no-decensor"))
+
+ANALYST_NO_DECENSOR = str(Prompt.get("world_state.system-analyst-no-decensor"))
+
+ANALYST_FREEFORM_NO_DECENSOR = str(
+    Prompt.get("world_state.system-analyst-freeform-no-decensor")
+)
+
+EDITOR_NO_DECENSOR = str(Prompt.get("editor.system-no-decensor"))
+
+WORLD_STATE_NO_DECENSOR = str(Prompt.get("world_state.system-analyst-no-decensor"))
+
+SUMMARIZE_NO_DECENSOR = str(Prompt.get("summarizer.system-no-decensor"))
+
+VISUALIZE_NO_DECENSOR = str(Prompt.get("visual.system-no-decensor"))

src/talemate/client/textgenwebui.py

@@ -1,74 +1,98 @@
-from talemate.client.base import ClientBase, STOPPING_STRINGS
-from talemate.client.registry import register
-from openai import AsyncOpenAI
-import httpx
-import copy
 import random
+import re
+
+import httpx
 import structlog
+from openai import AsyncOpenAI
+
+from talemate.client.base import STOPPING_STRINGS, ClientBase
+from talemate.client.registry import register
 
 log = structlog.get_logger("talemate.client.textgenwebui")
 
+
 @register()
 class TextGeneratorWebuiClient(ClientBase):
-    
     client_type = "textgenwebui"
-    
-    def tune_prompt_parameters(self, parameters:dict, kind:str):
+
+    class Meta(ClientBase.Meta):
+        name_prefix: str = "TextGenWebUI"
+        title: str = "Text-Generation-WebUI (ooba)"
+
+    def tune_prompt_parameters(self, parameters: dict, kind: str):
         super().tune_prompt_parameters(parameters, kind)
-        parameters["stopping_strings"] = STOPPING_STRINGS + parameters.get("extra_stopping_strings", [])
+        parameters["stopping_strings"] = STOPPING_STRINGS + parameters.get(
+            "extra_stopping_strings", []
+        )
         # is this needed?
         parameters["max_new_tokens"] = parameters["max_tokens"]
         parameters["stop"] = parameters["stopping_strings"]
-        
+
         # Half temperature on -Yi- models
-        if self.model_name and "-yi-" in self.model_name.lower() and parameters["temperature"] > 0.1:
-            parameters["temperature"] = parameters["temperature"] / 2
-            log.debug("halfing temperature for -yi- model", temperature=parameters["temperature"])
-            
+        if self.model_name and self.is_yi_model():
+            parameters["smoothing_factor"] = 0.3
+            # also half the temperature
+            parameters["temperature"] = max(0.1, parameters["temperature"] / 2)
+            log.debug(
+                "applying temperature smoothing for Yi model",
+            )
 
     def set_client(self, **kwargs):
-        self.client = AsyncOpenAI(base_url=self.api_url+"/v1", api_key="sk-1111")
-    
+        self.client = AsyncOpenAI(base_url=self.api_url + "/v1", api_key="sk-1111")
+
+    def is_yi_model(self):
+        model_name = self.model_name.lower()
+        # regex match for yi encased by non-word characters
+
+        return bool(re.search(r"[\-_]yi[\-_]", model_name))
+
     async def get_model_name(self):
         async with httpx.AsyncClient() as client:
-            response = await client.get(f"{self.api_url}/v1/internal/model/info", timeout=2)
+            response = await client.get(
+                f"{self.api_url}/v1/internal/model/info", timeout=2
+            )
         if response.status_code == 404:
             raise Exception("Could not find model info (wrong api version?)")
         response_data = response.json()
         model_name = response_data.get("model_name")
-        
+
         if model_name == "None":
             model_name = None
-        
+
         return model_name
-    
-    
-    async def generate(self, prompt:str, parameters:dict, kind:str):
-        
+
+    async def generate(self, prompt: str, parameters: dict, kind: str):
         """
         Generates text from the given prompt and parameters.
         """
-        
+
         headers = {}
         headers["Content-Type"] = "application/json"
-        
+
         parameters["prompt"] = prompt.strip(" ")
-        
+
         async with httpx.AsyncClient() as client:
-            response = await client.post(f"{self.api_url}/v1/completions", json=parameters, timeout=None, headers=headers)
+            response = await client.post(
+                f"{self.api_url}/v1/completions",
+                json=parameters,
+                timeout=None,
+                headers=headers,
+            )
             response_data = response.json()
             return response_data["choices"][0]["text"]
-        
-    def jiggle_randomness(self, prompt_config:dict, offset:float=0.3) -> dict:
+
+    def jiggle_randomness(self, prompt_config: dict, offset: float = 0.3) -> dict:
         """
         adjusts temperature and repetition_penalty
         by random values using the base value as a center
         """
-        
+
         temp = prompt_config["temperature"]
         rep_pen = prompt_config["repetition_penalty"]
-        
+
         min_offset = offset * 0.3
 
         prompt_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
-        prompt_config["repetition_penalty"] = random.uniform(rep_pen + min_offset * 0.3, rep_pen + offset * 0.3)
+        prompt_config["repetition_penalty"] = random.uniform(
+            rep_pen + min_offset * 0.3, rep_pen + offset * 0.3
+        )

src/talemate/client/utils.py

@@ -1,32 +1,33 @@
 import copy
 import random
 
-def jiggle_randomness(prompt_config:dict, offset:float=0.3) -> dict:
+
+def jiggle_randomness(prompt_config: dict, offset: float = 0.3) -> dict:
     """
     adjusts temperature and repetition_penalty
     by random values using the base value as a center
     """
-    
+
     temp = prompt_config["temperature"]
     rep_pen = prompt_config["repetition_penalty"]
-    
+
     copied_config = copy.deepcopy(prompt_config)
-    
+
     min_offset = offset * 0.3
 
     copied_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
-    copied_config["repetition_penalty"] = random.uniform(rep_pen + min_offset * 0.3, rep_pen + offset * 0.3)
-    
+    copied_config["repetition_penalty"] = random.uniform(
+        rep_pen + min_offset * 0.3, rep_pen + offset * 0.3
+    )
+
     return copied_config
-    
-    
-def jiggle_enabled_for(kind:str):
-    
+
+
+def jiggle_enabled_for(kind: str):
     if kind in ["conversation", "story"]:
         return True
-    
+
     if kind.startswith("narrate"):
         return True
-    
-    return False
 
+    return False

src/talemate/commands/__init__.py

@@ -1,4 +1,5 @@
 from .base import TalemateCommand
+from .cmd_characters import *
 from .cmd_debug_tools import *
 from .cmd_dialogue import *
 from .cmd_director import CmdDirectorDirect, CmdDirectorDirectWithOverride
@@ -11,17 +12,17 @@ from .cmd_memget import CmdMemget
 from .cmd_memset import CmdMemset
 from .cmd_narrate import *
 from .cmd_rebuild_archive import CmdRebuildArchive
+from .cmd_remove_character import CmdRemoveCharacter
 from .cmd_rename import CmdRename
-from .cmd_rerun import CmdRerun
+from .cmd_rerun import *
 from .cmd_reset import CmdReset
 from .cmd_rm import CmdRm
-from .cmd_remove_character import CmdRemoveCharacter
+from .cmd_run_helios_test import CmdHeliosTest
 from .cmd_save import CmdSave
 from .cmd_save_as import CmdSaveAs
 from .cmd_save_characters import CmdSaveCharacters
-from .cmd_setenv import CmdSetEnvironmentToScene, CmdSetEnvironmentToCreative
+from .cmd_setenv import CmdSetEnvironmentToCreative, CmdSetEnvironmentToScene
 from .cmd_time_util import *
 from .cmd_tts import *
 from .cmd_world_state import *
-from .cmd_run_helios_test import CmdHeliosTest
-from .manager import Manager
+from .manager import Manager

src/talemate/commands/base.py

@@ -41,7 +41,7 @@ class TalemateCommand(Emitter, ABC):
         raise NotImplementedError(
             "TalemateCommand.run() must be implemented by subclass"
         )
-        
+
     @property
     def verbose_name(self):
         if self.label:
@@ -50,6 +50,6 @@ class TalemateCommand(Emitter, ABC):
 
     def command_start(self):
         emit("command_status", self.verbose_name, status="started")
-        
+
     def command_end(self):
-        emit("command_status", self.verbose_name, status="ended")
+        emit("command_status", self.verbose_name, status="ended")

src/talemate/commands/cmd_characters.py

@@ -0,0 +1,172 @@
+import structlog
+
+from talemate.character import activate_character, deactivate_character
+from talemate.commands.base import TalemateCommand
+from talemate.commands.manager import register
+from talemate.emit import emit, wait_for_input
+from talemate.instance import get_agent
+
+log = structlog.get_logger("talemate.cmd.characters")
+
+__all__ = [
+    "CmdDeactivateCharacter",
+    "CmdActivateCharacter",
+]
+
+
+@register
+class CmdDeactivateCharacter(TalemateCommand):
+    """
+    Deactivates a character
+    """
+
+    name = "character_deactivate"
+    description = "Will deactivate a character"
+    aliases = ["char_d"]
+
+    label = "Character exit"
+
+    async def run(self):
+        narrator = get_agent("narrator")
+        world_state = get_agent("world_state")
+        characters = list(
+            [character.name for character in self.scene.get_npc_characters()]
+        )
+
+        if not characters:
+            emit("status", message="No characters found", status="error")
+            return True
+
+        if self.args:
+            character_name = self.args[0]
+        else:
+            character_name = await wait_for_input(
+                "Which character do you want to deactivate?",
+                data={
+                    "input_type": "select",
+                    "choices": characters,
+                },
+            )
+
+        if not character_name:
+            emit("status", message="No character selected", status="error")
+            return True
+
+        never_narrate = len(self.args) > 1 and self.args[1] == "no"
+
+        if not never_narrate:
+            is_present = await world_state.is_character_present(character_name)
+            is_leaving = await world_state.is_character_leaving(character_name)
+            log.debug(
+                "deactivate_character",
+                character_name=character_name,
+                is_present=is_present,
+                is_leaving=is_leaving,
+                never_narrate=never_narrate,
+            )
+        else:
+            is_present = False
+            is_leaving = True
+            log.debug(
+                "deactivate_character",
+                character_name=character_name,
+                never_narrate=never_narrate,
+            )
+
+        if is_present and not is_leaving and not never_narrate:
+            direction = await wait_for_input(
+                f"How does {character_name} exit the scene? (leave blank for AI to decide)"
+            )
+            message = await narrator.action_to_narration(
+                "narrate_character_exit",
+                character=self.scene.get_character(character_name),
+                direction=direction,
+            )
+            self.narrator_message(message)
+
+        await deactivate_character(self.scene, character_name)
+
+        emit("status", message=f"Deactivated {character_name}", status="success")
+
+        self.scene.emit_status()
+        self.scene.world_state.emit()
+
+        return True
+
+
+@register
+class CmdActivateCharacter(TalemateCommand):
+    """
+    Activates a character
+    """
+
+    name = "character_activate"
+    description = "Will activate a character"
+    aliases = ["char_a"]
+
+    label = "Character enter"
+
+    async def run(self):
+        world_state = get_agent("world_state")
+        narrator = get_agent("narrator")
+        characters = list(self.scene.inactive_characters.keys())
+
+        if not characters:
+            emit("status", message="No characters found", status="error")
+            return True
+
+        if self.args:
+            character_name = self.args[0]
+            if character_name not in characters:
+                emit("status", message="Character not found", status="error")
+                return True
+        else:
+            character_name = await wait_for_input(
+                "Which character do you want to activate?",
+                data={
+                    "input_type": "select",
+                    "choices": characters,
+                },
+            )
+
+        if not character_name:
+            emit("status", message="No character selected", status="error")
+            return True
+
+        never_narrate = len(self.args) > 1 and self.args[1] == "no"
+
+        if not never_narrate:
+            is_present = await world_state.is_character_present(character_name)
+            log.debug(
+                "activate_character",
+                character_name=character_name,
+                is_present=is_present,
+                never_narrate=never_narrate,
+            )
+        else:
+            is_present = True
+            log.debug(
+                "activate_character",
+                character_name=character_name,
+                never_narrate=never_narrate,
+            )
+
+        await activate_character(self.scene, character_name)
+
+        if not is_present and not never_narrate:
+            direction = await wait_for_input(
+                f"How does {character_name} enter the scene? (leave blank for AI to decide)"
+            )
+            message = await narrator.action_to_narration(
+                "narrate_character_entry",
+                character=self.scene.get_character(character_name),
+                direction=direction,
+            )
+            self.narrator_message(message)
+
+        emit("status", message=f"Activated {character_name}", status="success")
+
+        self.scene.emit_status()
+        self.scene.world_state.emit()
+
+        return True

src/talemate/commands/cmd_debug_tools.py

@@ -1,6 +1,9 @@
 import asyncio
+import json
 import logging
 
+import structlog
+
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
 from talemate.prompts.base import set_default_sectioning_handler
@@ -12,6 +15,9 @@ __all__ = [
     "CmdRunAutomatic",
 ]
 
+log = structlog.get_logger("talemate.commands.cmd_debug_tools")
+
+
 @register
 class CmdDebugOn(TalemateCommand):
     """
@@ -26,6 +32,7 @@ class CmdDebugOn(TalemateCommand):
         logging.getLogger().setLevel(logging.DEBUG)
         await asyncio.sleep(0)
 
+
 @register
 class CmdDebugOff(TalemateCommand):
     """
@@ -46,66 +53,64 @@ class CmdPromptChangeSectioning(TalemateCommand):
     """
     Command class for the '_prompt_change_sectioning' command
     """
-    
+
     name = "_prompt_change_sectioning"
     description = "Change the sectioning handler for the prompt system"
     aliases = []
-    
+
     async def run(self):
-        
         if not self.args:
             self.emit("system", "You must specify a sectioning handler")
             return
-        
+
         handler_name = self.args[0]
-        
+
         set_default_sectioning_handler(handler_name)
-        
+
         self.emit("system", f"Sectioning handler set to {handler_name}")
         await asyncio.sleep(0)
-        
-        
+
+
 @register
 class CmdRunAutomatic(TalemateCommand):
     """
     Command class for the 'run_automatic' command
     """
-    
+
     name = "run_automatic"
     description = "Will make the player character AI controlled for n turns"
     aliases = ["auto"]
-    
+
     async def run(self):
-        
-        
         if self.args:
             turns = int(self.args[0])
         else:
             turns = 10
-        
+
         self.emit("system", f"Making player character AI controlled for {turns} turns")
         self.scene.get_player_character().actor.ai_controlled = turns
-        
-        
+
 
 @register
 class CmdLongTermMemoryStats(TalemateCommand):
     """
     Command class for the 'long_term_memory_stats' command
     """
-    
+
     name = "long_term_memory_stats"
     description = "Show stats for the long term memory"
     aliases = ["ltm_stats"]
-    
+
     async def run(self):
-        
         memory = self.scene.get_helper("memory").agent
-        
+
         count = await memory.count()
         db_name = memory.db_name
-        
-        self.emit("system", f"Long term memory for {self.scene.name} has {count} entries in the {db_name} database")
+
+        self.emit(
+            "system",
+            f"Long term memory for {self.scene.name} has {count} entries in the {db_name} database",
+        )
 
 
 @register
@@ -113,35 +118,63 @@ class CmdLongTermMemoryReset(TalemateCommand):
     """
     Command class for the 'long_term_memory_reset' command
     """
-    
+
     name = "long_term_memory_reset"
     description = "Reset the long term memory"
     aliases = ["ltm_reset"]
-    
+
     async def run(self):
-        
         await self.scene.commit_to_memory()
-        
+
         self.emit("system", f"Long term memory for {self.scene.name} has been reset")
-        
+
+
 @register
 class CmdSetContentContext(TalemateCommand):
     """
     Command class for the 'set_content_context' command
     """
-    
+
     name = "set_content_context"
     description = "Set the content context for the scene"
     aliases = ["set_context"]
-    
+
     async def run(self):
-        
         if not self.args:
             self.emit("system", "You must specify a context")
             return
-        
+
         context = self.args[0]
-        
+
         self.scene.context = context
-        
-        self.emit("system", f"Content context set to {context}")
+
+        self.emit("system", f"Content context set to {context}")
+
+
+@register
+class CmdDumpHistory(TalemateCommand):
+    """
+    Command class for the 'dump_history' command
+    """
+
+    name = "dump_history"
+    description = "Dump the history of the scene"
+    aliases = []
+
+    async def run(self):
+        for entry in self.scene.history:
+            log.debug("dump_history", entry=entry)
+
+
+@register
+class CmdDumpSceneSerialization(TalemateCommand):
+    """
+    Command class for the 'dump_scene_serialization' command
+    """
+
+    name = "dump_scene_serialization"
+    description = "Dump the scene serialization"
+    aliases = []
+
+    async def run(self):
+        log.debug("dump_scene_serialization", serialization=self.scene.json)

src/talemate/commands/cmd_dialogue.py

@@ -1,9 +1,10 @@
 import asyncio
 import random
+
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.scene_message import DirectorMessage
 from talemate.emit import wait_for_input
+from talemate.scene_message import DirectorMessage
 
 __all__ = [
     "CmdAIDialogue",
@@ -11,6 +12,7 @@ __all__ = [
     "CmdAIDialogueDirected",
 ]
 
+
 @register
 class CmdAIDialogue(TalemateCommand):
     """
@@ -23,101 +25,104 @@ class CmdAIDialogue(TalemateCommand):
 
     async def run(self):
         conversation_agent = self.scene.get_helper("conversation").agent
-        
+
         actor = None
-        
+
         # if there is only one npc in the scene, use that
-        
+
         if len(self.scene.npc_character_names) == 1:
             actor = list(self.scene.get_npc_characters())[0].actor
         else:
-            
             if conversation_agent.actions["natural_flow"].enabled:
                 await conversation_agent.apply_natural_flow(force=True, npcs_only=True)
                 character_name = self.scene.next_actor
-                actor = self.scene.get_character(character_name).actor
+                try:
+                    actor = self.scene.get_character(character_name).actor
+                except AttributeError:
+                    return
+
                 if actor.character.is_player:
                     actor = random.choice(list(self.scene.get_npc_characters())).actor
             else:
                 # randomly select an actor
                 actor = random.choice(list(self.scene.get_npc_characters())).actor
-            
+
         if not actor:
             return
-        
+
         messages = await actor.talk()
-        
+
         self.scene.process_npc_dialogue(actor, messages)
-        
+
+
 @register
 class CmdAIDialogueSelective(TalemateCommand):
     """
     Command class for the 'ai_dialogue_selective' command
-    
+
     Will allow the player to select which npc dialogue will be generated
     for
     """
-    
+
     name = "ai_dialogue_selective"
-    
+
     description = "Generate dialogue for an AI selected actor"
-    
+
     aliases = ["dlg_selective"]
-    
+
     async def run(self):
-        
         npc_name = self.args[0]
-        
+
         character = self.scene.get_character(npc_name)
-        
+
         if not character:
             self.emit("system_message", message=f"Character not found: {npc_name}")
             return
-        
+
         actor = character.actor
-        
+
         messages = await actor.talk()
-        
+
         self.scene.process_npc_dialogue(actor, messages)
 
+
 @register
 class CmdAIDialogueDirected(TalemateCommand):
     """
     Command class for the 'ai_dialogue_directed' command
-    
+
     Will allow the player to select which npc dialogue will be generated
     for
     """
-    
+
     name = "ai_dialogue_directed"
-    
+
     description = "Generate dialogue for an AI selected actor"
-    
+
     aliases = ["dlg_directed"]
-    
+
     async def run(self):
-        
         npc_name = self.args[0]
-        
+
         character = self.scene.get_character(npc_name)
-        
+
         if not character:
             self.emit("system_message", message=f"Character not found: {npc_name}")
             return
-        
-        prefix = f"Director instructs {character.name}: \"To progress the scene, i want you to"
-        
-        direction = await wait_for_input(prefix+"... (enter your instructions)")
-        direction = f"{prefix} {direction}\""
-        
+
+        prefix = f'Director instructs {character.name}: "To progress the scene, i want you to'
+
+        direction = await wait_for_input(prefix + "... (enter your instructions)")
+        direction = f'{prefix} {direction}"'
+
         director_message = DirectorMessage(direction, source=character.name)
-        
+
         self.emit("director", director_message, character=character)
-        
+
         self.scene.push_history(director_message)
-        
+
         actor = character.actor
-        
+
         messages = await actor.talk()
-        
-        self.scene.process_npc_dialogue(actor, messages)
+
+        self.scene.process_npc_dialogue(actor, messages)

src/talemate/commands/cmd_director.py

@@ -1,8 +1,8 @@
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.emit import wait_for_input, emit
-from talemate.util import colored_text, wrap_text
+from talemate.emit import emit, wait_for_input
 from talemate.scene_message import DirectorMessage
+from talemate.util import colored_text, wrap_text
 
 
 @register
@@ -21,9 +21,9 @@ class CmdDirectorDirect(TalemateCommand):
         if not director:
             self.system_message("No director found")
             return True
-        
+
         npc_count = self.scene.num_npc_characters()
-        
+
         if npc_count == 1:
             character = list(self.scene.get_npc_characters())[0]
         elif npc_count > 1:
@@ -36,17 +36,20 @@ class CmdDirectorDirect(TalemateCommand):
         if not character:
             self.system_message(f"Character not found: {name}")
             return True
-        
-        goal = await wait_for_input(f"Enter a new goal for the director to direct {character.name}")
-        
+
+        goal = await wait_for_input(
+            f"Enter a new goal for the director to direct {character.name}"
+        )
+
         if not goal.strip():
             self.system_message("No goal specified")
             return True
-        
+
         director.agent.actions["direct"].config["prompt"].value = goal
-        
+
         await director.agent.direct_character(character, goal)
-        
+
+
 @register
 class CmdDirectorDirectWithOverride(CmdDirectorDirect):
     """
@@ -54,7 +57,9 @@ class CmdDirectorDirectWithOverride(CmdDirectorDirect):
     """
 
     name = "director_with_goal"
-    description = "Calls a director to give directionts to a character (with goal specified)"
+    description = (
+        "Calls a director to give directionts to a character (with goal specified)"
+    )
     aliases = ["direct_g"]
 
     async def run(self):

src/talemate/commands/cmd_memget.py

@@ -1,6 +1,7 @@
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
 
+
 @register
 class CmdMemget(TalemateCommand):
     """
@@ -16,4 +17,4 @@ class CmdMemget(TalemateCommand):
         memories = self.scene.get_helper("memory").agent.get(query)
 
         for memory in memories:
-            self.emit("narrator", memory["text"])
+            self.emit("narrator", memory["text"])

src/talemate/commands/cmd_narrate.py

@@ -2,9 +2,9 @@ import asyncio
 
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.util import colored_text, wrap_text
-from talemate.scene_message import NarratorMessage
 from talemate.emit import wait_for_input
+from talemate.scene_message import NarratorMessage
+from talemate.util import colored_text, wrap_text
 
 __all__ = [
     "CmdNarrate",
@@ -14,6 +14,7 @@ __all__ = [
     "CmdNarrateC",
 ]
 
+
 @register
 class CmdNarrate(TalemateCommand):
     """
@@ -33,7 +34,7 @@ class CmdNarrate(TalemateCommand):
 
         narration = await narrator.agent.narrate_scene()
         message = NarratorMessage(narration, source="narrate_scene")
-        
+
         self.narrator_message(message)
         self.scene.push_history(message)
 
@@ -58,17 +59,22 @@ class CmdNarrateQ(TalemateCommand):
 
         if self.args:
             query = self.args[0]
-            at_the_end = (self.args[1].lower() == "true") if len(self.args) > 1 else False
+            at_the_end = (
+                (self.args[1].lower() == "true") if len(self.args) > 1 else False
+            )
         else:
             query = await wait_for_input("Enter query: ")
             at_the_end = False
 
         narration = await narrator.agent.narrate_query(query, at_the_end=at_the_end)
-        message = NarratorMessage(narration, source=f"narrate_query:{query.replace(':', '-')}")
-        
+        message = NarratorMessage(
+            narration, source=f"narrate_query:{query.replace(':', '-')}"
+        )
+
         self.narrator_message(message)
         self.scene.push_history(message)
-        
+
+
 @register
 class CmdNarrateProgress(TalemateCommand):
     """
@@ -89,10 +95,11 @@ class CmdNarrateProgress(TalemateCommand):
         narration = await narrator.agent.progress_story()
 
         message = NarratorMessage(narration, source="progress_story")
-        
+
         self.narrator_message(message)
         self.scene.push_history(message)
 
+
 @register
 class CmdNarrateProgressDirected(TalemateCommand):
     """
@@ -105,16 +112,17 @@ class CmdNarrateProgressDirected(TalemateCommand):
 
     async def run(self):
         narrator = self.scene.get_helper("narrator")
-        
+
         direction = await wait_for_input("Enter direction for the narrator: ")
 
         narration = await narrator.agent.progress_story(narrative_direction=direction)
 
         message = NarratorMessage(narration, source=f"progress_story:{direction}")
-        
+
         self.narrator_message(message)
         self.scene.push_history(message)
 
+
 @register
 class CmdNarrateC(TalemateCommand):
     """
@@ -149,7 +157,8 @@ class CmdNarrateC(TalemateCommand):
 
         self.narrator_message(message)
         self.scene.push_history(message)
-        
+
+
 @register
 class CmdNarrateDialogue(TalemateCommand):
     """
@@ -165,23 +174,25 @@ class CmdNarrateDialogue(TalemateCommand):
         narrator = self.scene.get_helper("narrator")
 
         character_messages = self.scene.collect_messages("character", max_iterations=5)
-        
+
         if not character_messages:
             self.system_message("No recent dialogue message found")
             return True
-        
+
         character_message = character_messages[0]
-        
+
         character_name = character_message.character_name
-        
+
         character = self.scene.get_character(character_name)
-        
+
         if not character:
             self.system_message(f"Character not found: {character_name}")
             return True
-        
+
         narration = await narrator.agent.narrate_after_dialogue(character)
-        message = NarratorMessage(narration, source=f"narrate_dialogue:{character.name}")
+        message = NarratorMessage(
+            narration, source=f"narrate_dialogue:{character.name}"
+        )
 
         self.narrator_message(message)
-        self.scene.push_history(message)
+        self.scene.push_history(message)

src/talemate/commands/cmd_rebuild_archive.py

@@ -20,12 +20,18 @@ class CmdRebuildArchive(TalemateCommand):
         if not summarizer:
             self.system_message("No summarizer found")
             return True
-        
+
         # clear out archived history, but keep pre-established history
         self.scene.archived_history = [
             ah for ah in self.scene.archived_history if ah.get("end") is None
         ]
 
+        self.scene.ts = (
+            self.scene.archived_history[-1].ts
+            if self.scene.archived_history
+            else "PT0S"
+        )
+
         while True:
             more = await summarizer.agent.build_archive(self.scene)
 

src/talemate/commands/cmd_remove_character.py

@@ -14,38 +14,37 @@ class CmdRemoveCharacter(TalemateCommand):
     aliases = ["rmc"]
 
     async def run(self):
-        
         characters = list([character.name for character in self.scene.get_characters()])
-        
+
         if not characters:
             self.system_message("No characters found")
             return True
-        
+
         if self.args:
             character_name = self.args[0]
         else:
-            character_name = await wait_for_input("Which character do you want to remove?", data={
-                "input_type": "select",
-                "choices": characters,  
-            })
-        
+            character_name = await wait_for_input(
+                "Which character do you want to remove?",
+                data={
+                    "input_type": "select",
+                    "choices": characters,
+                },
+            )
+
         if not character_name:
             self.system_message("No character selected")
             return True
-        
 
         character = self.scene.get_character(character_name)
-        
+
         if not character:
             self.system_message(f"Character {character_name} not found")
             return True
-    
+
         await self.scene.remove_actor(character.actor)
-        
+
         self.system_message(f"Removed {character.name} from scene")
-        
+
         self.scene.emit_status()
-        
+
         return True
-        
-        

src/talemate/commands/cmd_rename.py

@@ -2,7 +2,6 @@ import asyncio
 
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-
 from talemate.emit import wait_for_input
 
 
@@ -23,20 +22,23 @@ class CmdRename(TalemateCommand):
             character_name = self.args[0]
         else:
             character_names = self.scene.character_names
-            character_name = await wait_for_input("Which character do you want to rename?", data={
-                "input_type": "select",
-                "choices": character_names,  
-            }) 
-        
+            character_name = await wait_for_input(
+                "Which character do you want to rename?",
+                data={
+                    "input_type": "select",
+                    "choices": character_names,
+                },
+            )
+
         character = self.scene.get_character(character_name)
-        
+
         if not character:
             self.system_message(f"Character {character_name} not found")
             return True
-        
+
         name = await wait_for_input("Enter new name: ")
 
         character.rename(name)
         await asyncio.sleep(0)
-        
+
         return True

src/talemate/commands/cmd_rerun.py

@@ -1,6 +1,14 @@
+from talemate.client.context import ClientContext
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.client.context import ClientContext
+from talemate.context import RerunContext
+from talemate.emit import wait_for_input
+
+__all__ = [
+    "CmdRerun",
+    "CmdRerunWithDirection",
+]
+
 
 @register
 class CmdRerun(TalemateCommand):
@@ -15,4 +23,37 @@ class CmdRerun(TalemateCommand):
     async def run(self):
         nuke_repetition = self.args[0] if self.args else 0.0
         with ClientContext(nuke_repetition=nuke_repetition):
-            await self.scene.rerun()
+            await self.scene.rerun()
+
+
+@register
+class CmdRerunWithDirection(TalemateCommand):
+    """
+    Command class for the 'rerun_directed' command
+    """
+
+    name = "rerun_directed"
+    description = "Rerun the scene with a direction"
+    aliases = ["rrd"]
+
+    label = "Directed Rerun"
+
+    async def run(self):
+        nuke_repetition = self.args[0] if self.args else 0.0
+        method = self.args[1] if len(self.args) > 1 else "replace"
+
+        if method not in ["replace", "edit"]:
+            raise ValueError(
+                f"Unknown method: {method}. Valid methods are 'replace' and 'edit'."
+            )
+
+        if method == "replace":
+            hint = ""
+        else:
+            hint = " (subtle change to previous generation)"
+
+        direction = await wait_for_input(f"Instructions for regeneration{hint}: ")
+
+        with RerunContext(self.scene, direction=direction, method=method):
+            with ClientContext(direction=direction, nuke_repetition=nuke_repetition):
+                await self.scene.rerun()

src/talemate/commands/cmd_reset.py

@@ -1,7 +1,6 @@
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-
-from talemate.emit import wait_for_input, wait_for_input_yesno, emit
+from talemate.emit import emit, wait_for_input, wait_for_input_yesno
 from talemate.exceptions import ResetScene
 
 
@@ -16,13 +15,12 @@ class CmdReset(TalemateCommand):
     aliases = [""]
 
     async def run(self):
-        
         reset = await wait_for_input_yesno("Reset the scene?")
-        
+
         if reset.lower() not in ["yes", "y"]:
             self.system_message("Reset cancelled")
             return True
-        
+
         self.scene.reset()
-        
+
         raise ResetScene()

src/talemate/commands/cmd_run_helios_test.py

@@ -1,7 +1,6 @@
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-
-from talemate.emit import wait_for_input, wait_for_input_yesno, emit
+from talemate.emit import emit, wait_for_input, wait_for_input_yesno
 from talemate.exceptions import ResetScene
 
 
@@ -14,26 +13,25 @@ class CmdHeliosTest(TalemateCommand):
     name = "helios_test"
     description = "Runs the helios test"
     aliases = [""]
-    
+
     analyst_script = [
         "Good morning helios, how are you today? Are you ready to run some tests?",
     ]
 
     async def run(self):
-        
         if self.scene.name != "Helios Test Arena":
             emit("system", "You are not in the Helios Test Arena")
-        
+
         self.scene.reset()
-        
+
         self.scene
 
         player = self.scene.get_player_character()
         player.actor.muted = 10
-        
+
         analyst = self.scene.get_character("The analyst")
         actor = analyst.actor
-        
+
         actor.script = self.analyst_script
-    
+
         raise ResetScene()

src/talemate/commands/cmd_setenv.py

@@ -2,6 +2,7 @@ import asyncio
 
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
+from talemate.emit import emit
 from talemate.exceptions import RestartSceneLoop
 
 
@@ -17,21 +18,20 @@ class CmdSetEnvironmentToScene(TalemateCommand):
 
     async def run(self):
         await asyncio.sleep(0)
-        
+
         player_character = self.scene.get_player_character()
-        
+
         if not player_character:
             self.system_message("No player character found")
             return True
-        
+
         self.scene.set_environment("scene")
-        
-        self.system_message(f"Game mode")
-        
+
+        emit("status", message="Switched to gameplay", status="info")
+
         raise RestartSceneLoop()
-    
-        
-        
+
+
 @register
 class CmdSetEnvironmentToCreative(TalemateCommand):
     """
@@ -43,8 +43,7 @@ class CmdSetEnvironmentToCreative(TalemateCommand):
     aliases = [""]
 
     async def run(self):
-        
         await asyncio.sleep(0)
         self.scene.set_environment("creative")
-        
+
         raise RestartSceneLoop()

src/talemate/commands/cmd_time_util.py

@@ -5,16 +5,18 @@ Commands to manage scene timescale
 import asyncio
 import logging
 
+import isodate
+
+import talemate.instance as instance
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
 from talemate.emit import wait_for_input
-import talemate.instance as instance
-import isodate
 
 __all__ = [
     "CmdAdvanceTime",
 ]
 
+
 @register
 class CmdAdvanceTime(TalemateCommand):
     """
@@ -29,20 +31,23 @@ class CmdAdvanceTime(TalemateCommand):
         if not self.args:
             self.emit("system", "You must specify an amount of time to advance")
             return
-        
-        
+
         narrator = instance.get_agent("narrator")
         narration_prompt = None
-        
+
         # if narrator has narrate_time_passage action enabled ask the user
         # for a prompt to guide the narration
-        
-        if narrator.actions["narrate_time_passage"].enabled and narrator.actions["narrate_time_passage"].config["ask_for_prompt"].value:
-            
-            narration_prompt = await wait_for_input("Enter a prompt to guide the time passage narration (or leave blank): ")
-        
+
+        if (
+            narrator.actions["narrate_time_passage"].enabled
+            and narrator.actions["narrate_time_passage"].config["ask_for_prompt"].value
+        ):
+            narration_prompt = await wait_for_input(
+                "Enter a prompt to guide the time passage narration (or leave blank): "
+            )
+
             if not narration_prompt.strip():
                 narration_prompt = None
-        
+
         world_state = instance.get_agent("world_state")
-        await world_state.advance_time(self.args[0], narration_prompt)
+        await world_state.advance_time(self.args[0], narration_prompt)

src/talemate/commands/cmd_tts.py

@@ -3,13 +3,14 @@ import logging
 
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.prompts.base import set_default_sectioning_handler
 from talemate.instance import get_agent
+from talemate.prompts.base import set_default_sectioning_handler
 
 __all__ = [
     "CmdTestTTS",
 ]
 
+
 @register
 class CmdTestTTS(TalemateCommand):
     """
@@ -22,12 +23,10 @@ class CmdTestTTS(TalemateCommand):
 
     async def run(self):
         tts_agent = get_agent("tts")
-        
+
         try:
             last_message = str(self.scene.history[-1])
         except IndexError:
             last_message = "Welcome to talemate!"
-        
-        
+
         await tts_agent.generate(last_message)
-        

src/talemate/commands/cmd_world_state.py

@@ -1,11 +1,14 @@
 import random
+
 import structlog
 
+import talemate.instance as instance
 from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
-from talemate.emit import wait_for_input, emit
+from talemate.emit import emit, wait_for_input
 from talemate.instance import get_agent
-import talemate.instance as instance
+from talemate.scene_message import NarratorMessage
+from talemate.status import LoadingStatus, set_loading
 
 log = structlog.get_logger("talemate.cmd.world_state")
 
@@ -20,6 +23,7 @@ __all__ = [
     "CmdSummarizeAndPin",
 ]
 
+
 @register
 class CmdWorldState(TalemateCommand):
     """
@@ -31,250 +35,321 @@ class CmdWorldState(TalemateCommand):
     aliases = ["ws"]
 
     async def run(self):
-        
         inline = self.args[0] == "inline" if self.args else False
         reset = self.args[0] == "reset" if self.args else False
-        
+
         if inline:
             await self.scene.world_state.request_update_inline()
             return True
-        
+
         if reset:
             self.scene.world_state.reset()
-        
+
         await self.scene.world_state.request_update()
 
+
 @register
 class CmdPersistCharacter(TalemateCommand):
-    
     """
     Will attempt to create an actual character from a currently non
     tracked character in the scene, by name.
-    
+
     Once persisted this character can then participate in the scene.
     """
-    
+
     name = "persist_character"
     description = "Persist a character by name"
     aliases = ["pc"]
-    
+
+    @set_loading("Generating character...", set_busy=False)
     async def run(self):
-        from talemate.tale_mate import Character, Actor
-        
+        from talemate.tale_mate import Actor, Character
+
         scene = self.scene
         world_state = instance.get_agent("world_state")
         creator = instance.get_agent("creator")
-        
+        narrator = instance.get_agent("narrator")
+
+        loading_status = LoadingStatus(3)
+
         if not len(self.args):
             characters = await world_state.identify_characters()
-            available_names = [character["name"] for character in characters.get("characters") if not scene.get_character(character["name"])]
-            
+            available_names = [
+                character["name"]
+                for character in characters.get("characters")
+                if not scene.get_character(character["name"])
+            ]
+
             if not len(available_names):
                 raise ValueError("No characters available to persist.")
-            
-            name = await wait_for_input("Which character would you like to persist?", data={
-                "input_type": "select",
-                "choices": available_names,
-                "multi_select": False,  
-            })
+
+            name = await wait_for_input(
+                "Which character would you like to persist?",
+                data={
+                    "input_type": "select",
+                    "choices": available_names,
+                    "multi_select": False,
+                },
+            )
         else:
             name = self.args[0]
-            
-        scene.log.debug("persist_character", name=name)
-        
+
+        extra_instructions = None
+        if name == "prompt":
+            name = await wait_for_input("What is the name of the character?")
+            description = await wait_for_input(
+                f"Brief description for {name} (or leave blank):"
+            )
+            if description.strip():
+                extra_instructions = f"Name: {name}\nBrief Description: {description}"
+
+        never_narrate = len(self.args) > 1 and self.args[1] == "no"
+
+        if not never_narrate:
+            is_present = await world_state.is_character_present(name)
+            log.debug(
+                "persist_character",
+                name=name,
+                is_present=is_present,
+                never_narrate=never_narrate,
+            )
+        else:
+            is_present = False
+            log.debug("persist_character", name=name, never_narrate=never_narrate)
+
         character = Character(name=name)
-        character.color = random.choice(['#F08080', '#FFD700', '#90EE90', '#ADD8E6', '#DDA0DD', '#FFB6C1', '#FAFAD2', '#D3D3D3', '#B0E0E6', '#FFDEAD'])
-        
-        attributes = await world_state.extract_character_sheet(name=name)
+        character.color = random.choice(
+            [
+                "#F08080",
+                "#FFD700",
+                "#90EE90",
+                "#ADD8E6",
+                "#DDA0DD",
+                "#FFB6C1",
+                "#FAFAD2",
+                "#D3D3D3",
+                "#B0E0E6",
+                "#FFDEAD",
+            ]
+        )
+
+        loading_status("Generating character attributes...")
+
+        attributes = await world_state.extract_character_sheet(
+            name=name, text=extra_instructions
+        )
         scene.log.debug("persist_character", attributes=attributes)
-        
+
         character.base_attributes = attributes
-        
+
+        loading_status("Generating character description...")
+
         description = await creator.determine_character_description(character)
-        
+
         character.description = description
-        
+
         scene.log.debug("persist_character", description=description)
-        
+
         actor = Actor(character=character, agent=instance.get_agent("conversation"))
-        
+
         await scene.add_actor(actor)
-        
-        self.emit("system", f"Added character {name} to the scene.")
-        
+
+        emit(
+            "status", message=f"Added character {name} to the scene.", status="success"
+        )
+
+        # write narrative for the character entering the scene
+        if not is_present and not never_narrate:
+            loading_status("Narrating character entrance...")
+            entry_narration = await narrator.narrate_character_entry(
+                character, direction=extra_instructions
+            )
+            message = NarratorMessage(
+                entry_narration, source=f"narrate_character_entry:{character.name}"
+            )
+            self.narrator_message(message)
+            self.scene.push_history(message)
+
         scene.emit_status()
-        
+        scene.world_state.emit()
+
+
 @register
 class CmdAddReinforcement(TalemateCommand):
-    
     """
     Will attempt to create an actual character from a currently non
     tracked character in the scene, by name.
-    
+
     Once persisted this character can then participate in the scene.
     """
-    
+
     name = "add_reinforcement"
     description = "Add a reinforcement to the world state"
     aliases = ["ws_ar"]
-    
+
     async def run(self):
-        
         scene = self.scene
-        
+
         world_state = scene.world_state
-        
+
         if not len(self.args):
             question = await wait_for_input("Ask reinforcement question")
         else:
             question = self.args[0]
-        
+
         await world_state.add_reinforcement(question)
-        
-        
+
+
 @register
 class CmdRemoveReinforcement(TalemateCommand):
-    
     """
     Will attempt to create an actual character from a currently non
     tracked character in the scene, by name.
-    
+
     Once persisted this character can then participate in the scene.
     """
-    
+
     name = "remove_reinforcement"
     description = "Remove a reinforcement from the world state"
     aliases = ["ws_rr"]
-    
+
     async def run(self):
-        
         scene = self.scene
-        
+
         world_state = scene.world_state
-        
+
         if not len(self.args):
             question = await wait_for_input("Ask reinforcement question")
         else:
             question = self.args[0]
-            
+
         idx, reinforcement = await world_state.find_reinforcement(question)
-        
+
         if idx is None:
             raise ValueError(f"Reinforcement {question} not found.")
-        
+
         await world_state.remove_reinforcement(idx)
-        
+
+
 @register
 class CmdUpdateReinforcements(TalemateCommand):
-    
     """
     Will attempt to create an actual character from a currently non
     tracked character in the scene, by name.
-    
+
     Once persisted this character can then participate in the scene.
     """
-    
+
     name = "update_reinforcements"
     description = "Update the reinforcements in the world state"
     aliases = ["ws_ur"]
-    
+
     async def run(self):
-        
         scene = self.scene
-        
+
         world_state = get_agent("world_state")
-        
+
         await world_state.update_reinforcements(force=True)
-        
+
+
 @register
 class CmdCheckPinConditions(TalemateCommand):
-    
     """
     Will attempt to create an actual character from a currently non
     tracked character in the scene, by name.
-    
+
     Once persisted this character can then participate in the scene.
     """
-    
+
     name = "check_pin_conditions"
     description = "Check the pin conditions in the world state"
     aliases = ["ws_cpc"]
-    
+
     async def run(self):
         world_state = get_agent("world_state")
         await world_state.check_pin_conditions()
-        
+
+
 @register
 class CmdApplyWorldStateTemplate(TalemateCommand):
-    
     """
     Will apply a world state template setting up
     automatic state tracking.
     """
-    
+
     name = "apply_world_state_template"
     description = "Apply a world state template, creating an auto state reinforcement."
     aliases = ["ws_awst"]
     label = "Add state"
-    
+
     async def run(self):
-        
         scene = self.scene
-        
+
         if not len(self.args):
             raise ValueError("No template name provided.")
-        
+
         template_name = self.args[0]
         template_type = self.args[1] if len(self.args) > 1 else None
-        
+
         character_name = self.args[2] if len(self.args) > 2 else None
-        
+
         templates = await self.scene.world_state_manager.get_templates()
-        
+
         try:
-            template = getattr(templates,template_type)[template_name]
+            template = getattr(templates, template_type)[template_name]
         except KeyError:
             raise ValueError(f"Template {template_name} not found.")
-        
-        reinforcement = await scene.world_state_manager.apply_template_state_reinforcement(
-            template, character_name=character_name, run_immediately=True
+
+        reinforcement = (
+            await scene.world_state_manager.apply_template_state_reinforcement(
+                template, character_name=character_name, run_immediately=True
+            )
         )
-        
+
         response_data = {
             "template_name": template_name,
             "template_type": template_type,
             "reinforcement": reinforcement.model_dump() if reinforcement else None,
             "character_name": character_name,
         }
-        
+
         if reinforcement is None:
-            emit("status", message="State already tracked.", status="info", data=response_data)
+            emit(
+                "status",
+                message="State already tracked.",
+                status="info",
+                data=response_data,
+            )
         else:
-            emit("status", message="Auto state added.", status="success", data=response_data)
+            emit(
+                "status",
+                message="Auto state added.",
+                status="success",
+                data=response_data,
+            )
+
 
 @register
 class CmdSummarizeAndPin(TalemateCommand):
-    
     """
     Will take a message index and then walk back N messages
     summarizing the scene and pinning it to the context.
     """
-    
+
     name = "summarize_and_pin"
     label = "Summarize and pin"
     description = "Summarize a snapshot of the scene and pin it to the world state"
     aliases = ["ws_sap"]
-    
+
     async def run(self):
-        
         scene = self.scene
-        
+
         world_state = get_agent("world_state")
-        
+
         if not self.scene.history:
             raise ValueError("No history to summarize.")
-        
+
         message_id = int(self.args[0]) if len(self.args) else scene.history[-1].id
-        num_messages = int(self.args[1]) if len(self.args) > 1 else 3
-        
-        await world_state.summarize_and_pin(message_id, num_messages=num_messages)
+        num_messages = int(self.args[1]) if len(self.args) > 1 else 5
+
+        await world_state.summarize_and_pin(message_id, num_messages=num_messages)

src/talemate/commands/manager.py

@@ -1,8 +1,10 @@
-from talemate.emit import Emitter, AbortCommand
 import structlog
 
+from talemate.emit import AbortCommand, Emitter
+
 log = structlog.get_logger("talemate.commands.manager")
 
+
 class Manager(Emitter):
     """
     TaleMateCommand class to handle user command
@@ -38,7 +40,7 @@ class Manager(Emitter):
         cmd_args = ""
         if not self.is_command(cmd):
             return False
-        
+
         if ":" in cmd:
             # split command name and args which are separated by a colon
             cmd_name, cmd_args = cmd[1:].split(":", 1)
@@ -46,7 +48,7 @@ class Manager(Emitter):
         else:
             cmd_name = cmd[1:]
             cmd_args = []
-            
+
         for command_cls in self.command_classes:
             if command_cls.is_command(cmd_name):
                 command = command_cls(self, *cmd_args)