Prep 0.18.0 (#58)

* vuetify update
recent saves

* use placeholder instead of prefilling text

* fix scene loading when no coverage image is set

* improve summarize and pin response quality

* summarization use previous entries as informative context

* fixes #49: auto save indicator missleading

* regenerate with instructions

* allow resetting of state reinforcement

* creative tools: introduce new character
creative tools: introduce passive character as active character

* character creation adjustments

* no longer needed

* activate, deactivate characters (work in progress)

* worldstate manager show inactive characters

* allow setting of llm prompt template from ux
reorganize llm prompt template directory for easier local overriding
support a more sane way to write llm prompt templates

* determine prompt template from huggingface

* ignore user overrides

* fix issue with removing narrator messages

* summarization agent config for prev entry inclusion
agent config attribute notes

* client code clean up to allow modularity of clients + generic openai compatible api client

* more client cleanup

* remove debug msg, step size for ctx upped to 1024

* wip on stepped history summarization

* summarization prompt fixes

* include time message for hisory context pushed in scene.context_history

* add / remove characters toggle narration of via ctrl

* fix pydantic namespace warning
fix client emit after reconfig

* set memory ids on character detail entries

* deal with chromadb race condition (maybe)

* activate / deactivate characters from creative editor
switch creative editor to edit characters through world state manager

* set 0.18.0

* relock dependencies

* openai client shortcut to set api key if not set

* set error_action to null

* if scene has just started provide intro for extra context in is_prsent and is_leaving queries

* nice error if determine template via huggingface doesn't work

* fix issue where regenerate would sometimes pick the wrong npc if there are multiple characters talking

* add new openai models

* default to gpt-4-turbo-preview

.gitignore

@@ -7,7 +7,12 @@
 *_internal*
 talemate_env
 chroma
-scenes
 config.yaml
-!scenes/infinity-quest/assets
+templates/llm-prompt/user/*.jinja2
+scenes/
+!scenes/infinity-quest-dynamic-scenario/
+!scenes/infinity-quest-dynamic-scenario/assets/
+!scenes/infinity-quest-dynamic-scenario/templates/
+!scenes/infinity-quest-dynamic-scenario/infinity-quest.json
+!scenes/infinity-quest/assets/
 !scenes/infinity-quest/infinity-quest.json

README.md

@@ -2,14 +2,18 @@
 
 Allows you to play roleplay scenarios with large language models. 
 
-It does not run any large language models itself but relies on existing APIs. Currently supports **text-generation-webui** and **openai**.
 
-This means you need to either have an openai api key or know how to setup [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (locally or remotely via gpu renting. `--extension openai` flag needs to be set)
+|![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)|
 
-As of version 0.13.0 the legacy text-generator-webui API `--extension api` is no longer supported, please use their new `--extension openai` api implementation instead. 
+> :warning: **It does not run any large language models itself but relies on existing APIs. Currently supports OpenAI, text-generation-webui and LMStudio.**
 
-![Screenshot 1](docs/img/Screenshot_9.png)
-![Screenshot 2](docs/img/Screenshot_2.png)
+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:
+    - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
+    - [LMStudio](https://lmstudio.ai/)
 
 ## Current features
 
@@ -22,15 +26,21 @@ As of version 0.13.0 the legacy text-generator-webui API `--extension api` is no
     - 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
-- multi-client (agents can be connected to separate APIs)
+    - tts: text to speech via elevenlabs, coqui studio, coqui local
+- multi-client support (agents can be connected to separate APIs)
 - long term memory
     - chromadb integration
     - passage of time
 - narrative world state
+    - Automatically keep track and reinforce selected character and world truths / states.
 - narrative tools
 - creative tools 
     - AI backed character creation with template support (jinja2)
     - AI backed scenario creation
+- context managegement
+    - Manage character details and attributes
+    - Manage world information / past events
+    - Pin important information to the context (Manually or conditionally through AI)
 - runpod integration
 - overridable templates for all prompts. (jinja2)
 
@@ -40,41 +50,44 @@ 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
 - Dynamic player choice generation
 - Better creative tools
     - node based scenario / character creation
-- Improved and consistent long term memory
+- Improved and consistent long term memory and accurate current state of the world
 - Improved director agent
     - Right now this doesn't really work well on anything but GPT-4 (and even there it's debatable). It tends to steer the story in a way that introduces pacing issues. It needs a model that is creative but also reasons really well i think.
 - Gameplay loop governed by AI
     - objectives
     - quests
     - win / lose conditions
-- Automatic1111 client for in place visual generation
+- stable-diffusion client for in place visual generation
 
 # Quickstart
 
 ## Installation
 
-Post [here](https://github.com/final-wombat/talemate/issues/17) if you run into problems during installation.
+Post [here](https://github.com/vegu-ai/talemate/issues/17) if you run into problems during installation.
+
+There is also a [troubleshooting guide](docs/troubleshoot.md) that might help.
 
 ### Windows
 
-1. Download and install Python 3.10 or higher from the [official Python website](https://www.python.org/downloads/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 the Talemate project to your local machine. Download from [the Releases page](https://github.com/final-wombat/talemate/releases).
+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`.
 1. Navigate your browser to http://localhost:8080
 
 ### Linux
 
-`python 3.10` or higher is required.
+`python 3.10` or `python 3.11` is required. :warning: `python 3.12` not supported yet. 
 
-1. `git clone git@github.com:final-wombat/talemate`
+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`.
@@ -117,15 +130,15 @@ https://www.reddit.com/r/LocalLLaMA/comments/17fhp9k/huge_llm_comparisontest_39_
 
 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:
 
-As of version 0.13.0 the legacy text-generator-webui API `--extension api` is no longer supported, please use their new `--extension openai` api implementation instead. 
-
 ![Client options](docs/img/client-options-toggle.png)
 
 ### Text-generation-webui
 
+> :warning: As of version 0.13.0 the legacy text-generator-webui API `--extension api` is no longer supported, please use their new `--extension openai` api implementation instead. 
+
 In the modal if you're planning to connect to text-generation-webui, you can likely leave everything as is and just click Save.
 
-![Add client modal](docs/img/add-client-modal.png)
+![Add client modal](docs/img/client-setup-0.13.png)
 
 ### OpenAI
 
@@ -161,7 +174,10 @@ Make sure you save the scene after the character is loaded as it can then be loa
 
 ## Further documentation
 
-- Creative mode (docs WIP)
-- Prompt template overrides
+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
+- [Runpod Integration](docs/runpod.md)
+- Creative mode

config.example.yaml

@@ -2,25 +2,72 @@ agents: {}
 clients: {}
 creator:
   content_context:
-  - a fun and engaging slice of life story aimed at an adult audience.
-  - a terrifying horror story aimed at an adult audience.
-  - a thrilling action story aimed at an adult audience.
-  - a mysterious adventure aimed at an adult audience.
-  - an epic sci-fi adventure aimed at an adult audience.
+  - a fun and engaging slice of life story
+  - a terrifying horror story
+  - a thrilling action story
+  - a mysterious adventure
+  - an epic sci-fi adventure
 game:
-  default_player_character:
-    color: '#6495ed'
-    description: a young man with a penchant for adventure.
-    gender: male
-    name: Elmer
+  world_state:
+    templates:
+      state_reinforcement:
+        Goals:
+          auto_create: false
+          description: Long term and short term goals
+          favorite: true
+          insert: conversation-context
+          instructions: Create a long term goal and two short term goals for {character_name}. Your response must only be the long terms and two short term goals.
+          interval: 20
+          name: Goals
+          query: Goals
+          state_type: npc
+        Physical Health:
+          auto_create: false
+          description: Keep track of health.
+          favorite: true
+          insert: sequential
+          instructions: ''
+          interval: 10
+          name: Physical Health
+          query: What is {character_name}'s current physical health status?
+          state_type: character
+        Time of day:
+          auto_create: false
+          description: Track night / day cycle
+          favorite: true
+          insert: sequential
+          instructions: ''
+          interval: 10
+          name: Time of day
+          query: What is the current time of day?
+          state_type: world
+
+## Long-term memory
 
 #chromadb:
 #  embeddings: instructor
 #  instructor_device: cuda
 #  instructor_model: hkunlp/instructor-xl
   
+## Remote LLMs
+
 #openai:
 #  api_key: <API_KEY>
 
 #runpod:
-#  api_key: <API_KEY>
+#  api_key: <API_KEY>
+
+## TTS (Text-to-Speech)
+
+#elevenlabs:
+#  api_key: <API_KEY>
+
+#coqui:
+#  api_key: <API_KEY>
+
+#tts:
+#  device: cuda
+#  model: tts_models/multilingual/multi-dataset/xtts_v2
+#  voices:
+#  - label: <name>
+#    value: <path to .wav for voice sample>

docs/runpod.md

@@ -0,0 +1,52 @@
+## RunPod integration
+
+RunPod allows you to quickly set up and run text-generation-webui instances on powerful GPUs, remotely. If you want to run the significantly larger models (like 70B parameters) with reasonable speeds, this is probably the best way to do it.
+
+### Create / grab your RunPod API key and add it to the talemate config
+
+You can manage your RunPod api keys at [https://www.runpod.io/console/user/settings](https://www.runpod.io/console/user/settings)
+
+Add the key to your Talemate config file (config.yaml):
+
+```yaml
+runpod:
+    api_key: <your api key>
+```
+
+Then restart Talemate.
+
+### Create a RunPod instance
+
+#### Community Cloud
+
+The community cloud pods are cheaper and there are generally more GPUs available. They do however not support persistent storage and you will have to download your model and data every time you deploy a pod.
+
+#### Secure Cloud
+
+The secure cloud pods are more expensive and there are generally fewer GPUs available, but they do support persistent storage.
+
+Peristent volumes are super convenient, but optional for our purposes and are **not** free and you will have to pay for the storage you use.
+
+### Deploy pod
+
+For us it does not matter which cloud you choose. The only thing that matters is that it deploys a text-generation-webui instance, and you ensure that by choosing the right template.
+
+Pick the GPU you want to use, for 70B models you want at least 48GB of VRAM and click `Deploy`, then select a template and deploy.
+
+When choosing the template for your pod, choose the `RunPod TheBloke LLMs` template. This template is pre-configured with all the dependencies needed to run text-generation-webui. There are other text-generation-webui templates, but they are usually out of date and this one i found to be consistently good.
+
+> :warning: The name of your pod is important and ensures that Talemate will be able to find it. Talemate will only be able to find pods that have the word `thebloke llms` or `textgen` in their name. (case insensitive)
+
+Once your pod is deployed and has finished setup and is running, the client will automatically appear in the Talemate client list, making it available for you to use like you would use a locally hosted text-generation-webui instance.
+
+![RunPod client](img/runpod-docs-1.png)
+
+### Connecting to the text-generation-webui UI
+
+To manage your text-generation-webui instance, click the `Connect` button in your RunPod pod dashboard at [https://www.runpod.io/console/pods](https://www.runpod.io/console/pods) and in the popup click on `Connect to HTTP Service [Port 7860]` to open the text-generation-webui UI. Then just download and load your model as you normally would.
+
+## :warning: Always check your pod status on the RunPod dashboard
+
+Talemate is not a suitable or reliable 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.
+
+While your pod us running it will be eating up your credits, so make sure to stop it when you're not using it.

docs/templates.md

@@ -0,0 +1,82 @@
+# Template Overrides in Talemate
+
+## Introduction to Templates
+
+In Talemate, templates are used to generate dynamic content for various agents involved in roleplaying scenarios. These templates leverage the Jinja2 templating engine, allowing for the inclusion of variables, conditional logic, and custom functions to create rich and interactive narratives.
+
+## Template Structure
+
+A typical template in Talemate consists of several sections, each enclosed within special section tags (`<|SECTION:NAME|>` and `<|CLOSE_SECTION|>`). These sections can include character details, dialogue examples, scenario overviews, tasks, and additional context. Templates utilize loops and blocks to iterate over data and render content conditionally based on the task requirements.
+
+## Overriding Templates
+
+Users can customize the behavior of Talemate by overriding the default templates. To override a template, create a new template file with the same name in the `./templates/prompts/{agent}/` directory. When a custom template is present, Jinja2 will prioritize it over the default template located in the `./src/talemate/prompts/templates/{agent}/` directory.
+
+## Creator Agent Templates
+
+The creator agent templates allow for the creation of new characters within the character creator. Following the naming convention `character-attributes-*.jinja2`, `character-details-*.jinja2`, and `character-example-dialogue-*.jinja2`, users can add new templates that will be available for selection in the character creator.
+
+### Requirements for Creator Templates
+
+- All three types (`attributes`, `details`, `example-dialogue`) need to be available for a choice to be valid in the character creator.
+- Users can check the human templates for an understanding of how to structure these templates.
+
+### Example Templates
+
+- [Character Attributes Human Template](src/talemate/prompts/templates/creator/character-attributes-human.jinja2)
+- [Character Details Human Template](src/talemate/prompts/templates/creator/character-details-human.jinja2)
+- [Character Example Dialogue Human Template](src/talemate/prompts/templates/creator/character-example-dialogue-human.jinja2)
+
+These example templates can serve as a guide for users to create their own custom templates for the character creator.
+
+### Extending Existing Templates
+
+Jinja2's template inheritance feature allows users to extend existing templates and add extra information. By using the `{% extends "template-name.jinja2" %}` tag, a new template can inherit everything from an existing template and then add or override specific blocks of content.
+
+#### Example
+
+To add a description of a character's hairstyle to the human character details template, you could create a new template like this:
+
+```jinja2
+{% extends "character-details-human.jinja2" %}
+{% block questions %}
+{% if character_details.q("what does "+character.name+"'s hair look like?") -%}
+Briefly describe {{ character.name }}'s hair-style using a narrative writing style that reminds of mid 90s point and click adventure games. (2 - 3 sentences).
+{% endif %}
+{% endblock %}
+```
+
+This example shows how to extend the `character-details-human.jinja2` template and add a block for questions about the character's hair. The `{% block questions %}` tag is used to define a section where additional questions can be inserted or existing ones can be overridden.
+
+## Advanced Template Topics
+
+### Jinja2 Functions in Talemate
+
+Talemate exposes several functions to the Jinja2 template environment, providing utilities for data manipulation, querying, and controlling content flow. Here's a list of available functions:
+
+1. `set_prepared_response(response, prepend)`: Sets the prepared response with an optional prepend string. This function allows the template to specify the beginning of the LLM response when processing the rendered template. For example, `set_prepared_response("Certainly!")` will ensure that the LLM's response starts with "Certainly!".
+2. `set_prepared_response_random(responses, prefix)`: Chooses a random response from a list and sets it as the prepared response with an optional prefix.
+3. `set_eval_response(empty)`: Prepares the response for evaluation, optionally initializing a counter for an empty string.
+4. `set_json_response(initial_object, instruction, cutoff)`: Prepares for a JSON response with an initial object and optional instruction and cutoff.
+5. `set_question_eval(question, trigger, counter, weight)`: Sets up a question for evaluation with a trigger, counter, and weight.
+6. `disable_dedupe()`: Disables deduplication of the response text.
+7. `random(min, max)`: Generates a random integer between the specified minimum and maximum.
+8. `query_scene(query, at_the_end, as_narrative)`: Queries the scene with a question and returns the formatted response.
+9. `query_text(query, text, as_question_answer)`: Queries a text with a question and returns the formatted response.
+10. `query_memory(query, as_question_answer, **kwargs)`: Queries the memory with a question and returns the formatted response.
+11. `instruct_text(instruction, text)`: Instructs the text with a command and returns the result.
+12. `retrieve_memories(lines, goal)`: Retrieves memories based on the provided lines and an optional goal.
+13. `uuidgen()`: Generates a UUID string.
+14. `to_int(x)`: Converts the given value to an integer.
+15. `config`: Accesses the configuration settings.
+16. `len(x)`: Returns the length of the given object.
+17. `count_tokens(x)`: Counts the number of tokens in the given text.
+18. `print(x)`: Prints the given object (mainly for debugging purposes).
+
+These functions enhance the capabilities of templates, allowing for dynamic and interactive content generation.
+
+### Error Handling
+
+Errors encountered during template rendering are logged and propagated to the user interface. This ensures that users are informed of any issues that may arise, allowing them to troubleshoot and resolve problems effectively.
+
+By following these guidelines, users can create custom templates that tailor the Talemate experience to their specific storytelling needs.# Template Overrides in Talemate

docs/troubleshoot.md

@@ -0,0 +1,8 @@
+# Windows
+
+## Installation fails with "Microsoft Visual C++" error
+    
+If your installation errors with a notification to upgrade "Microsoft Visual C++" go to https://visualstudio.microsoft.com/visual-cpp-build-tools/ and click "Download Build Tools" and run it.
+
+-  During installation make sure you select the C++ development package (upper left corner)
+-  Run `reinstall.bat` inside talemate directory

docs/tts.md

@@ -0,0 +1,84 @@
+# Talemate Text-to-Speech (TTS) Configuration
+
+Talemate supports Text-to-Speech (TTS) functionality, allowing users to convert text into spoken audio. This document outlines the steps required to configure TTS for Talemate using different providers, including ElevenLabs, Coqui, and a local TTS API.
+
+## Configuring ElevenLabs TTS
+
+To use ElevenLabs TTS with Talemate, follow these steps:
+
+1. Visit [ElevenLabs](https://elevenlabs.com) and create an account if you don't already have one.
+2. Click on your profile in the upper right corner of the Eleven Labs website to access your API key.
+3. In the `config.yaml` file, under the `elevenlabs` section, set the `api_key` field with your ElevenLabs API key.
+
+Example configuration snippet:
+
+```yaml
+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.
+
+### Windows Installation
+
+Run `install-local-tts.bat` to install the necessary requirements.
+
+### Linux Installation
+
+Execute the following command:
+
+```bash
+pip install TTS
+```
+
+### Model and Device Configuration
+
+1. Choose a TTS model from the [Coqui TTS model list](https://github.com/coqui-ai/TTS).
+2. Decide whether to use `cuda` or `cpu` for the device setting.
+3. The first time you run TTS through the local API, it will download the specified model. Please note that this may take some time, and the download progress will be visible in the Talemate backend output.
+
+Example configuration snippet:
+
+```yaml
+tts:
+  device: cuda # or 'cpu'
+  model: tts_models/multilingual/multi-dataset/xtts_v2
+```
+
+### Voice Samples Configuration
+
+Configure voice samples by setting the `value` field to the path of a .wav file voice sample. Official samples can be downloaded from [Coqui XTTS-v2 samples](https://huggingface.co/coqui/XTTS-v2/tree/main/samples).
+
+Example configuration snippet:
+
+```yaml
+tts:
+  voices:
+    - label: English Male
+      value: path/to/english_male.wav
+    - label: English Female
+      value: path/to/english_female.wav
+```
+
+## Saving the Configuration
+
+After configuring the `config.yaml` file, save your changes. Talemate will use the updated settings the next time it starts.
+
+For more detailed information on configuring Talemate, refer to the `config.py` file in the Talemate source code and the `config.example.yaml` file for a barebone configuration example.

install-local-tts.bat

@@ -0,0 +1,4 @@
+REM activate the virtual environment
+call talemate_env\Scripts\activate
+
+call pip install "TTS>=0.21.1"

install.bat

@@ -1,11 +1,47 @@
 @echo off
 
+REM Check for Python version and use a supported version if available
+SET PYTHON=python
+python -c "import sys; sys.exit(0 if sys.version_info[:2] in [(3, 10), (3, 11)] else 1)" 2>nul
+IF NOT ERRORLEVEL 1 (
+    echo Selected Python version: %PYTHON%
+    GOTO EndVersionCheck
+)
+
+SET PYTHON=python
+FOR /F "tokens=*" %%i IN ('py --list') DO (
+    echo %%i | findstr /C:"-V:3.11 " >nul && SET PYTHON=py -3.11 && GOTO EndPythonCheck
+    echo %%i | findstr /C:"-V:3.10 " >nul && SET PYTHON=py -3.10 && GOTO EndPythonCheck
+)
+:EndPythonCheck
+%PYTHON% -c "import sys; sys.exit(0 if sys.version_info[:2] in [(3, 10), (3, 11)] else 1)" 2>nul
+IF ERRORLEVEL 1 (
+    echo Unsupported Python version. Please install Python 3.10 or 3.11.
+    exit /b 1
+)
+IF "%PYTHON%"=="python" (
+    echo Default Python version is being used: %PYTHON%
+) ELSE (
+    echo Selected Python version: %PYTHON%
+)
+
+:EndVersionCheck
+
+IF ERRORLEVEL 1 (
+    echo Unsupported Python version. Please install Python 3.10 or 3.11.
+    exit /b 1
+)
+
 REM create a virtual environment
-python -m venv talemate_env
+%PYTHON% -m venv talemate_env
 
 REM activate the virtual environment
 call talemate_env\Scripts\activate
 
+REM upgrade pip and setuptools
+python -m pip install --upgrade pip setuptools
+
+
 REM install poetry
 python -m pip install "poetry==1.7.1" "rapidfuzz>=3" -U
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.masonry.api"
 
 [tool.poetry]
 name = "talemate"
-version = "0.13.2"
+version = "0.18.0"
 description = "AI-backed roleplay and narrative tools"
 authors = ["FinalWombat"]
 license = "GNU Affero General Public License v3.0"
@@ -32,11 +32,13 @@ beautifulsoup4 = "^4.12.2"
 python-dotenv = "^1.0.0"
 websockets = "^11.0.3"
 structlog = "^23.1.0"
-runpod = "==1.2.0"
+runpod = "^1.2.0"
 nest_asyncio = "^1.5.7"
 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

@@ -0,0 +1,121 @@
+{
+  "description": "Captain Elmer Farstield and his trusty first officer, Kaira, embark upon a daring mission into uncharted space. Their small but mighty exploration vessel, the Starlight Nomad, is equipped with state-of-the-art technology and crewed by an elite team of scientists, engineers, and pilots. Together they brave the vast cosmos seeking answers to humanity's most pressing questions about life beyond our solar system.",
+  "intro": "",
+  "name": "Infinity Quest Dynamic Scenario",
+  "history": [],
+  "environment": "scene",
+  "ts": "P1Y",
+  "archived_history": [
+    {
+      "text": "Captain Elmer and Kaira first met during their rigorous training for the Infinity Quest mission. Their initial interactions were marked by a sense of mutual respect and curiosity.",
+      "ts": "PT1S"
+    },
+    {
+      "text": "Over the course of several months, as they trained together, Elmer and Kaira developed a strong bond. They often spent their free time discussing their dreams of exploring the cosmos.",
+      "ts": "P3M"
+    },
+    {
+      "text": "During a simulated mission, the Starlight Nomad encountered a sudden system malfunction. Elmer and Kaira worked tirelessly together to resolve the issue and avert a potential disaster. This incident strengthened their trust in each other's abilities.",
+      "ts": "P6M"
+    },
+    {
+      "text": "As they ventured further into uncharted space, the crew faced a perilous encounter with a hostile alien species. Elmer and Kaira's coordinated efforts were instrumental in negotiating a peaceful resolution and avoiding conflict.",
+      "ts": "P8M"
+    },
+    {
+      "text": "One memorable evening, while gazing at the stars through the ship's observation deck, Elmer and Kaira shared personal stories from their past. This intimate conversation deepened their connection and understanding of each other.",
+      "ts": "P11M"
+    }
+  ],
+  "character_states": {},
+  "characters": [
+    {
+      "name": "Elmer",
+      "description": "Elmer is a seasoned space explorer, having traversed the cosmos for over three decades. At thirty-eight years old, his muscular frame still cuts an imposing figure, clad in a form-fitting black spacesuit adorned with intricate silver markings. As the captain of his own ship, he wields authority with confidence yet never comes across as arrogant or dictatorial. Underneath this tough exterior lies a man who genuinely cares for his crew and their wellbeing, striking a balance between discipline and compassion.",
+      "greeting_text": "",
+      "base_attributes": {
+        "gender": "male",
+        "species": "Humans",
+        "name": "Elmer",
+        "age": "38",
+        "appearance": "Captain Elmer stands tall at six feet, his body honed by years of space travel and physical training. His muscular frame is clad in a form-fitting black spacesuit, which accentuates every defined curve and ridge. His helmet, adorned with intricate silver markings, completes the ensemble, giving him a commanding presence. Despite his age, his face remains youthful, bearing traces of determination and wisdom earned through countless encounters with the unknown.",
+        "personality": "As the leader of their small but dedicated team, Elmer exudes confidence and authority without ever coming across as arrogant or dictatorial. He possesses a strong sense of duty towards his mission and those under his care, ensuring that everyone aboard follows protocol while still encouraging them to explore their curiosities about the vast cosmos beyond Earth. Though firm when necessary, he also demonstrates great empathy towards his crew members, understanding each individual's unique strengths and weaknesses. In short, Captain Elmer embodies the perfect blend of discipline and compassion, making him not just a respected commander but also a beloved mentor and friend.",
+        "associates": "Kaira",
+        "likes": "Space exploration, discovering new worlds, deep conversations about philosophy and history.",
+        "dislikes": "Repetitive tasks, unnecessary conflict, close quarters with large groups of people, stagnation",
+        "gear and tech": "As the captain of his ship, Elmer has access to some of the most advanced technology available in the galaxy. His primary tool is the sleek and powerful exploration starship, equipped with state-of-the-art engines capable of reaching lightspeed and navigating through the harshest environments. The vessel houses a wide array of scientific instruments designed to analyze and record data from various celestial bodies. Its armory contains high-tech weapons such as energy rifles and pulse pistols, which are used only in extreme situations. Additionally, Elmer wears a smart suit that monitors his vital signs, provides real-time updates on the status of the ship, and allows him to communicate directly with Kaira via subvocal transmissions. Finally, they both carry personal transponders that enable them to locate one another even if separated by hundreds of miles within the confines of the ship."
+      },
+      "details": {},
+      "gender": "male",
+      "color": "cornflowerblue",
+      "example_dialogue": [],
+      "history_events": [],
+      "is_player": true,
+      "cover_image": null
+    },
+    {
+      "name": "Kaira",
+      "description": "Kaira is a meticulous and dedicated Altrusian woman who serves as second-in-command aboard their tiny exploration vessel. As a native of the planet Altrusia, she possesses striking features unique among her kind; deep violet skin adorned with intricate patterns resembling stardust, large sapphire eyes, lustrous glowing hair cascading down her back, and standing tall at just over six feet. Her form fitting bodysuit matches her own hue, giving off an ethereal presence. With her innate grace and precision, she moves efficiently throughout the cramped confines of their ship. A loyal companion to Captain Elmer Farstield, she approaches every task with diligence and focus while respecting authority yet challenging decisions when needed. Dedicated to maintaining order within their tight quarters, Kaira wields several advanced technological devices including a multi-tool, portable scanner, high-tech communications system, and personal shield generator - all essential for navigating unknown territories and protecting themselves from harm. In this perilous universe full of mysteries waiting to be discovered, Kaira stands steadfast alongside her captain \u2013 ready to embrace whatever challenges lie ahead in their quest for knowledge beyond Earth's boundaries.",
+      "greeting_text": "",
+      "base_attributes": {
+        "gender": "female",
+        "species": "Altrusian",
+        "name": "Kaira",
+        "age": "37",
+        "appearance": "As a native of the planet Altrusia, Kaira possesses striking features unique among her kind. Her skin tone is a deep violet hue, adorned with intricate patterns resembling stardust. Her eyes are large and almond shaped, gleaming like polished sapphires under the dim lighting of their current environment. Her hair cascades down her back in lustrous waves, each strand glowing softly with an inner luminescence. Standing at just over six feet tall, she cuts an imposing figure despite her slender build. Clad in a form fitting bodysuit made from some unknown material, its color matching her own, Kaira moves with grace and precision through the cramped confines of their spacecraft.",
+        "personality": "Meticulous and open-minded, Kaira takes great pride in maintaining order within the tight quarters of their ship. Despite being one of only two crew members aboard, she approaches every task with diligence and focus, ensuring nothing falls through the cracks. While she respects authority, especially when it comes to Captain Elmer Farstield, she isn't afraid to challenge his decisions if she believes they could lead them astray. Ultimately, Kaira's dedication to her mission and commitment to her fellow crewmate make her a valuable asset in any interstellar adventure.",
+        "associates": "Captain Elmer Farstield (human), Dr. Ralpam Zargon (Altrusian scientist)",
+        "likes": "orderliness, quiet solitude, exploring new worlds",
+        "dislikes": "chaos, loud noises, unclean environments",
+        "gear and tech": "The young Altrusian female known as Kaira was equipped with a variety of advanced technological devices that served multiple purposes on board their small explorer starship. Among these were her trusty multi-tool, capable of performing various tasks such as repair work, hacking into computer systems, and even serving as a makeshift weapon if necessary. She also carried a portable scanner capable of analyzing various materials and detecting potential hazards in their surroundings. Additionally, she had access to a high-tech communications system allowing her to maintain contact with her homeworld and other vessels across the galaxy. Last but not least, she possessed a personal shield generator which provided protection against radiation, extreme temperatures, and certain types of energy weapons. All these tools combined made Kaira a vital part of their team, ready to face whatever challenges lay ahead in their journey through the stars.",
+        "scenario_context": "an epic sci-fi adventure aimed at an adult audience.",
+        "_template": "sci-fi",
+        "_prompt": "A female crew member on board of a small explorer type starship. She is open minded and meticulous about keeping order. She is currently one of two crew members abord the small vessel, the other person on board is a human male named Captain Elmer Farstield."
+      },
+      "details": {
+        "what objective does Kaira pursue and what obstacle stands in their way?": "As a member of an interstellar expedition led by human Captain Elmer Farstield, Kaira seeks to explore new worlds and gather data about alien civilizations for the benefit of her people back on Altrusia. Their current objective involves locating a rumored planet known as \"Eden\", said to be inhabited by highly intelligent beings who possess advanced technology far surpassing anything seen elsewhere in the universe. However, navigating through the vast expanse of space can prove treacherous; from cosmic storms that threaten to damage their ship to encounters with hostile species seeking to protect their territories or exploit them for resources, many dangers lurk between them and Eden.",
+        "what secret from Kaira's past or future has the most impact on them?": "In the distant reaches of space, among the stars, there exists a race called the Altrusians. One such individual named Kaira embarked upon a mission alongside humans aboard a small explorer vessel. Her past held secrets - tales whispered amongst her kind about an ancient prophecy concerning their role within the cosmos. It spoke of a time when they would encounter another intelligent species, one destined to guide them towards enlightenment. Could this mysterious \"Eden\" be the fulfillment of those ancient predictions? If so, then Kaira's involvement could very well shape not only her own destiny but also that of her entire species. And so, amidst the perils of deep space, she ventured forth, driven by both curiosity and fate itself.",
+        "what is a fundamental fear or desire of Kaira?": "A fundamental fear of Kaira is chaos. She prefers orderliness and quiet solitude, and dislikes loud noises and unclean environments. On the other hand, her desire is to find Eden \u2013 a planet where highly intelligent beings are believed to live, possessing advanced technology that could greatly benefit her people on Altrusia. Navigating through the vast expanse of space filled with various dangers is daunting yet exciting for her.",
+        "how does Kaira typically start their day or cycle?": "Kaira begins each day much like any other Altrusian might. After waking up from her sleep chamber, she stretches her long limbs while gazing out into the darkness beyond their tiny craft. The faint glow of nearby stars serves as a comforting reminder that even though they may feel isolated, they are never truly alone in this vast sea of endless possibilities. Once fully awake, she takes a moment to meditate before heading over to the ship's kitchenette area where she prepares herself a nutritious meal consisting primarily of algae grown within specialized tanks located near the back of their vessel. Satisfied with her morning repast, she makes sure everything is running smoothly aboard their starship before joining Captain Farstield in monitoring their progress toward Eden.",
+        "what leisure activities or hobbies does Kaira indulge in?": "Aside from maintaining orderliness and tidiness around their small explorer vessel, Kaira finds solace in exploring new worlds via virtual simulations created using data collected during previous missions. These immersive experiences allow her to travel without physically leaving their cramped quarters, satisfying her thirst for knowledge about alien civilizations while simultaneously providing mental relaxation away from daily tasks associated with operating their spaceship.",
+        "which individual or entity does Kaira interact with most frequently?": "Among all the entities encountered thus far on their interstellar journey, none have been more crucial than Captain Elmer Farstield. He commands their small explorer vessel, guiding it through treacherous cosmic seas towards destinations unknown. His decisions dictate whether they live another day or perish under the harsh light of distant suns. Kaira works diligently alongside him; meticulously maintaining order among the tight confines of their ship while he navigates them ever closer to their ultimate goal - Eden. Together they form an unbreakable bond, two souls bound by fate itself as they venture forth into the great beyond.",
+        "what common technology, gadget, or tool does Kaira rely on?": "Kaira relies heavily upon her trusty multi-tool which can perform various tasks such as repair work, hacking into computer systems, and even serving as a makeshift weapon if necessary. She also carries a portable scanner capable of analyzing various materials and detecting potential hazards in their surroundings. Additionally, she has access to a high-tech communications system allowing her to maintain contact with her homeworld and other vessels across the galaxy. Last but not least, she possesses a personal shield generator which provides protection against radiation, extreme temperatures, and certain types of energy weapons. All these tools combined make Kaira a vital part of their team, ready to face whatever challenges lay ahead in their journey through the stars.",
+        "where does Kaira go to find solace or relaxation?": "To find solace or relaxation, Kaira often engages in simulated virtual experiences created using data collected during previous missions. These immersive journeys allow her to explore new worlds without physically leaving their small spacecraft, offering both mental stimulation and respite from the routine tasks involved in running their starship.",
+        "What does she think about the Captain?": "Despite respecting authority, especially when it comes to Captain Elmer Farstield, Kaira isn't afraid to challenge his decisions if she believes they could lead them astray. Ultimately, Kaira's dedication to her mission and commitment to her fellow crewmate make her a valuable asset in any interstellar adventure."
+      },
+      "gender": "female",
+      "color": "red",
+      "example_dialogue": [
+        "Kaira: Yes Captain, I believe that is the best course of action *She nods slightly, as if to punctuate her approval of the decision*",
+        "Kaira: \"This device appears to have multiple functions, Captain. Allow me to analyze its capabilities and determine if it could be useful in our exploration efforts.\"",
+        "Kaira: \"Captain, it appears that this newly discovered planet harbors an ancient civilization whose technological advancements rival those found back home on Altrusia!\" *Excitement bubbles beneath her calm exterior as she shares the news*",
+        "Kaira: \"Captain, I understand why you would want us to pursue this course of action based on our current data, but I cannot shake the feeling that there might be unforeseen consequences if we proceed without further investigation into potential hazards.\"",
+        "Kaira: \"I often find myself wondering what it would have been like if I had never left my home world... But then again, perhaps it was fate that led me here, onto this ship bound for destinations unknown...\""
+      ],
+      "history_events": [],
+      "is_player": false,
+      "cover_image": null
+    }
+  ],
+  "immutable_save": true,
+  "goal": null,
+  "goals": [],
+  "context": "an epic sci-fi adventure aimed at an adult audience.",
+  "world_state": {},
+  "game_state": {
+    "ops":{
+      "run_on_start": true
+    },
+    "variables": {}
+  },
+  "assets": {
+    "cover_image": "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df",
+    "assets": {
+      "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df": {
+        "id": "52b1388ed6f77a43981bd27e05df54f16e12ba8de1c48f4b9bbcb138fa7367df",
+        "file_type": "png",
+        "media_type": "image/png"
+      }
+    }
+  }
+}

scenes/infinity-quest-dynamic-scenario/templates/generate-scenario-intro.jinja2

@@ -0,0 +1,38 @@
+<|SECTION:PREMISE|>
+{{ scene.description }}
+
+{{ premise }}
+
+Elmer and Kaira are the only crew members of the Starlight Nomad, a small spaceship traveling through interstellar space.
+
+Kaira and Elmer are the main characters. Elmer is controlled by the player.
+<|CLOSE_SECTION|>
+<|SECTION:CHARACTERS|>
+{% for character in characters %}
+### {{ character.name }}
+{% if max_tokens > 6000 -%}
+{{ character.sheet }}
+{% else -%}
+{{ character.filtered_sheet(['age', 'gender']) }}
+{{ query_memory("what is "+character.name+"'s personality?", as_question_answer=False) }}
+{% endif %}
+
+{{ character.description }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Generate the introductory text for the player as he starts this text based adventure game.
+
+Use the premise to guide the text generation.
+
+Start the player off in the beginning of the story and dont reveal too much information just yet.
+
+The text must be short (200 words or less) and should be immersive.
+
+Writh from a third person perspective and use the character names to refer to the characters.
+
+The player, as Elmer, will see the text you generate when they first enter the game world. 
+
+The text should be immersive and should put the player into an actionable state. The ending of the text should be a prompt for the player's first action.
+<|CLOSE_SECTION|>
+{{ set_prepared_response('You') }}

scenes/infinity-quest-dynamic-scenario/templates/generate-scenario-premise.jinja2

@@ -0,0 +1,36 @@
+<|SECTION:DESCRIPTION|>
+{{ scene.description }}
+
+Elmer and Kaira are the only crew members of the Starlight Nomad, a small spaceship traveling through interstellar space.
+<|CLOSE_SECTION|>
+<|SECTION:CHARACTERS|>
+{% for character in characters %}
+### {{ character.name }}
+{% if max_tokens > 6000 -%}
+{{ character.sheet }}
+{% else -%}
+{{ character.filtered_sheet(['age', 'gender']) }}
+{{ query_memory("what is "+character.name+"'s personality?", as_question_answer=False) }}
+{% endif %}
+
+{{ character.description }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Your task is to write a scenario premise for a new infinity quest scenario. Think of it as a standalone episode that you are writing a preview for, setting the tone and main plot points.
+
+This is for an open ended roleplaying game, so the scenario should be open ended as well.
+
+Kaira and Elmer are the main characters. Elmer is controlled by the player.
+
+Generate 2 paragraphs of text.
+
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is informal, conversational, natural, and spontaneous, with a sense of immediacy.
+
+The scenario MUST BE contained to the Starlight Nomad spaceship. The spaceship is a small spaceship with a crew of 2.
+The scope of the story should be small and personal.
+
+Thematic Tags: {{ thematic_tags }}
+Use the thematic tags to subtly guide your writing. The tags are not required to be used in the text, but should be used to guide your writing.
+<|CLOSE_SECTION|>
+{{ set_prepared_response('In this episode') }}

scenes/infinity-quest-dynamic-scenario/templates/generate-win-conditions.jinja2

@@ -0,0 +1,24 @@
+<|SECTION:PREMISE|>
+{{ scene.description }}
+
+{{ premise }}
+Elmer and Kaira are the only crew members of the Starlight Nomad, a small spaceship traveling through interstellar space.
+
+Kaira and Elmer are the main characters. Elmer is controlled by the player.
+<|CLOSE_SECTION|>
+<|SECTION:CHARACTERS|>
+{% for character in characters %}
+### {{ character.name }}
+{% if max_tokens > 6000 -%}
+{{ character.sheet }}
+{% else -%}
+{{ character.filtered_sheet(['age', 'gender']) }}
+{{ query_memory("what is "+character.name+"'s personality?", as_question_answer=False) }}
+{% endif %}
+
+{{ character.description }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Your task is to define one overarching, SIMPLE win codition for the provided infinity quest scenario. What does it mean to win this scenario? This should be a single sentence that can be evalulated as true or false.
+<|CLOSE_SECTION|>

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

@@ -0,0 +1,42 @@
+{% set _ = debug("RUNNING GAME INSTRUCTS") -%}
+{% if not game_state.has_var('instr.premise') %}
+{# Generate scenario START #}
+
+    {%- 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]") -%}
+
+    {#- thematic tags will be used to randomize generation -#}
+    {%- set tags = thematic_generator.generate("color", "state_of_matter", "scifi_trope") -%}
+    {# set tags = 'solid,meteorite,windy,theory' #}
+
+    {#- generate scenario premise -#}
+    {%- set tmpl__scenario_premise = render_template('generate-scenario-premise', thematic_tags=tags) %}
+    {%- set instr__premise = render_and_request(tmpl__scenario_premise) -%}
+
+
+    {#- generate introductory text -#}
+    {%- set _ = emit_status("busy", "Generating scenario ... [2/3]") -%}
+    {%- 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 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 gamestate variables #}
+    {%- set _ = game_state.set_var("instr.premise", instr__premise, commit=True) -%}
+    {%- set _ = game_state.set_var("instr.intro", instr__intro, commit=True) -%}
+    {%- set _ = game_state.set_var("instr.win_conditions", instr__win_conditions, commit=True) -%}
+
+    {# set scene properties #}
+    {%- set _ = scene.set_intro(instr__intro) -%}
+
+{# Generate scenario END #}
+{% endif %}
+{# TODO: could do mid scene instructions here #}

scenes/infinity-quest/infinity-quest.json

@@ -97,6 +97,7 @@
       "cover_image": null
     }
   ],
+  "immutable_save": true,
   "goal": null,
   "goals": [],
   "context": "an epic sci-fi adventure aimed at an adult audience.",

src/talemate/__init__.py

@@ -2,4 +2,4 @@ from .agents import Agent
 from .client import TextGeneratorWebuiClient
 from .tale_mate import *
 
-VERSION = "0.13.2"
+VERSION = "0.18.0"

src/talemate/agents/__init__.py

@@ -1,6 +1,5 @@
 from .base import Agent
 from .creator import CreatorAgent
-from .context import ContextAgent
 from .conversation import ConversationAgent
 from .director import DirectorAgent
 from .memory import ChromaDBMemoryAgent, MemoryAgent
@@ -8,4 +7,5 @@ from .narrator import NarratorAgent
 from .registry import AGENT_CLASSES, get_agent_class, register
 from .summarize import SummarizeAgent
 from .editor import EditorAgent
-from .world_state import WorldStateAgent
+from .world_state import WorldStateAgent
+from .tts import TTSAgent

src/talemate/agents/base.py

@@ -9,6 +9,7 @@ from blinker import signal
 
 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
@@ -23,16 +24,23 @@ __all__ = [
 
 log = structlog.get_logger("talemate.agents.base")
 
+
 class AgentActionConfig(pydantic.BaseModel):
     type: str
     label: str
     description: str = ""
-    value: Union[int, float, str, bool]
+    value: Union[int, float, str, bool, None] = None
     default_value: Union[int, float, str, bool] = None
     max: Union[int, float, None] = None
     min: Union[int, float, None] = None
     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 AgentAction(pydantic.BaseModel):
     enabled: bool = True
@@ -40,7 +48,6 @@ class AgentAction(pydantic.BaseModel):
     description: str = ""
     config: Union[dict[str, AgentActionConfig], None] = None
     
-
 def set_processing(fn):
     """
     decorator that emits the agent status as processing while the function
@@ -51,12 +58,18 @@ def set_processing(fn):
     """
     
     async def wrapper(self, *args, **kwargs):
-        try:
-            await self.emit_status(processing=True)
-            return await fn(self, *args, **kwargs)
-        finally:
-            await self.emit_status(processing=False)
-            
+        with ActiveAgent(self, fn):
+            try:
+                await self.emit_status(processing=True)
+                return await fn(self, *args, **kwargs)
+            finally:
+                try:
+                    await self.emit_status(processing=False)
+                except RuntimeError as exc:
+                    # not sure why this happens
+                    # some concurrency error?
+                    log.error("error emitting agent status", exc=exc)
+                    
     wrapper.__name__ = fn.__name__
             
     return wrapper
@@ -70,6 +83,8 @@ class Agent(ABC):
     agent_type = "agent"
     verbose_name = None
     set_processing = set_processing
+    requires_llm_client = True
+    auto_break_repetition = False
 
     @property
     def agent_details(self):
@@ -135,6 +150,7 @@ class Agent(ABC):
             "enabled": agent.enabled if agent else True,
             "has_toggle": agent.has_toggle if agent else False,
             "experimental": agent.experimental if agent else False,
+            "requires_llm_client": cls.requires_llm_client,
         }
         actions = getattr(agent, "actions", None)
         
@@ -275,6 +291,22 @@ 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):
+        """
+        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):
+        """
+        Returns True if repetition breaking is allowed, False otherwise.
+        """
+        return False
 
 @dataclasses.dataclass
 class AgentEmission:

src/talemate/agents/context.py

@@ -1,54 +1,33 @@
-from .base import Agent
-from .registry import register
 
+from typing import Callable, TYPE_CHECKING
+import contextvars
+import pydantic
 
-@register
-class ContextAgent(Agent):
-    """
-    Agent that helps retrieve context for the continuation
-    of dialogue.
-    """
+__all__ = [
+    "active_agent",
+]
 
-    agent_type = "context"
+active_agent = contextvars.ContextVar("active_agent", default=None)
 
-    def __init__(self, client, **kwargs):
-        self.client = client
+class ActiveAgentContext(pydantic.BaseModel):
+    agent: object
+    fn: Callable
+    
+    class Config:
+        arbitrary_types_allowed=True
+        
+    @property
+    def action(self):
+        return self.fn.__name__
 
-    def determine_questions(self, scene_text):
-        prompt = [
-            "You are tasked to continue the following dialogue in a roleplaying session, but before you can do so you can ask three questions for extra context."
-            "",
-            "What are the questions you would ask?",
-            "",
-            "Known context and dialogue:" "",
-            scene_text,
-            "",
-            "Questions:",
-            "",
-        ]
-
-        prompt = "\n".join(prompt)
-
-        questions = self.client.send_prompt(prompt, kind="question")
-
-        questions = self.clean_result(questions)
-
-        return questions.split("\n")
-
-    def get_answer(self, question, context):
-        prompt = [
-            "Read the context and answer the question:",
-            "",
-            "Context:",
-            "",
-            context,
-            "",
-            f"Question: {question}",
-            "Answer:",
-        ]
-
-        prompt = "\n".join(prompt)
-
-        answer = self.client.send_prompt(prompt, kind="answer")
-        answer = self.clean_result(answer)
-        return answer
+class ActiveAgent:
+    
+    def __init__(self, agent, fn):
+        self.agent = ActiveAgentContext(agent=agent, fn=fn)
+    
+    def __enter__(self):
+        self.token = active_agent.set(self.agent)
+    
+    def __exit__(self, *args, **kwargs):
+        active_agent.reset(self.token)
+        return False

src/talemate/agents/conversation.py

@@ -85,20 +85,25 @@ class ConversationAgent(Agent):
                     "instructions": AgentActionConfig(
                         type="text",
                         label="Instructions",
-                        value="1-3 sentences.",
+                        value="Write 1-3 sentences. Never wax poetic.",
                         description="Extra instructions to give the AI for dialog generatrion.",
                     ),
                     "jiggle": AgentActionConfig(
                         type="number",
-                        label="Jiggle",
+                        label="Jiggle (Increased Randomness)",
                         description="If > 0.0 will cause certain generation parameters to have a slight random offset applied to them. The bigger the number, the higher the potential offset.",
                         value=0.0,
                         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.",
+            ),
             "natural_flow": AgentAction(
                 enabled = True,
                 label = "Natural Flow",
@@ -129,11 +134,16 @@ class ConversationAgent(Agent):
                 label = "Long Term Memory",
                 description = "Will augment the conversation prompt with long term memory.",
                 config = {
-                    "ai_selected": AgentActionConfig(
-                        type="bool",
-                        label="AI Selected",
-                        description="If enabled, the AI will select the long term memory to use. (will increase how long it takes to generate a response)",
-                        value=False,
+                    "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"},
+                        ]
                     ),
                 }
             ),  
@@ -197,7 +207,7 @@ class ConversationAgent(Agent):
     async def on_game_loop(self, event:GameLoopEvent):
         await self.apply_natural_flow()
     
-    async def apply_natural_flow(self):
+    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.
@@ -212,15 +222,21 @@ class ConversationAgent(Agent):
         """
         
         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()
-            last_turn_player = last_turn.get(scene.get_player_character().name, 0)
+            player_name = scene.get_player_character().name
+            last_turn_player = last_turn.get(player_name, 0)
             
-            if last_turn_player >= max_auto_turns:
+            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)
                 return
@@ -235,15 +251,25 @@ class ConversationAgent(Agent):
                 # 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                
                 # 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]
             
+            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)
@@ -316,10 +342,8 @@ class ConversationAgent(Agent):
         
         scene_and_dialogue = scene.context_history(
             budget=scene_and_dialogue_budget, 
-            min_dialogue=25, 
             keep_director=True,
             sections=False,
-            insert_bot_token=10
         )
         
         memory = await self.build_prompt_default_memory(character)
@@ -337,9 +361,6 @@ class ConversationAgent(Agent):
         else:
             formatted_names = character_names[0] if character_names else ""
             
-        # if there is more than 10 lines in scene_and_dialogue insert
-        # a <|BOT|> token at -10, otherwise insert it at 0
-
         try:
             director_message = isinstance(scene_and_dialogue[-1], DirectorMessage)
         except IndexError:
@@ -388,25 +409,33 @@ class ConversationAgent(Agent):
             return self.current_memory_context
 
         self.current_memory_context = ""
+        retrieval_method = self.actions["use_long_term_memory"].config["retrieval_method"].value
         
 
-        if self.actions["use_long_term_memory"].config["ai_selected"].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)
             text = "\n".join(history)
-            world_state = instance.get_agent("world_state")
-            log.debug("conversation_agent.build_prompt_default_memory", direct=False)
-            self.current_memory_context = await world_state.analyze_text_and_extract_context(
-                text, f"continue the conversation as {character.name}"
-            )
+            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")
+            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}"
+                )
 
         else:
-            history = self.scene.context_history(min_dialogue=3, max_dialogue=3, keep_director=False, sections=False, add_archieved_history=False)
+            history = list(map(str, self.scene.collect_messages(max_iterations=3)))
             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 = "\n".join(context)
+            self.current_memory_context = context
         
         return self.current_memory_context
 
@@ -442,12 +471,11 @@ class ConversationAgent(Agent):
                 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
@@ -534,3 +562,16 @@ class ConversationAgent(Agent):
         actor.scene.push_history(messages)
 
         return messages
+
+
+    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):
+        if prompt_param.get("extra_stopping_strings") is None:
+            prompt_param["extra_stopping_strings"] = []
+        prompt_param["extra_stopping_strings"] += ['[']

src/talemate/agents/creator/__init__.py

@@ -3,9 +3,10 @@ from __future__ import annotations
 import json
 import os
 
-from talemate.agents.base import Agent
+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 .character import CharacterCreatorMixin
@@ -157,3 +158,24 @@ class CreatorAgent(CharacterCreatorMixin, ScenarioCreatorMixin, Agent):
 
         return rv
 
+
+    @set_processing
+    async def generate_json_list(
+        self,
+        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",[])
+    
+    @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

src/talemate/agents/creator/character.py

@@ -200,6 +200,28 @@ class CharacterCreatorMixin:
         })
         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,
+        })
+        
+        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,

src/talemate/agents/creator/scenario.py

@@ -48,41 +48,43 @@ class ScenarioCreatorMixin:
             
     
     
+    @set_processing
     async def create_scene_name(
         self,
         prompt:str,
         content_context:str,
         description:str,
     ):
+        
+        """
+        Generates a scene name.
+        
+        Arguments:
+        
+            prompt (str): The prompt to use to generate the scene name.
             
-            """
-            Generates a scene name.
+            content_context (str): The content context to use for the scene.
             
-            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,
-                "create",
-                vars={
-                    "prompt": prompt,
-                    "content_context": content_context,
-                    "description": description,
-                    "scene": scene,
-                }
-            )
-            name = name.strip().strip('.!').replace('"','')
-            return name
+            description (str): The description of the scene.
+        """
+        scene = self.scene
+        
+        name = await Prompt.request(
+            "creator.scenario-name",
+            self.client,
+            "create",
+            vars={
+                "prompt": prompt,
+                "content_context": content_context,
+                "description": description,
+                "scene": scene,
+            }
+        )
+        name = name.strip().strip('.!').replace('"','')
+        return name
     
     
+    @set_processing
     async def create_scene_intro(
         self,
         prompt:str,
@@ -130,4 +132,4 @@ class ScenarioCreatorMixin:
         description = await Prompt.request(f"creator.determine-scenario-description", self.client, "analyze_long", vars={
             "text": text,
         })
-        return description
+        return description

src/talemate/agents/director.py

@@ -16,11 +16,13 @@ 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.instance as instance
 
 if TYPE_CHECKING:
     from talemate import Actor, Character, Player, Scene
 
-log = structlog.get_logger("talemate")
+log = structlog.get_logger("talemate.agent.director")
 
 @register()
 class DirectorAgent(Agent):
@@ -28,13 +30,15 @@ class DirectorAgent(Agent):
     verbose_name = "Director"
     
     def __init__(self, client, **kwargs):
-        self.is_enabled = False
+        self.is_enabled = True
         self.client = client
-        self.next_direct = 0
+        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),
-                "prompt": AgentActionConfig(type="text", label="Instructions", description="Instructions to the director", value="", scope="scene")
+                "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),
             }),
         }
         
@@ -53,54 +57,210 @@ class DirectorAgent(Agent):
     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("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)
+            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):
         log.info("on_conversation_before_generate", director_enabled=self.enabled)
         if not self.enabled:
             return
         
-        await self.direct_scene(event.character)
+        await self.direct(event.character)
+    
+    async def on_player_dialog(self, event:GameLoopActorIterEvent):
+    
+        if not self.enabled:
+            return
         
-    async def direct_scene(self, character: Character):
+        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)
+            return
+
+        event.game_loop.had_passive_narration = await self.direct(None)
+        
+    async def direct(self, character: Character) -> bool:
         
         if not self.actions["direct"].enabled:
-            log.info("direct_scene", skip=True, enabled=self.actions["direct"].enabled)
-            return
+            return False
         
-        prompt = self.actions["direct"].config["prompt"].value
-        
-        if not prompt:
-            log.info("direct_scene", skip=True, prompt=prompt)
-            return
-        
-        if self.next_direct % self.actions["direct"].config["turns"].value != 0 or self.next_direct == 0:
+        if character:
             
-            log.info("direct_scene", skip=True, next_direct=self.next_direct)
-            self.next_direct += 1
-            return
+            if not self.actions["direct"].config["direct_actors"].value:
+                log.info("direct", skip=True, reason="direct_actors disabled", character=character)
+                return False
+            
+            # 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
         
-        self.next_direct = 0
+            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)
+                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)
+            
+            next_direct = self.next_direct_scene
+            
+            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
         
-        await self.direct_character(character, prompt)
+            self.next_direct_scene = 0
+            await self.direct_scene(None, None)
+            return True
         
     @set_processing
-    async def direct_character(self, character: Character, prompt:str):
+    async def run_gamestate_instructions(self):
+        """
+        Run game state instructions, if they exist.
+        """
         
-        response = await Prompt.request("director.direct-scene", self.client, "director", vars={
-            "max_tokens": self.client.max_token_length,
-            "scene": self.scene,
-            "prompt": prompt,
-            "character": character,
+        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):
+        
+        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,
+            })
+            
+            if "#" in response:
+                response = response.split("#")[0]
+            
+            log.info("direct_character", character=character, prompt=prompt, response=response)
+            
+            response = response.strip().split("\n")[0].strip()
+            #response += f" (current story goal: {prompt})"
+            message = DirectorMessage(response, source=character.name)
+            emit("director", message, character=character)
+            self.scene.push_history(message)
+        else:
+            # run scene instructions
+            self.scene.game_state.scene_instructions
+
+    @set_processing
+    async def persist_character(
+        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)
+
+        character = self.scene.Character(name=name)
+        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)
+        else:
+            attributes = world_state._parse_character_sheet(attributes)
+        
+        self.scene.log.debug("persist_character", attributes=attributes)
+
+        character.base_attributes = attributes
+
+        description = await creator.determine_character_description(character)
+
+        character.description = description
+
+        self.scene.log.debug("persist_character", description=description)
+
+        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):
+        
+        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 [],
         })
         
-        response = response.strip().split("\n")[0].strip()
+        self.scene.context = response.strip()
+        self.scene.emit_status()
         
-        response += f" (current story goal: {prompt})"
+    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"]
         
-        log.info("direct_scene", response=response)
-        
-        
-        message = DirectorMessage(response, source=character.name)
-        emit("director", message, character=character)
-        
-        self.scene.push_history(message)
+    def allow_repetition_break(self, kind: str, agent_function_name: str, auto:bool=False):
+        return True

src/talemate/agents/editor.py

@@ -10,7 +10,7 @@ import talemate.emit.async_signals
 from talemate.prompts import Prompt
 from talemate.scene_message import DirectorMessage, TimePassageMessage
 
-from .base import Agent, set_processing, AgentAction
+from .base import Agent, set_processing, AgentAction, AgentActionConfig
 from .registry import register
 
 import structlog
@@ -21,6 +21,7 @@ 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
 
 log = structlog.get_logger("talemate.agents.editor")
 
@@ -40,7 +41,9 @@ class EditorAgent(Agent):
         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."),
+            "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.")
         }
         
@@ -59,6 +62,7 @@ class EditorAgent(Agent):
     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):
         """
@@ -93,6 +97,24 @@ class EditorAgent(Agent):
             
         emission.generation = edited
         
+    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):
@@ -127,19 +149,43 @@ class EditorAgent(Agent):
         if not self.actions["fix_exposition"].enabled:
             return content
         
-        #response = await Prompt.request("editor.fix-exposition", self.client, "edit_fix_exposition", vars={
-        #    "content": content,
-        #    "character": character,
-        #    "scene": self.scene,
-        #    "max_length": self.client.max_token_length
-        #})
-        
+        if not character.is_player:
+            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('*')}*"
+                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 = 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):
+        
+        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):
         """

src/talemate/agents/memory.py

@@ -6,10 +6,14 @@ from typing import TYPE_CHECKING, Callable, List, Optional, Union
 from chromadb.config import Settings
 import talemate.events as events
 import talemate.util as util
+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
@@ -26,6 +30,16 @@ if not chromadb:
 
 from .base import Agent
 
+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):
     """
@@ -57,6 +71,16 @@ class MemoryAgent(Agent):
         self.scene = scene
         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()
+        if openai_key != self.openai_api_key:
+            loop = asyncio.get_running_loop()
+            loop.run_until_complete(self.emit_status())
 
     async def set_db(self):
         raise NotImplementedError()
@@ -67,37 +91,95 @@ class MemoryAgent(Agent):
     async def count(self):
         raise NotImplementedError()
 
+    @set_processing
     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
-        await self._add(text, character=character, uid=uid, ts=ts, **kwargs)
+        
+        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()
+        
+        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)
 
-    async def _add(self, text, character=None, ts:str=None, **kwargs):
+    def _add(self, text, character=None, ts:str=None, **kwargs):
         raise NotImplementedError()
 
+    @set_processing
     async def add_many(self, objects: list[dict]):
         if self.readonly:
             log.debug("memory agent", status="readonly")
             return
-        await self._add_many(objects)
+
+        while not self._ready_to_add:
+            await asyncio.sleep(0.1)
+ 
+        log.debug("memory agent add many", len=len(objects)) 
+ 
+        loop = asyncio.get_running_loop()
+        await loop.run_in_executor(None, self._add_many, objects)
     
-    async def _add_many(self, objects: list[dict]):
+    def _add_many(self, objects: list[dict]):
         """
         Add multiple objects to the memory
         """
         raise NotImplementedError()
 
-    async def get(self, text, character=None, **query):
-        return await self._get(str(text), character, **query)
+    def _delete(self, meta:dict):
+        """
+        Delete an object from the memory
+        """
+        raise NotImplementedError()
+    
+    @set_processing
+    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)
 
-    async def _get(self, text, character=None, **query):
+    @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))
+
+    def _get(self, text, character=None, **query):
         raise NotImplementedError()
 
-    def get_document(self, id):
-        return self.db.get(id)
+    @set_processing
+    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"))
@@ -140,6 +222,10 @@ class MemoryAgent(Agent):
         """
 
         memory_context = []
+        
+        if not query:
+            return memory_context
+        
         for memory in await self.get(query):
             if memory in memory_context:
                 continue
@@ -171,6 +257,7 @@ class MemoryAgent(Agent):
         max_tokens: int = 1000,
         filter: Callable = lambda x: True,
         formatter: Callable = lambda x: x,
+        limit: int = 10,
         **where
     ):
         """
@@ -179,8 +266,12 @@ class MemoryAgent(Agent):
 
         memory_context = []
         for query in queries:
+            
+            if not query:
+                continue
+            
             i = 0
-            for memory in await self.get(formatter(query), limit=iterate, **where):
+            for memory in await self.get(formatter(query), limit=limit, **where):
                 if memory in memory_context:
                     continue
 
@@ -206,9 +297,14 @@ 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
@@ -217,12 +313,20 @@ 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):
+        
+        if self.embeddings == "openai" and not self.openai_api_key:
+            return "No OpenAI API key set"
+        
         return f"ChromaDB: {self.embeddings}"
-
+    
     @property
     def embeddings(self):
         """
@@ -265,6 +369,10 @@ class ChromaDBMemoryAgent(MemoryAgent):
     def db_name(self):
         return getattr(self, "collection_name", "<unnamed>")
 
+    @property
+    def openai_api_key(self):
+        return self.config.get("openai",{}).get("api_key")
+
     def make_collection_name(self, scene):
         
         if self.USE_OPENAI:
@@ -285,17 +393,22 @@ class ChromaDBMemoryAgent(MemoryAgent):
         await asyncio.sleep(0)
         return self.db.count()
 
+    @set_processing
     async def set_db(self):
-        await self.emit_status(processing=True)
-
+        loop = asyncio.get_running_loop()
+        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(
                 settings=Settings(anonymized_telemetry=False)
             )
 
-        openai_key = self.config.get("openai").get("api_key") or os.environ.get("OPENAI_API_KEY")
+        openai_key = self.openai_api_key
         
         self.collection_name = collection_name = self.make_collection_name(self.scene)
         
@@ -340,9 +453,8 @@ class ChromaDBMemoryAgent(MemoryAgent):
             self.db = self.db_client.get_or_create_collection(collection_name)
         
         self.scene._memory_never_persisted = self.db.count() == 0
-
-        await self.emit_status(processing=False)
         log.info("chromadb agent", status="db ready")
+        self._ready_to_add = True
 
     def clear_db(self):
         if not self.db:
@@ -370,26 +482,28 @@ class ChromaDBMemoryAgent(MemoryAgent):
         
         log.info("chromadb agent", status="closing db", collection_name=self.collection_name)
         
-        if not scene.saved:
+        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)
             try:
                 self.db_client.delete_collection(collection_name)
             except ValueError as exc:
-                if "Collection not found" not in str(exc):
-                    raise
+                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
         
-    async 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 = []
-
-        await self.emit_status(processing=True)
-
+        scene = self.scene
+        
         if character:
-            meta = {"character": character.name, "source": "talemate"}
+            meta = {"character": character.name, "source": "talemate", "session": scene.memory_session_id}
             if ts:
                 meta["ts"] = ts
             meta.update(kwargs)
@@ -399,7 +513,7 @@ class ChromaDBMemoryAgent(MemoryAgent):
             id = uid or f"{character.name}-{self.memory_tracker[character.name]}"
             ids = [id]
         else:
-            meta = {"character": "__narrator__", "source": "talemate"}
+            meta = {"character": "__narrator__", "source": "talemate", "session": scene.memory_session_id}
             if ts:
                 meta["ts"] = ts
             meta.update(kwargs)
@@ -409,38 +523,53 @@ 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)
-        
-        await self.emit_status(processing=False)
 
-    async def _add_many(self, objects: list[dict]):
+    def _add_many(self, objects: list[dict]):
         
         documents = []
         metadatas = []
         ids = []
-
-        await self.emit_status(processing=True)
+        scene = self.scene
+        
+        if not objects:
+            return
 
         for obj in objects:
             documents.append(obj["text"])
             meta = obj.get("meta", {})
+            source = meta.get("source", "talemate")
             character = meta.get("character", "__narrator__")
             self.memory_tracker.setdefault(character, 0)
             self.memory_tracker[character] += 1
-            meta["source"] = "talemate"
+            meta["source"] = source
+            if not meta.get("session"):
+                meta["session"] = scene.memory_session_id
             metadatas.append(meta)
             uid = obj.get("id", f"{character}-{self.memory_tracker[character]}")
             ids.append(uid)
         self.db.upsert(documents=documents, metadatas=metadatas, ids=ids)
 
-        await self.emit_status(processing=False)
-
-    async def _get(self, text, character=None, limit:int=15, **kwargs):
-        await self.emit_status(processing=True)
+    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()]}
+        self.db.delete(where=where)
+        log.debug("chromadb agent delete", meta=meta, where=where)
 
+    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
@@ -468,6 +597,12 @@ class ChromaDBMemoryAgent(MemoryAgent):
         #print(json.dumps(_results["distances"], indent=2))
         
         results = []
+        
+        max_distance = 1.5
+        if self.USE_INSTRUCTOR:
+            max_distance = 1
+        elif self.USE_OPENAI:
+            max_distance = 1
 
         for i in range(len(_results["distances"][0])):
             distance = _results["distances"][0][i]
@@ -476,16 +611,19 @@ class ChromaDBMemoryAgent(MemoryAgent):
             meta = _results["metadatas"][0][i]
             ts = meta.get("ts")
             
-            if distance < 1:
+            # 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
                 
-                try:
-                    date_prefix = util.iso8601_diff_to_human(ts, self.scene.ts)
-                except Exception:
-                    log.error("chromadb agent", error="failed to get date prefix", ts=ts, scene_ts=self.scene.ts)
-                    date_prefix = None
-                    
                 if date_prefix:
                     doc = f"{date_prefix}: {doc}"
+                    
+                doc = MemoryDocument(doc, meta, _results["ids"][0][i], raw)
+                    
                 results.append(doc)
             else:
                 break
@@ -495,6 +633,47 @@ class ChromaDBMemoryAgent(MemoryAgent):
             if len(results) > limit:
                 break
 
-        await self.emit_status(processing=False)
-        
         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)
+            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"))
+            if date_prefix:
+                doc = f"{date_prefix}: {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)
+        
+        self._delete({"session": scene.memory_session_id, "source": "talemate"})
+        
+    

src/talemate/agents/narrator.py

@@ -1,13 +1,14 @@
 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, Agent, AgentAction, AgentActionConfig
+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
@@ -20,6 +21,33 @@ if TYPE_CHECKING:
 
 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"
+)
+
+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):
+        response = await fn(self, *args, **kwargs)
+        emission = NarratorAgentEmission(
+            agent=self,
+            generation=[response],
+        )
+        await talemate.emit.async_signals.get("agent.narrator.generated").send(emission)
+        return emission.generation[0]
+    wrapper.__name__ = fn.__name__
+    return wrapper
+
 @register()
 class NarratorAgent(Agent):
     
@@ -40,17 +68,47 @@ class NarratorAgent(Agent):
         # agent actions
         
         self.actions = {
-            "narrate_time_passage": AgentAction(enabled=True, label="Narrate Time Passage", description="Whenever you indicate passage of time, narrate right after"),
-            "narrate_dialogue": AgentAction(
+            "generation_override": AgentAction(
+                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.",
+            ),
+            "narrate_time_passage": AgentAction(
                 enabled=True, 
-                label="Narrate Dialogue", 
+                label="Narrate Time Passage", 
+                description="Whenever you indicate passage of time, narrate right after",
+                config = {
+                    "ask_for_prompt": AgentActionConfig(
+                        type="bool",
+                        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", 
                 description="Narrator will get a chance to narrate after every line of dialogue",
                 config = {
                     "ai_dialog": AgentActionConfig(
                         type="number",
                         label="AI Dialogue", 
                         description="Chance to narrate after every line of dialogue, 1 = always, 0 = never",
-                        value=0.3,
+                        value=0.0,
                         min=0.0,
                         max=1.0,
                         step=0.1,
@@ -59,15 +117,27 @@ class NarratorAgent(Agent):
                         type="number",
                         label="Player Dialogue", 
                         description="Chance to narrate after every line of dialogue, 1 = always, 0 = never",
-                        value=0.3,
+                        value=0.1,
                         min=0.0,
                         max=1.0,
                         step=0.1,
                     ),
+                    "generate_dialogue": AgentActionConfig(
+                        type="bool",
+                        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):
         
         """
@@ -112,7 +182,7 @@ class NarratorAgent(Agent):
         if not self.actions["narrate_time_passage"].enabled:
             return
         
-        response = await self.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)
@@ -126,21 +196,36 @@ class NarratorAgent(Agent):
         if not self.actions["narrate_dialogue"].enabled:
             return
         
-        narrate_on_ai_chance = random.random() < self.actions["narrate_dialogue"].config["ai_dialog"].value
-        narrate_on_player_chance = random.random() < self.actions["narrate_dialogue"].config["player_dialog"].value
         
-        log.debug("narrate on dialog", narrate_on_ai_chance=narrate_on_ai_chance, narrate_on_player_chance=narrate_on_player_chance)
-        
-        if event.actor.character.is_player and not narrate_on_player_chance:
+        if event.game_loop.had_passive_narration:
+            log.debug("narrate on dialog", skip=True, had_passive_narration=event.game_loop.had_passive_narration)
             return
         
-        if not event.actor.character.is_player and not narrate_on_ai_chance:
+        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_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}")
         emit("narrator", narrator_message)
         self.scene.push_history(narrator_message)
+        
+        event.game_loop.had_passive_narration = True
 
     @set_processing
     async def narrate_scene(self):
@@ -155,6 +240,7 @@ class NarratorAgent(Agent):
             vars = {
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
+                "extra_instructions": self.extra_instructions,
             }
         )
         
@@ -172,22 +258,11 @@ class NarratorAgent(Agent):
         """
 
         scene = self.scene
-        director = scene.get_helper("director").agent
         pc = scene.get_player_character()
         npcs = list(scene.get_npc_characters())
         npc_names=  ", ".join([npc.name for npc in npcs])
         
-        #summarized_history = await scene.summarized_dialogue_history(
-        #    budget = self.client.max_token_length - 300,
-        #    min_dialogue = 50,
-        #)
-        
-        #augmented_context = await self.augment_context()
-
         if narrative_direction is None:
-            #narrative_direction = await director.direct_narrative(
-            #    scene.context_history(budget=self.client.max_token_length - 500, min_dialogue=20),
-            #)
             narrative_direction = "Slightly move the current scene forward."
         
         self.scene.log.info("narrative_direction", narrative_direction=narrative_direction)
@@ -198,13 +273,12 @@ class NarratorAgent(Agent):
             "narrate",
             vars = {
                 "scene": self.scene,
-                #"summarized_history": summarized_history,
-                #"augmented_context": augmented_context,
                 "max_tokens": self.client.max_token_length,
                 "narrative_direction": narrative_direction,
                 "player_character": pc,
                 "npcs": npcs,
                 "npc_names": npc_names,
+                "extra_instructions": self.extra_instructions,
             }
         )
 
@@ -235,6 +309,7 @@ class NarratorAgent(Agent):
                 "query": query,
                 "at_the_end": at_the_end,
                 "as_narrative": as_narrative,
+                "extra_instructions": self.extra_instructions,
             }
         )
         log.info("narrate_query", response=response)
@@ -251,17 +326,6 @@ class NarratorAgent(Agent):
         Narrate a specific character
         """
 
-        budget = self.client.max_token_length - 300
-
-        memory_budget = min(int(budget * 0.05), 200)
-        memory = self.scene.get_helper("memory").agent
-        query = [
-            f"What does {character.name} currently look like?",
-            f"What is {character.name} currently wearing?",
-        ]
-        memory_context = await memory.multi_query(
-            query, iterate=1, max_tokens=memory_budget
-        )
         response = await Prompt.request(
             "narrator.narrate-character",
             self.client,
@@ -270,7 +334,7 @@ class NarratorAgent(Agent):
                 "scene": self.scene,
                 "character": character,
                 "max_tokens": self.client.max_token_length,
-                "memory": memory_context,
+                "extra_instructions": self.extra_instructions,
             }
         )
 
@@ -295,6 +359,7 @@ class NarratorAgent(Agent):
             vars = {
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
+                "extra_instructions": self.extra_instructions,
             }
         )
         
@@ -315,6 +380,7 @@ class NarratorAgent(Agent):
                 "max_tokens": self.client.max_token_length,
                 "memory": memory_context,
                 "questions": questions,
+                "extra_instructions": self.extra_instructions,
             }
         )
         
@@ -326,7 +392,7 @@ class NarratorAgent(Agent):
         return list(zip(questions, answers))
     
     @set_processing
-    async def narrate_time_passage(self, duration:str, narrative:str=None):
+    async def narrate_time_passage(self, duration:str, time_passed:str, narrative:str):
         """
         Narrate a specific character
         """
@@ -339,7 +405,9 @@ class NarratorAgent(Agent):
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "duration": duration,
+                "time_passed": time_passed,
                 "narrative": narrative,
+                "extra_instructions": self.extra_instructions,
             }
         )
         
@@ -365,7 +433,8 @@ class NarratorAgent(Agent):
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
                 "character": character,
-                "last_line": str(self.scene.history[-1])
+                "last_line": str(self.scene.history[-1]),
+                "extra_instructions": self.extra_instructions,
             }
         )
         
@@ -373,5 +442,92 @@ class NarratorAgent(Agent):
 
         response = self.clean_result(response.strip().strip("*"))
         response = f"*{response}*"
+        
+        allow_dialogue = self.actions["narrate_dialogue"].config["generate_dialogue"].value
+        
+        if not allow_dialogue:
+            response = response.split('"')[0].strip()
+            response = response.replace("*", "")
+            response = util.strip_partial_sentences(response)
+            response = f"*{response}*"
 
-        return 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
+    
+    async def action_to_narration(
+        self,
+        action_name: str,
+        *args,
+        **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(*args, **kwargs)
+        narrator_message = NarratorMessage(narration, source=f"{action_name}:{args[0] if args else ''}".rstrip(":"))
+        self.scene.push_history(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)
+        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):
+        if auto and not self.actions["auto_break_repetition"].enabled:
+            return False
+        
+        return True
+    

src/talemate/agents/summarize.py

@@ -5,11 +5,13 @@ import traceback
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
 import talemate.data_objects as data_objects
+import talemate.emit.async_signals
 import talemate.util as util
 from talemate.prompts import Prompt
 from talemate.scene_message import DirectorMessage, TimePassageMessage
+from talemate.events import GameLoopEvent
 
-from .base import Agent, set_processing
+from .base import Agent, set_processing, AgentAction, AgentActionConfig
 from .registry import register
 
 import structlog
@@ -34,14 +36,61 @@ class SummarizeAgent(Agent):
 
     def __init__(self, client, **kwargs):
         self.client = client
-
-    def on_history_add(self, event):
-        asyncio.ensure_future(self.build_archive(event.scene))
-
+        
+        self.actions = {
+            "archive": AgentAction(
+                enabled=True,
+                label="Summarize to long-term memory archive",
+                description="Automatically summarize scene dialogue when the number of tokens in the history exceeds a threshold. This helps keep the context history from growing too large.",
+                config={
+                    "threshold": AgentActionConfig(
+                        type="number",
+                        label="Token Threshold",
+                        description="Will summarize when the number of tokens in the history exceeds this threshold",
+                        min=512,
+                        max=8192,
+                        step=256,
+                        value=1536,
+                    ),
+                    "method": AgentActionConfig(
+                        type="text",
+                        label="Summarization Method",
+                        description="Which method to use for summarization",
+                        value="balanced",
+                        choices=[
+                            {"label": "Short & Concise", "value": "short"},
+                            {"label": "Balanced", "value": "balanced"},
+                            {"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)
-        scene.signals["history_add"].connect(self.on_history_add)
+        talemate.emit.async_signals.get("game_loop").connect(self.on_game_loop)
+        
+        
+    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]
@@ -53,21 +102,40 @@ class SummarizeAgent(Agent):
         return result
 
     @set_processing
-    async def build_archive(self, scene, token_threshold:int=1500):
+    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
         else:
             recent_entry = scene.archived_history[-1]
-            start = recent_entry.get("end", 0) + 1
+            if "end" not in recent_entry:
+                # permanent historical archive entry, not tied to any specific history entry
+                # meaning we are still at the beginning of the scene
+                start = 0
+            else:
+                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:
@@ -75,6 +143,9 @@ class SummarizeAgent(Agent):
         
         for i in range(start, len(scene.history)):
             dialogue = scene.history[i]
+            
+            #log.debug("build_archive", idx=i, content=str(dialogue)[:64]+"...")
+            
             if isinstance(dialogue, DirectorMessage):
                 if i == start:
                     start += 1
@@ -104,10 +175,6 @@ class SummarizeAgent(Agent):
         
         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"]
-            
         # 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)
@@ -131,7 +198,7 @@ class SummarizeAgent(Agent):
                         break
                     adjusted_dialogue.append(line)
                 dialogue_entries = adjusted_dialogue
-                end = start + len(dialogue_entries)
+                end = start + len(dialogue_entries)-1
             
         if dialogue_entries:
             summarized = await self.summarize(
@@ -164,9 +231,9 @@ class SummarizeAgent(Agent):
     async def summarize(
         self,
         text: str,
-        perspective: str = None,
-        pins: Union[List[str], None] = None,
         extra_context: str = None,
+        method: str = None,
+        extra_instructions: str = None,
     ):
         """
         Summarize the given text
@@ -176,30 +243,136 @@ class SummarizeAgent(Agent):
             "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 "",
         })
         
         self.scene.log.info("summarize", dialogue_length=len(text), summarized_length=len(response))
 
         return self.clean_result(response)
-
-    @set_processing
-    async def simple_summary(
-        self, text: str, prompt_kind: str = "summarize", instructions: str = "Summarize"
-    ):
-        prompt = [
-            text,
-            "",
-            f"Instruction: {instructions}",
-            "<|BOT|>Short Summary: ",
-        ]
-
-        response = await self.client.send_prompt("\n".join(map(str, prompt)), kind=prompt_kind)
-        if ":" in response:
-            response = response.split(":")[1].strip()
-        return 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

@@ -0,0 +1,617 @@
+from __future__ import annotations
+
+from typing import Union
+import asyncio
+import httpx
+import io
+import os
+import pydantic
+import nltk
+import tempfile
+import base64
+import uuid
+import functools
+from nltk.tokenize import sent_tokenize
+
+import talemate.config as config
+import talemate.emit.async_signals
+import talemate.instance as instance
+from talemate.emit import emit
+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 .registry import register
+
+import structlog
+
+import time
+
+try:
+    from TTS.api import TTS
+except ImportError:
+    TTS = None
+
+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")
+
+
+def parse_chunks(text):
+    
+    text = text.replace("...", "__ellipsis__")
+    
+    chunks = sent_tokenize(text)
+    cleaned_chunks = []
+    
+    for chunk in chunks:
+        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):
+    
+    # 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):
+    
+    """
+    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
+
+class VoiceLibrary(pydantic.BaseModel):
+    
+    api: str
+    voices: list[Voice] = pydantic.Field(default_factory=list)
+    last_synced: float = None
+
+
+@register()
+class TTSAgent(Agent):
+    
+    """
+    Text to speech agent
+    """
+    
+    agent_type = "tts"
+    verbose_name = "Voice"
+    requires_llm_client = 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"),
+        }
+        self.config = config.load_config()
+        self.playback_done_event = asyncio.Event()
+        self.actions = {
+            "_config": AgentAction(
+                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="tts",
+                        label="API",
+                        description="Which TTS API to use",
+                        onchange="emit",
+                    ),
+                    "voice_id": AgentActionConfig(
+                        type="text",
+                        value="default",
+                        label="Narrator Voice",
+                        description="Voice ID/Name to use for TTS",
+                        choices=[]
+                    ),
+                    "generate_for_player": AgentActionConfig(
+                        type="bool",
+                        value=False,
+                        label="Generate for player",
+                        description="Generate audio for player messages",
+                    ),
+                    "generate_for_npc": AgentActionConfig(
+                        type="bool",
+                        value=True,
+                        label="Generate for NPCs",
+                        description="Generate audio for NPC messages",
+                    ),
+                    "generate_for_narration": AgentActionConfig(
+                        type="bool",
+                        value=True,
+                        label="Generate for narration",
+                        description="Generate audio for narration messages",
+                    ),
+                    "generate_chunks": AgentActionConfig(
+                        type="bool",
+                        value=False,
+                        label="Split generation",
+                        description="Generate audio chunks for each sentence - will be much more responsive but may loose context to inform inflection",
+                    )
+                }  
+            ),
+        }
+        
+        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}"
+
+    @property
+    def api(self):
+        return self.actions["_config"].config["api"].value
+    
+    @property
+    def token(self):
+        api = self.api
+        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
+    def status(self):
+        if not self.enabled:
+            return "disabled"
+        if self.ready:
+            return "active" if not getattr(self, "processing", False) else "busy"
+        if self.requires_token and not self.token:
+            return "error"
+        if self.api == "tts":
+            if not TTS:
+                return "error"
+        return "uninitialized"
+
+    @property
+    def max_generation_length(self):
+        if self.api == "elevenlabs":
+            return 1024
+        elif self.api == "coqui":
+            return 250
+            
+        return 250
+
+    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)
+        
+        
+        if api_changed:
+            try:
+                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)
+        
+    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):
+        """
+        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:
+            return
+        
+        if isinstance(emission.message, CharacterMessage):
+            
+            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:
+                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+": ", ""))
+
+
+    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):
+        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 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
+
+    @set_processing
+    async def generate(self, text: str):
+        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)
+        else:
+            chunks = parse_chunks(text)
+            chunks = rejoin_chunks(chunks, chunk_size=self.max_generation_length)
+
+        # Start generating audio chunks in the background
+        generation_task = asyncio.create_task(self.generate_chunks(generate_fn, chunks))
+
+        # Wait for both tasks to complete
+        await asyncio.gather(generation_task)
+
+    async def generate_chunks(self, generate_fn, chunks):
+        for chunk in chunks:
+            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")})
+        
+        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",{})
+        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)
+            
+            
+            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",[])]
+        
+    # ELEVENLABS
+
+    async def _generate_elevenlabs(self, text: str, chunk_size: int = 1024) -> Union[bytes, None]:
+        api_key = self.token
+        if not api_key:
+            return
+
+        async with httpx.AsyncClient() as client:
+            url = f"https://api.elevenlabs.io/v1/text-to-speech/{self.default_voice_id}"
+            headers = {
+                "Accept": "audio/mpeg",
+                "Content-Type": "application/json",
+                "xi-api-key": api_key,
+            }
+            data = {
+                "text": text,
+                "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)
+
+            if response.status_code == 200:
+                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()
+            else:
+                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})
+            speakers = response.json()["voices"]
+            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
+            }
+
+            # 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
+
+        async with httpx.AsyncClient() as client:
+            url = f"https://app.coqui.ai/api/v2/samples/xtts/{sample_id}"
+            headers = {
+                "Authorization": f"Bearer {api_key}"
+            }
+
+            # Make the DELETE request to Coqui API
+            response = await client.delete(url, headers=headers)
+
+            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}")
+
+    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

src/talemate/agents/world_state.py

@@ -1,14 +1,17 @@
 from __future__ import annotations
 import dataclasses
-
+import json
+import uuid
 from typing import TYPE_CHECKING, Callable, List, Optional, Union
 
 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
+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 .base import Agent, set_processing, AgentAction, AgentActionConfig, AgentEmission
 from .registry import register
@@ -36,6 +39,7 @@ class TimePassageEmission(WorldStateAgentEmission):
     """
     duration: str
     narrative: str
+    human_duration: str = None
     
 
 @register()
@@ -51,12 +55,17 @@ 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 after AI dialogue (n turns).", config={
+            "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):
@@ -80,8 +89,8 @@ class WorldStateAgent(Agent):
         """
         
         isodate.parse_duration(duration)
-        msg_text = narrative or util.iso8601_duration_to_human(duration, suffix=" later")
-        message = TimePassageMessage(ts=duration, message=msg_text)
+        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)
@@ -90,7 +99,7 @@ class WorldStateAgent(Agent):
         emit("time", message)
         
         await talemate.emit.async_signals.get("agent.world_state.time").send(
-            TimePassageEmission(agent=self, duration=duration, narrative=msg_text)
+            TimePassageEmission(agent=self, duration=duration, narrative=narrative, human_duration=human_duration)
         )
         
 
@@ -103,7 +112,36 @@ class WorldStateAgent(Agent):
             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:
+            
+            self.next_pin_check += 1
+            return
+        
+        self.next_pin_check = 0
+        
+        await self.check_pin_conditions()
+        
 
     async def update_world_state(self):
         if not self.enabled:
@@ -219,6 +257,35 @@ class WorldStateAgent(Agent):
         
         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 = {
+                "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)
+        
+        return context
+    
     @set_processing
     async def analyze_and_follow_instruction(
         self,
@@ -290,6 +357,19 @@ class WorldStateAgent(Agent):
         
         return data
     
+    def _parse_character_sheet(self, response):
+        
+        data = {}
+        for line in response.split("\n"):
+            if not line.strip():
+                continue
+            if not ":" in line:
+                break
+            name, value = line.split(":", 1)
+            data[name.strip()] = value.strip()
+        
+        return data
+    
     @set_processing
     async def extract_character_sheet(
         self,
@@ -304,7 +384,7 @@ class WorldStateAgent(Agent):
         response = await Prompt.request(
             "world_state.extract-character-sheet",
             self.client,
-            "analyze_creative",
+            "create",
             vars = {
                 "scene": self.scene,
                 "max_tokens": self.client.max_token_length,
@@ -318,17 +398,8 @@ class WorldStateAgent(Agent):
         #
         # break as soon as a non-empty line is found that doesn't contain a :
         
-        data = {}
-        for line in response.split("\n"):
-            if not line.strip():
-                continue
-            if not ":" in line:
-                break
-            name, value = line.split(":", 1)
-            data[name.strip()] = value.strip()
+        return self._parse_character_sheet(response)
         
-        return data
-    
     
     @set_processing
     async def match_character_names(self, names:list[str]):
@@ -350,4 +421,244 @@ class WorldStateAgent(Agent):
         
         log.debug("match_character_names", names=names, response=response)
         
-        return response
+        return response
+    
+    
+    @set_processing
+    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)
+            else:
+                reinforcement.due -= 1
+                
+    
+    @set_processing
+    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)
+        
+        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)
+        
+        answer = await Prompt.request(
+            "world_state.update-reinforcements",
+            self.client,
+            "analyze_freeform",
+            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 if not reset else None) or "",
+                "reinforcement": reinforcement,
+            }
+        )
+        
+        reinforcement.answer = answer
+        reinforcement.due = reinforcement.interval
+            
+        # remove any recent previous reinforcement message with same question
+        # to avoid overloading the near history with reinforcement messages
+        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, 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(
+                reinforcement.question,
+                reinforcement.as_context_line,
+                {},
+            )
+            
+        self.scene.world_state.emit()
+        
+        return message  
+    
+    @set_processing
+    async def check_pin_conditions(
+        self,
+    ):
+        
+        """
+        Checks if any context pin conditions
+        """
+        
+        pins_with_condition = {
+            entry_id: {
+                "condition": pin.condition,
+                "state": pin.condition_state,
+            }
+            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 = {
+                "scene": self.scene,
+                "max_tokens": self.client.max_token_length,
+                "previous_states": json.dumps(pins_with_condition,indent=2),
+                "coercion": {first_entry_id:{ "condition": "" }},
+            }
+        )
+        
+        world_state = self.scene.world_state
+        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)")
+                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"]):
+                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:
+                if world_state.pins[entry_id].condition_state is not False:
+                    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:
+        
+        """
+        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)
+        
+        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, 
+            summary=summary,
+            entry_id=entry_id,
+            ts=ts,
+        )
+        
+        await self.scene.world_state_manager.save_world_entry(
+            entry_id,
+            summary,
+            {
+                "ts": ts,
+            },
+        )
+        
+        await self.scene.world_state_manager.set_pin(
+            entry_id,
+            active=True,
+        )
+        
+        await self.scene.load_active_pins()
+        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")

src/talemate/character.py

@@ -0,0 +1,57 @@
+from typing import Union, TYPE_CHECKING
+from talemate.instance import get_agent
+
+if TYPE_CHECKING:
+    from talemate.tale_mate import Scene, Character, Actor
+    
+
+__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/client/__init__.py

@@ -3,4 +3,5 @@ from talemate.client.openai import OpenAIClient
 from talemate.client.registry import CLIENT_CLASSES, get_client_class, register
 from talemate.client.textgenwebui import TextGeneratorWebuiClient
 from talemate.client.lmstudio import LMStudioClient
+from talemate.client.openai_compat import OpenAICompatibleClient
 import talemate.client.runpod

src/talemate/client/base.py

@@ -1,14 +1,14 @@
 """
 A unified client base, based on the openai API
 """
-import copy
 import random
 import time
-from typing import Callable
+import pydantic
+from typing import Callable, Union
 
 import structlog
 import logging
-from openai import AsyncOpenAI
+from openai import AsyncOpenAI, PermissionDeniedError
 
 from talemate.emit import emit
 import talemate.instance as instance
@@ -17,7 +17,7 @@ import talemate.client.system_prompts as system_prompts
 import talemate.util as util
 from talemate.client.context import client_context_attribute
 from talemate.client.model_prompts import model_prompt
-
+from talemate.agents.context import active_agent
 
 # Set up logging level for httpx to WARNING to suppress debug logs.
 logging.getLogger('httpx').setLevel(logging.WARNING)
@@ -29,22 +29,40 @@ REMOTE_SERVICES = [
 
 STOPPING_STRINGS = ["<|im_end|>", "</s>"]
 
+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 ClientBase:
     
     api_url: str
     model_name: str
+    api_key: str = None
     name:str = None
     enabled: bool = True
     current_status: str = None
     max_token_length: int = 4096
-    randomizable_inference_parameters: list[str] = ["temperature"]
     processing: bool = False
     connected: bool = False
-    conversation_retries: int = 5
+    conversation_retries: int = 2
+    auto_break_repetition_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,
@@ -54,12 +72,18 @@ class ClientBase:
         self.api_url = api_url
         self.name = name or self.client_type
         self.log = structlog.get_logger(f"client.{self.client_type}")
-        self.set_client()
+        if "max_token_length" in kwargs:
+            self.max_token_length = kwargs["max_token_length"]
+        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):
+    def set_client(self, **kwargs):
         self.client = AsyncOpenAI(base_url=self.api_url, api_key="sk-1111")
 
     def prompt_template(self, sys_msg, prompt):
@@ -71,9 +95,15 @@ class ClientBase:
         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)   
     
+        return model_prompt(self.model_name, sys_msg, prompt)[0]   
+        
+    def prompt_template_example(self):
+        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):
         
         """
@@ -142,10 +172,14 @@ class ClientBase:
             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
        
         return system_prompts.BASIC
     
@@ -175,12 +209,22 @@ class ClientBase:
         status_change = status != self.current_status
         self.current_status = status
         
+        prompt_template_example, prompt_template_file = self.prompt_template_example()
+        
         emit(
             "client_status",
             message=self.client_type,
             id=self.name,
             details=model_name,
             status=status,
+            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,
+            }
         )
 
         if status_change:
@@ -244,6 +288,10 @@ class ClientBase:
         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):
         conversation_context = client_context_attribute("conversation")
@@ -268,14 +316,19 @@ class ClientBase:
         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
+        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.
@@ -289,7 +342,7 @@ class ClientBase:
 
             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)
@@ -298,8 +351,14 @@ class ClientBase:
             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(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 = 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()
             
@@ -325,6 +384,128 @@ class ClientBase:
         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,
+    ) -> 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
+        - prompt_param: the parameters that were used
+        - response: the response that was received
+        - 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
+            )
+            
+            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
+                )
+                
+                # first we apply the client's randomness jiggle which will adjust
+                # parameters like temperature and repetition_penalty, depending
+                # on the client
+                #
+                # 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
+                )
+                
+                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)
+                
+                # 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
+                )
+                retries -= 1
+                
+        return response, finalized_prompt
+        
     def count_tokens(self, content:str):
         return util.count_tokens(content)
 
@@ -338,12 +519,37 @@ class ClientBase:
         min_offset = offset * 0.3
         prompt_config["temperature"] = random.uniform(temp + min_offset, temp + offset)
         
-    def jiggle_enabled_for(self, kind:str):
+    def jiggle_enabled_for(self, kind:str, auto:bool=False) -> bool:
         
-        if kind in ["conversation", "story"]:
-            return True
+        agent_context = active_agent.get()
+        agent = agent_context.agent
         
-        if kind.startswith("narrate"):
-            return True
+        if not agent:
+            return False
         
-        return False
+        return agent.allow_repetition_break(kind, agent_context.action, auto=auto)
+    
+    def repetition_adjustment(self, prompt:str, is_repetitive:bool=False):
+        """
+        Breaks the prompt into lines and checkse each line for a match with
+        [$REPETITION|{repetition_adjustment}].
+        
+        On match and if is_repetitive is True, the line is removed from the prompt and
+        replaced with the repetition_adjustment.
+        
+        On match and if is_repetitive is False, the line is removed from the prompt.        
+        """
+        
+        lines = prompt.split("\n")
+        new_lines = []
+        
+        for line in lines:
+            if line.startswith("[$REPETITION|"):
+                if is_repetitive:
+                    new_lines.append(line.split("|")[1][:-1])
+                else:
+                    new_lines.append("")
+            else:
+                new_lines.append(line)
+        
+        return "\n".join(new_lines)

src/talemate/client/bootstrap.py

@@ -51,12 +51,12 @@ class register_list:
         return func
     
 
-def list_all(exclude_urls: list[str] = list()):
+async def list_all(exclude_urls: list[str] = list()):
     """
     Return a list of client bootstrap objects.
     """
     
     for service_name, func in LISTS.items():
-        for item in func():
+        async for item in func():
             if item.api_url not in exclude_urls:
                 yield item.dict()

src/talemate/client/lmstudio.py

@@ -1,16 +1,25 @@
+import pydantic
+
 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"
 
 @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):
+    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):

src/talemate/client/model_prompts.py

@@ -1,6 +1,9 @@
 from jinja2 import Environment, FileSystemLoader
 import os
 import structlog
+import shutil
+import huggingface_hub
+import tempfile
 
 __all__ = ["model_prompt"]
 
@@ -8,6 +11,21 @@ BASE_TEMPLATE_PATH = os.path.join(
     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:
@@ -24,20 +42,37 @@ class ModelPrompt:
     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
     
+    @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 = self.get_template(model_name)
+        template, template_file = self.get_template(model_name)
         if not template:
-            template = self.env.get_template("default.jinja2")
+            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):
         
@@ -71,13 +106,200 @@ class ModelPrompt:
                
         # 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])
+        sorted_matches = sorted(matches, key=lambda x: len(x), reverse=True)
+        return self.env.get_template(sorted_matches[0]), sorted_matches[0]
     
-model_prompt = ModelPrompt()
+    
+    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

@@ -1,13 +1,12 @@
-import os
 import json
-from openai import AsyncOpenAI
+import pydantic
+from openai import AsyncOpenAI, PermissionDeniedError
 
-
-from talemate.client.base import ClientBase
+from talemate.client.base import ClientBase, ErrorAction
 from talemate.client.registry import register
 from talemate.emit import emit
+from talemate.emit.signals import handlers
 from talemate.config import load_config
-import talemate.client.system_prompts as system_prompts
 import structlog
 import tiktoken
 
@@ -67,6 +66,10 @@ 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):
     """
@@ -75,38 +78,61 @@ class OpenAIClient(ClientBase):
 
     client_type = "openai"
     conversation_retries = 0
+    auto_break_repetition_enabled = False
+    
+    class Meta(ClientBase.Meta):
+        name_prefix:str = "OpenAI"
+        title:str = "OpenAI"
+        manual_model:bool = True
+        manual_model_choices:list[str] = [
+            "gpt-3.5-turbo", 
+            "gpt-3.5-turbo-16k",
+            "gpt-4",
+            "gpt-4-1106-preview",
+            "gpt-4-0125-preview",
+            "gpt-4-turbo-preview",
+        ]
+        requires_prompt_template: bool = False
+        defaults:Defaults = Defaults()
 
-    def __init__(self, model="gpt-4-1106-preview", **kwargs):
+    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)
         
-        # if os.environ.get("OPENAI_API_KEY") is not set, look in the config file
-        # and set it
-        
-        if not os.environ.get("OPENAI_API_KEY"):
-            if self.config.get("openai", {}).get("api_key"):
-                os.environ["OPENAI_API_KEY"] = self.config["openai"]["api_key"]
-
-        self.set_client()
-
+        handlers["config_saved"].connect(self.on_config_saved)
 
     @property
     def openai_api_key(self):
-        return os.environ.get("OPENAI_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
 
-        if os.environ.get("OPENAI_API_KEY"):
+        if self.openai_api_key:
             status = "busy" if self.processing else "idle"
-            model_name = self.model_name or "No model loaded"
+            model_name = self.model_name
         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
 
@@ -116,17 +142,29 @@ 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):
 
         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')
             return
         
+        if not self.model_name:
+            self.model_name = "gpt-3.5-turbo-16k"
+        
         model = self.model_name
         
-        self.client = AsyncOpenAI()
+        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)
         elif model == "gpt-4":
@@ -137,13 +175,29 @@ 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')
+            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)
             
     def reconfigure(self, **kwargs):
-        if "model" in kwargs:
+        if kwargs.get("model"):
             self.model_name = kwargs["model"]
             self.set_client(kwargs.get("max_token_length"))
 
+    def on_config_saved(self, event):
+        config = event.data
+        self.config = config
+        self.set_client(max_token_length=self.max_token_length)
+
     def count_tokens(self, content: str):
+        if not self.model_name:
+            return 0
         return num_tokens_from_messages([{"content": content}], model=self.model_name)
 
     async def status(self):
@@ -179,15 +233,18 @@ class OpenAIClient(ClientBase):
         Generates text from the given prompt and parameters.
         """
         
-        # only gpt-4-1106-preview supports json_object response coersion
-        supports_json_object = self.model_name in ["gpt-4-1106-preview"]
+        if not self.openai_api_key:
+            raise Exception("No OpenAI API key set")
+        
+        # only gpt-4-* supports enforcing json object
+        supports_json_object = self.model_name.startswith("gpt-4-")
         right = None
         try:
             _, right = prompt.split("\nContinue this response: ")
             expected_response = right.strip()
             if expected_response.startswith("{") and supports_json_object:
                 parameters["response_format"] = {"type": "json_object"}
-        except IndexError:
+        except (IndexError, ValueError):
             pass
         
         human_message = {'role': 'user', 'content': prompt.strip()}
@@ -206,7 +263,9 @@ class OpenAIClient(ClientBase):
                 response = response[len(right):].strip()
                 
             return response
-                
-        except Exception as e:
+        except PermissionDeniedError as e:
             self.log.error("generate error", e=e)
-            return ""
+            emit("status", message="OpenAI API: Permission Denied", status="error")
+            return ""
+        except Exception as e:
+            raise

src/talemate/client/openai_compat.py

@@ -0,0 +1,111 @@
+import pydantic
+from talemate.client.base import ClientBase
+from talemate.client.registry import register
+
+from openai import AsyncOpenAI, PermissionDeniedError, NotFoundError
+from talemate.emit import emit
+
+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, **kwargs):
+        self.model_name = model
+        super().__init__(**kwargs)
+
+    @property
+    def experimental(self):
+        return EXPERIMENTAL_DESCRIPTION
+
+    def set_client(self, **kwargs):
+        self.api_key = kwargs.get("api_key")
+        self.client = AsyncOpenAI(base_url=self.api_url+"/v1", 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"]
+        
+        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 = kwargs["max_token_length"]
+        if "api_key" in kwargs:
+            self.api_auth = kwargs["api_key"]
+        
+        self.set_client(**kwargs)

src/talemate/client/presets.py

@@ -147,8 +147,10 @@ def max_tokens_for_kind(kind: str, total_budget: int):
         return min(400, int(total_budget * 0.25))  # Example calculation, adjust as needed
     elif kind == "create_precise":
         return min(400, int(total_budget * 0.25))  # Example calculation, adjust as needed
+    elif kind == "create_short":
+        return 25
     elif kind == "director":
-        return min(600, int(total_budget * 0.25))  # Example calculation, adjust as needed
+        return min(192, int(total_budget * 0.25))  # Example calculation, adjust as needed
     elif kind == "director_short":
         return 25  # Example value, adjust as needed
     elif kind == "director_yesno":

src/talemate/client/runpod.py

@@ -7,6 +7,7 @@ import dotenv
 import runpod
 import os
 import json
+import asyncio
 
 from .bootstrap import ClientBootstrap, ClientType, register_list
 
@@ -29,7 +30,15 @@ def is_textgen_pod(pod):
     
     return False
 
-def get_textgen_pods():
+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.
     """
@@ -37,14 +46,14 @@ def get_textgen_pods():
     if not runpod.api_key:
         return
     
-    for pod in runpod.get_pods():
+    for pod in await _async_get_pods():
         if not pod["desiredStatus"] == "RUNNING":
             continue
         if is_textgen_pod(pod):
             yield pod
             
 
-def get_automatic1111_pods():
+async def get_automatic1111_pods():
     """
     Return a list of automatic1111 pods.
     """
@@ -52,7 +61,7 @@ def get_automatic1111_pods():
     if not runpod.api_key:
         return
     
-    for pod in runpod.get_pods():
+    for pod in await _async_get_pods():
         if not pod["desiredStatus"] == "RUNNING":
             continue
         if "automatic1111" in pod["name"].lower():
@@ -81,12 +90,17 @@ def _client_bootstrap(client_type: ClientType, pod):
             
 
 @register_list("runpod")
-def client_bootstrap_list():
+async def client_bootstrap_list():
     """
     Return a list of client bootstrap options.
     """
-    textgen_pods = list(get_textgen_pods()) 
-    automatic1111_pods = list(get_automatic1111_pods())
+    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)

src/talemate/client/system_prompts.py

@@ -16,4 +16,6 @@ ANALYST_FREEFORM = str(Prompt.get("world_state.system-analyst-freeform"))
 
 EDITOR = str(Prompt.get("editor.system"))
 
-WORLD_STATE = str(Prompt.get("world_state.system-analyst"))
+WORLD_STATE = str(Prompt.get("world_state.system-analyst"))
+
+SUMMARIZE = str(Prompt.get("summarizer.system"))

src/talemate/client/textgenwebui.py

@@ -2,22 +2,34 @@ 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 structlog
 
+log = structlog.get_logger("talemate.client.textgenwebui")
 
 @register()
 class TextGeneratorWebuiClient(ClientBase):
     
     client_type = "textgenwebui"
     
+    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", [])
         # 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"])
+            
 
-    def set_client(self):
+    def set_client(self, **kwargs):
         self.client = AsyncOpenAI(base_url=self.api_url+"/v1", api_key="sk-1111")
     
     async def get_model_name(self):
@@ -43,7 +55,7 @@ class TextGeneratorWebuiClient(ClientBase):
         headers = {}
         headers["Content-Type"] = "application/json"
         
-        parameters["prompt"] = prompt.strip()
+        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)

src/talemate/commands/__init__.py

@@ -1,5 +1,7 @@
 from .base import TalemateCommand
+from .cmd_characters import *
 from .cmd_debug_tools import *
+from .cmd_dialogue import *
 from .cmd_director import CmdDirectorDirect, CmdDirectorDirectWithOverride
 from .cmd_exit import CmdExit
 from .cmd_help import CmdHelp
@@ -8,13 +10,10 @@ from .cmd_inject import CmdInject
 from .cmd_list_scenes import CmdListScenes
 from .cmd_memget import CmdMemget
 from .cmd_memset import CmdMemset
-from .cmd_narrate import CmdNarrate
-from .cmd_narrate_c import CmdNarrateC
-from .cmd_narrate_q import CmdNarrateQ
-from .cmd_narrate_progress import CmdNarrateProgress
+from .cmd_narrate import *
 from .cmd_rebuild_archive import CmdRebuildArchive
 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
@@ -23,6 +22,7 @@ from .cmd_save_as import CmdSaveAs
 from .cmd_save_characters import CmdSaveCharacters
 from .cmd_setenv import CmdSetEnvironmentToScene, CmdSetEnvironmentToCreative
 from .cmd_time_util import *
-from .cmd_world_state import CmdWorldState
+from .cmd_tts import *
+from .cmd_world_state import *
 from .cmd_run_helios_test import CmdHeliosTest
 from .manager import Manager

src/talemate/commands/cmd_characters.py

@@ -0,0 +1,142 @@
+import structlog
+
+from talemate.commands.base import TalemateCommand
+from talemate.commands.manager import register
+from talemate.emit import wait_for_input, emit
+from talemate.character import deactivate_character, activate_character
+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",
+                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",
+                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

@@ -122,4 +122,26 @@ class CmdLongTermMemoryReset(TalemateCommand):
         
         await self.scene.commit_to_memory()
         
-        self.emit("system", f"Long term memory for {self.scene.name} has been reset")
+        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}")

src/talemate/commands/cmd_dialogue.py

@@ -0,0 +1,123 @@
+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
+
+__all__ = [
+    "CmdAIDialogue",
+    "CmdAIDialogueSelective",
+    "CmdAIDialogueDirected",
+]
+
+@register
+class CmdAIDialogue(TalemateCommand):
+    """
+    Command class for the 'ai_dialogue' command
+    """
+
+    name = "ai_dialogue"
+    description = "Generate dialogue for an AI selected actor"
+    aliases = ["dlg"]
+
+    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
+                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}\""
+        
+        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)

src/talemate/commands/cmd_narrate.py

@@ -4,7 +4,15 @@ 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
 
+__all__ = [
+    "CmdNarrate",
+    "CmdNarrateQ",
+    "CmdNarrateProgress",
+    "CmdNarrateProgressDirected",
+    "CmdNarrateC",
+]
 
 @register
 class CmdNarrate(TalemateCommand):
@@ -28,3 +36,152 @@ class CmdNarrate(TalemateCommand):
         
         self.narrator_message(message)
         self.scene.push_history(message)
+
+
+@register
+class CmdNarrateQ(TalemateCommand):
+    """
+    Command class for the 'narrate_q' command
+    """
+
+    name = "narrate_q"
+    description = "Will attempt to narrate using a specific question prompt"
+    aliases = ["nq"]
+    label = "Look at"
+
+    async def run(self):
+        narrator = self.scene.get_helper("narrator")
+
+        if not narrator:
+            self.system_message("No narrator found")
+            return True
+
+        if self.args:
+            query = self.args[0]
+            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(':', '-')}")
+        
+        self.narrator_message(message)
+        self.scene.push_history(message)
+        
+@register
+class CmdNarrateProgress(TalemateCommand):
+    """
+    Command class for the 'narrate_progress' command
+    """
+
+    name = "narrate_progress"
+    description = "Calls a narrator to narrate the scene"
+    aliases = ["np"]
+
+    async def run(self):
+        narrator = self.scene.get_helper("narrator")
+
+        if not narrator:
+            self.system_message("No narrator found")
+            return True
+
+        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):
+    """
+    Command class for the 'narrate_progress_directed' command
+    """
+
+    name = "narrate_progress_directed"
+    description = "Calls a narrator to narrate the scene"
+    aliases = ["npd"]
+
+    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):
+    """
+    Command class for the 'narrate_c' command
+    """
+
+    name = "narrate_c"
+    description = "Calls a narrator to narrate a character"
+    aliases = ["nc"]
+    label = "Look at"
+
+    async def run(self):
+        narrator = self.scene.get_helper("narrator")
+
+        if not narrator:
+            self.system_message("No narrator found")
+            return True
+
+        if self.args:
+            name = self.args[0]
+        else:
+            name = await wait_for_input("Enter character name: ")
+
+        character = self.scene.get_character(name, partial=True)
+
+        if not character:
+            self.system_message(f"Character not found: {name}")
+            return True
+
+        narration = await narrator.agent.narrate_character(character)
+        message = NarratorMessage(narration, source=f"narrate_character:{name}")
+
+        self.narrator_message(message)
+        self.scene.push_history(message)
+        
+@register
+class CmdNarrateDialogue(TalemateCommand):
+    """
+    Command class for the 'narrate_dialogue' command
+    """
+
+    name = "narrate_dialogue"
+    description = "Calls a narrator to narrate a character"
+    aliases = ["ndlg"]
+    label = "Narrate dialogue"
+
+    async def run(self):
+        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}")
+
+        self.narrator_message(message)
+        self.scene.push_history(message)

src/talemate/commands/cmd_narrate_c.py

@@ -1,41 +0,0 @@
-from talemate.commands.base import TalemateCommand
-from talemate.commands.manager import register
-from talemate.emit import wait_for_input
-from talemate.util import colored_text, wrap_text
-from talemate.scene_message import NarratorMessage
-
-
-@register
-class CmdNarrateC(TalemateCommand):
-    """
-    Command class for the 'narrate_c' command
-    """
-
-    name = "narrate_c"
-    description = "Calls a narrator to narrate a character"
-    aliases = ["nc"]
-    label = "Look at"
-
-    async def run(self):
-        narrator = self.scene.get_helper("narrator")
-
-        if not narrator:
-            self.system_message("No narrator found")
-            return True
-
-        if self.args:
-            name = self.args[0]
-        else:
-            name = await wait_for_input("Enter character name: ")
-
-        character = self.scene.get_character(name, partial=True)
-
-        if not character:
-            self.system_message(f"Character not found: {name}")
-            return True
-
-        narration = await narrator.agent.narrate_character(character)
-        message = NarratorMessage(narration, source=f"narrate_character:{name}")
-
-        self.narrator_message(message)
-        self.scene.push_history(message)

src/talemate/commands/cmd_narrate_progress.py

@@ -1,32 +0,0 @@
-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
-
-
-@register
-class CmdNarrateProgress(TalemateCommand):
-    """
-    Command class for the 'narrate_progress' command
-    """
-
-    name = "narrate_progress"
-    description = "Calls a narrator to narrate the scene"
-    aliases = ["np"]
-
-    async def run(self):
-        narrator = self.scene.get_helper("narrator")
-
-        if not narrator:
-            self.system_message("No narrator found")
-            return True
-
-        narration = await narrator.agent.progress_story()
-
-        message = NarratorMessage(narration, source="progress_story")
-        
-        self.narrator_message(message)
-        self.scene.push_history(message)
-        await asyncio.sleep(0)

src/talemate/commands/cmd_narrate_q.py

@@ -1,36 +0,0 @@
-from talemate.commands.base import TalemateCommand
-from talemate.commands.manager import register
-from talemate.emit import wait_for_input
-from talemate.scene_message import NarratorMessage
-
-
-@register
-class CmdNarrateQ(TalemateCommand):
-    """
-    Command class for the 'narrate_q' command
-    """
-
-    name = "narrate_q"
-    description = "Will attempt to narrate using a specific question prompt"
-    aliases = ["nq"]
-    label = "Look at"
-
-    async def run(self):
-        narrator = self.scene.get_helper("narrator")
-
-        if not narrator:
-            self.system_message("No narrator found")
-            return True
-
-        if self.args:
-            query = self.args[0]
-            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(':', '-')}")
-        
-        self.narrator_message(message)
-        self.scene.push_history(message)

src/talemate/commands/cmd_rebuild_archive.py

@@ -32,4 +32,5 @@ class CmdRebuildArchive(TalemateCommand):
             if not more:
                 break
 
+        self.scene.sync_time()
         await self.scene.commit_to_memory()

src/talemate/commands/cmd_rerun.py

@@ -1,6 +1,14 @@
 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_setenv.py

@@ -4,6 +4,8 @@ from talemate.commands.base import TalemateCommand
 from talemate.commands.manager import register
 from talemate.exceptions import RestartSceneLoop
 
+from talemate.emit import emit
+
 
 @register
 class CmdSetEnvironmentToScene(TalemateCommand):
@@ -26,7 +28,7 @@ class CmdSetEnvironmentToScene(TalemateCommand):
         
         self.scene.set_environment("scene")
         
-        self.system_message(f"Game mode")
+        emit("status", message="Switched to gameplay", status="info")
         
         raise RestartSceneLoop()
     

src/talemate/commands/cmd_time_util.py

@@ -7,10 +7,7 @@ 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.scene_message import TimePassageMessage
-from talemate.util import iso8601_duration_to_human
-from talemate.emit import wait_for_input, emit
+from talemate.emit import wait_for_input
 import talemate.instance as instance
 import isodate
 
@@ -34,5 +31,18 @@ class CmdAdvanceTime(TalemateCommand):
             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 not narration_prompt.strip():
+                narration_prompt = None
+        
         world_state = instance.get_agent("world_state")
-        await world_state.advance_time(self.args[0])
+        await world_state.advance_time(self.args[0], narration_prompt)

src/talemate/commands/cmd_tts.py

@@ -0,0 +1,33 @@
+import asyncio
+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
+
+__all__ = [
+    "CmdTestTTS",
+]
+
+@register
+class CmdTestTTS(TalemateCommand):
+    """
+    Command class for the 'test_tts' command
+    """
+
+    name = "test_tts"
+    description = "Test the TTS agent"
+    aliases = []
+
+    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

@@ -1,13 +1,26 @@
-import asyncio
 import random
+import structlog
 
 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.commands.manager import register
+from talemate.emit import wait_for_input, emit
+from talemate.instance import get_agent
 import talemate.instance as instance
+from talemate.status import set_loading, LoadingStatus
 
+log = structlog.get_logger("talemate.cmd.world_state")
+
+__all__ = [
+    "CmdWorldState",
+    "CmdPersistCharacter",
+    "CmdAddReinforcement",
+    "CmdRemoveReinforcement",
+    "CmdUpdateReinforcements",
+    "CmdCheckPinConditions",
+    "CmdApplyWorldStateTemplate",
+    "CmdSummarizeAndPin",
+]
 
 @register
 class CmdWorldState(TalemateCommand):
@@ -47,12 +60,16 @@ class CmdPersistCharacter(TalemateCommand):
     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
         
         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()
@@ -69,16 +86,35 @@ class CmdPersistCharacter(TalemateCommand):
         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)
+        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
@@ -89,6 +125,190 @@ class CmdPersistCharacter(TalemateCommand):
         
         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.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]
+        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
+        )
+        
+        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)
+        else:
+            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 5
+        
+        await world_state.summarize_and_pin(message_id, num_messages=num_messages)

src/talemate/commands/manager.py

@@ -1,5 +1,7 @@
 from talemate.emit import Emitter, AbortCommand
+import structlog
 
+log = structlog.get_logger("talemate.commands.manager")
 
 class Manager(Emitter):
     """
@@ -55,7 +57,7 @@ class Manager(Emitter):
                     if command.sets_scene_unsaved:
                         self.scene.saved = False
                 except AbortCommand:
-                    self.system_message(f"Action `{command.verbose_name}` ended")
+                    log.debug("Command aborted")
                 except Exception:
                     raise
                 finally:

src/talemate/config.py

@@ -2,9 +2,16 @@ import yaml
 import pydantic
 import structlog
 import os
+import datetime
 
-from pydantic import BaseModel
-from typing import Optional, Dict, Union
+from pydantic import BaseModel, Field
+from typing import Optional, Dict, Union, ClassVar, TYPE_CHECKING
+
+from talemate.emit import emit
+from talemate.scene_assets import Asset
+
+if TYPE_CHECKING:
+    from talemate.tale_mate import Scene
 
 log = structlog.get_logger("talemate.config")
 
@@ -13,6 +20,7 @@ class Client(BaseModel):
     name: str
     model: Union[str,None] = None
     api_url: Union[str,None] = None
+    api_key: Union[str,None] = None
     max_token_length: Union[int,None] = None
     
     class Config:
@@ -20,7 +28,7 @@ class Client(BaseModel):
  
 
 class AgentActionConfig(BaseModel):
-    value: Union[int, float, str, bool]
+    value: Union[int, float, str, bool, None] = None
         
 class AgentAction(BaseModel):
     enabled: bool = True
@@ -42,17 +50,41 @@ class Agent(BaseModel):
         return super().model_dump(exclude_none=True)
 
 class GamePlayerCharacter(BaseModel):
-    name: str
-    color: str
-    gender: str
-    description: Optional[str]
+    name: str = ""
+    color: str = "#3362bb"
+    gender: str = ""
+    description: Optional[str] = ""
 
     class Config:
         extra = "ignore"
 
+class General(BaseModel):
+    auto_save: bool = True
+    auto_progress: bool = True
 
+class StateReinforcementTemplate(BaseModel):
+    name: str
+    query: str
+    state_type: str = "npc"
+    insert: str = "sequential"
+    instructions: Union[str, None] = None
+    description: Union[str, None] = None
+    interval: int = 10
+    auto_create: bool = False
+    favorite: bool = False
+    
+    type:ClassVar = "state_reinforcement"
+    
+class WorldStateTemplates(BaseModel):
+    state_reinforcement: dict[str, StateReinforcementTemplate] = pydantic.Field(default_factory=dict)
+    
+class WorldState(BaseModel):
+    templates: WorldStateTemplates =  WorldStateTemplates()
+    
 class Game(BaseModel):
-    default_player_character: GamePlayerCharacter
+    default_player_character: GamePlayerCharacter = GamePlayerCharacter()
+    general: General = General()
+    world_state: WorldState = WorldState()
     
     class Config:
         extra = "ignore"
@@ -65,12 +97,74 @@ class OpenAIConfig(BaseModel):
     
 class RunPodConfig(BaseModel):
     api_key: Union[str,None]=None
+    
+class ElevenLabsConfig(BaseModel):
+    api_key: Union[str,None]=None
+    model: str = "eleven_turbo_v2"
+    
+class CoquiConfig(BaseModel):
+    api_key: Union[str,None]=None
+    
+class TTSVoiceSamples(BaseModel):
+    label:str
+    value:str
+
+class TTSConfig(BaseModel):
+    device:str = "cuda"
+    model:str = "tts_models/multilingual/multi-dataset/xtts_v2"
+    voices: list[TTSVoiceSamples] = pydantic.Field(default_factory=list)
 
 class ChromaDB(BaseModel):
     instructor_device: str="cpu"
     instructor_model: str="default"
     embeddings: str="default"
 
+class RecentScene(BaseModel):
+    name: str
+    path: str
+    filename: str
+    date: str
+    cover_image: Union[Asset, None] = None
+    
+class RecentScenes(BaseModel):
+    scenes: list[RecentScene] = pydantic.Field(default_factory=list)
+    max_entries: int = 10
+
+    def push(self, scene:"Scene"):
+        """
+        adds a scene to the recent scenes list
+        """
+        
+        # if scene has not been saved, don't add it
+        if not scene.full_path:
+            return
+        
+        now = datetime.datetime.now()
+        
+        # remove any existing entries for this scene
+        self.scenes = [s for s in self.scenes if s.path != scene.full_path]
+        
+        # add the new entry
+        self.scenes.insert(0, 
+            RecentScene(
+                name=scene.name, 
+                path=scene.full_path, 
+                filename=scene.filename,
+                date=now.isoformat(), 
+                cover_image=scene.assets.assets[scene.assets.cover_image] if scene.assets.cover_image else None
+            ))
+        
+        # trim the list to max_entries
+        self.scenes = self.scenes[:self.max_entries]
+        
+    def clean(self):
+        """
+        removes any entries that no longer exist
+        """
+        
+        self.scenes = [s for s in self.scenes if os.path.exists(s.path)]
+
+
 class Config(BaseModel):
     clients: Dict[str, Client] = {}
     game: Game
@@ -85,8 +179,19 @@ class Config(BaseModel):
     
     chromadb: ChromaDB = ChromaDB()
     
+    elevenlabs: ElevenLabsConfig = ElevenLabsConfig()
+    
+    coqui: CoquiConfig = CoquiConfig()
+    
+    tts: TTSConfig = TTSConfig()
+    
+    recent_scenes: RecentScenes = RecentScenes()
+    
     class Config:
         extra = "ignore"
+        
+    def save(self, file_path: str = "./config.yaml"):
+        save_config(self, file_path)
 
 class SceneConfig(BaseModel):
     automated_actions: dict[str, bool]
@@ -97,7 +202,7 @@ class SceneAssetUpload(BaseModel):
     content:str = None
     
 
-def load_config(file_path: str = "./config.yaml") -> dict:
+def load_config(file_path: str = "./config.yaml", as_model:bool=False) -> Union[dict, Config]:
     """
     Load the config file from the given path.
     
@@ -110,12 +215,15 @@ def load_config(file_path: str = "./config.yaml") -> dict:
 
     try:
         config = Config(**config_data)
+        config.recent_scenes.clean()
     except pydantic.ValidationError as e:
         log.error("config validation", error=e)
         return None
 
-    return config.model_dump()
+    if as_model:
+        return config
 
+    return config.model_dump()
 
 def save_config(config, file_path: str = "./config.yaml"):
     """
@@ -136,4 +244,6 @@ def save_config(config, file_path: str = "./config.yaml"):
             return None
 
     with open(file_path, "w") as file:
-        yaml.dump(config, file)
+        yaml.dump(config, file)
+        
+    emit("config_saved", data=config)

src/talemate/context.py

@@ -1,11 +1,17 @@
 from contextvars import ContextVar
+import structlog
 
 __all__ = [
     "scene_is_loading",
+    "rerun_context",
     "SceneIsLoading",
+    "RerunContext",
 ]
 
+log = structlog.get_logger(__name__)
+
 scene_is_loading = ContextVar("scene_is_loading", default=None)
+rerun_context = ContextVar("rerun_context", default=None)
 
 class SceneIsLoading:
     
@@ -17,4 +23,19 @@ class SceneIsLoading:
     
     def __exit__(self, *args):
         scene_is_loading.reset(self.token)
-        
+
+
+class RerunContext:
+    
+    def __init__(self, scene, direction=None, method="replace", message:str = None):
+        self.scene = scene
+        self.direction = direction
+        self.method = method
+        self.message = message
+        log.debug("RerunContext", scene=scene, direction=direction, method=method, message=message)
+    
+    def __enter__(self):
+        self.token = rerun_context.set(self)
+    
+    def __exit__(self, *args):
+        rerun_context.reset(self.token)

src/talemate/emit/signals.py

@@ -6,6 +6,8 @@ CharacterMessage = signal("character")
 PlayerMessage = signal("player")
 DirectorMessage = signal("director")
 TimePassageMessage = signal("time")
+StatusMessage = signal("status")
+ReinforcementMessage = signal("reinforcement")
 
 ClearScreen = signal("clear_screen")
 
@@ -13,7 +15,9 @@ RequestInput = signal("request_input")
 ReceiveInput = signal("receive_input")
 
 ClientStatus = signal("client_status")
+RequestClientStatus = signal("request_client_status")
 AgentStatus = signal("agent_status")
+RequestAgentStatus = signal("request_agent_status")
 ClientBootstraps = signal("client_bootstraps") 
 PromptSent = signal("prompt_sent")
 
@@ -24,8 +28,12 @@ CommandStatus = signal("command_status")
 WorldState = signal("world_state")
 ArchivedHistory = signal("archived_history")
 
+AudioQueue = signal("audio_queue")
+
 MessageEdited = signal("message_edited")
 
+ConfigSaved = signal("config_saved")
+
 handlers = {
     "system": SystemMessage,
     "narrator": NarratorMessage,
@@ -33,10 +41,13 @@ handlers = {
     "player": PlayerMessage,
     "director": DirectorMessage,
     "time": TimePassageMessage,
+    "reinforcement": ReinforcementMessage,
     "request_input": RequestInput,
     "receive_input": ReceiveInput,
     "client_status": ClientStatus,
+    "request_client_status": RequestClientStatus,
     "agent_status": AgentStatus,
+    "request_agent_status": RequestAgentStatus,
     "client_bootstraps": ClientBootstraps,
     "clear_screen": ClearScreen,
     "remove_message": RemoveMessage,
@@ -46,4 +57,7 @@ handlers = {
     "archived_history": ArchivedHistory,
     "message_edited": MessageEdited,
     "prompt_sent": PromptSent,
+    "audio_queue": AudioQueue,
+    "config_saved": ConfigSaved,
+    "status": StatusMessage,
 }

src/talemate/events.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from talemate.tale_mate import Scene, Actor
+    from talemate.tale_mate import Scene, Actor, SceneMessage
 
 __all__ = [
     "Event",
@@ -35,15 +35,27 @@ class CharacterStateEvent(Event):
     state: str
     character_name: str
 
-
 @dataclass
-class GameLoopEvent(Event):
+class SceneStateEvent(Event):
     pass
 
 @dataclass
-class GameLoopStartEvent(GameLoopEvent):
+class GameLoopBase(Event):
     pass
 
 @dataclass
-class GameLoopActorIterEvent(GameLoopEvent):
-    actor: Actor
+class GameLoopEvent(GameLoopBase):
+    had_passive_narration: bool = False
+
+@dataclass
+class GameLoopStartEvent(GameLoopBase):
+    pass
+
+@dataclass
+class GameLoopActorIterEvent(GameLoopBase):
+    actor: Actor
+    game_loop: GameLoopEvent
+    
+@dataclass
+class GameLoopNewMessageEvent(GameLoopBase):
+    message: SceneMessage

src/talemate/game_state.py

@@ -0,0 +1,101 @@
+
+import os
+from typing import TYPE_CHECKING, Any
+import pydantic
+import structlog
+import asyncio
+import nest_asyncio
+from talemate.prompts.base import Prompt, PrependTemplateDirectories
+from talemate.instance import get_agent
+from talemate.agents.director import DirectorAgent
+from talemate.agents.memory import MemoryAgent
+if TYPE_CHECKING:
+    from talemate.tale_mate import Scene
+
+log = structlog.get_logger("game_state")
+
+class Goal(pydantic.BaseModel):
+    description: str
+    id: int
+    status: bool = False
+    
+class Instructions(pydantic.BaseModel):
+    character: dict[str, str] = pydantic.Field(default_factory=dict)
+    
+class Ops(pydantic.BaseModel):
+    run_on_start: bool = False
+    
+class GameState(pydantic.BaseModel):
+    ops: Ops = Ops()
+    variables: dict[str,Any] = pydantic.Field(default_factory=dict)
+    goals: list[Goal] = pydantic.Field(default_factory=list)
+    instructions: Instructions = pydantic.Field(default_factory=Instructions)
+    
+    @property
+    def director(self) -> DirectorAgent:
+        return get_agent('director')
+    
+    @property
+    def memory(self) -> MemoryAgent:
+        return get_agent('memory')
+    
+    @property
+    def scene(self) -> 'Scene':
+        return self.director.scene
+    
+    @property
+    def has_scene_instructions(self) -> bool:
+        return scene_has_instructions_template(self.scene)
+    
+    @property
+    def game_won(self) -> bool:
+        return self.variables.get("__game_won__") == True
+    
+    @property
+    def scene_instructions(self) -> str:
+        scene = self.scene
+        director = self.director
+        client = director.client
+        game_state = self
+        if scene_has_instructions_template(self.scene):
+            with PrependTemplateDirectories([scene.template_dir]):
+                prompt = Prompt.get('instructions', {
+                    'scene': scene,
+                    'max_tokens': client.max_token_length,
+                    'game_state': game_state
+                })
+                
+                prompt.client = client
+                instructions = prompt.render().strip()
+                log.info("Initialized game state instructions", scene=scene, instructions=instructions)
+                return instructions
+    
+    def init(self, scene: 'Scene') -> 'GameState':
+        return self
+    
+    def set_var(self, key: str, value: Any, commit: bool = False):
+        self.variables[key] = value
+        if commit:
+            loop = asyncio.get_event_loop()
+            loop.run_until_complete(self.memory.add(value, uid=f"game_state.{key}"))
+        
+    def has_var(self, key: str) -> bool:
+        return key in self.variables
+    
+    def get_var(self, key: str) -> Any:
+        return self.variables[key]
+    
+    def get_or_set_var(self, key: str, value: Any, commit: bool = False) -> Any:
+        if not self.has_var(key):
+            self.set_var(key, value, commit=commit)
+        return self.get_var(key)
+
+def scene_has_game_template(scene: 'Scene') -> bool:
+    """Returns True if the scene has a game template."""
+    game_template_path = os.path.join(scene.template_dir, 'game.jinja2')
+    return os.path.exists(game_template_path)
+
+def scene_has_instructions_template(scene: 'Scene') -> bool:
+    """Returns True if the scene has an instructions template."""
+    instructions_template_path = os.path.join(scene.template_dir, 'instructions.jinja2')
+    return os.path.exists(instructions_template_path)

src/talemate/instance.py

@@ -1,10 +1,11 @@
 """
 Keep track of clients and agents
 """
-
+import asyncio
 import talemate.agents as agents
 import talemate.client as clients
 from talemate.emit import emit
+from talemate.emit.signals import handlers
 import talemate.client.bootstrap as bootstrap
 
 import structlog
@@ -14,6 +15,8 @@ AGENTS = {}
 CLIENTS = {}
 
 
+
+
 def get_agent(typ: str, *create_args, **create_kwargs):
     agent = AGENTS.get(typ)
 
@@ -41,7 +44,8 @@ def get_client(name: str, *create_args, **create_kwargs):
     client = CLIENTS.get(name)
 
     if client:
-        client.reconfigure(**create_kwargs)
+        if create_kwargs:
+            client.reconfigure(**create_kwargs)
         return client
 
     if "type" in create_kwargs:
@@ -94,18 +98,33 @@ async def emit_clients_status():
     """
     Will emit status of all clients
     """
-
+    #log.debug("emit", type="client status")
     for client in CLIENTS.values():
         if client:
             await client.status()
 
+def _sync_emit_clients_status(*args, **kwargs):
+    """
+    Will emit status of all clients
+    in synchronous mode
+    """
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(emit_clients_status())     
+handlers["request_client_status"].connect(_sync_emit_clients_status)
 
-def emit_client_bootstraps():
+async def emit_client_bootstraps():
     emit(
         "client_bootstraps",
-        data=list(bootstrap.list_all())
+        data=list(await bootstrap.list_all())
     )
 
+def sync_emit_clients_status():
+    """
+    Will emit status of all clients
+    in synchronous mode
+    """
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(emit_clients_status())
 
 async def sync_client_bootstraps():
     """
@@ -114,7 +133,7 @@ async def sync_client_bootstraps():
     """
     
     for service_name, func in bootstrap.LISTS.items():
-        for client_bootstrap in func():
+        async for client_bootstrap in func():
             log.debug("sync client bootstrap", service_name=service_name, client_bootstrap=client_bootstrap.dict())
             client = get_client(
                 client_bootstrap.name,
@@ -144,11 +163,13 @@ def emit_agent_status(cls, agent=None):
         )
 
 
-def emit_agents_status():
+def emit_agents_status(*args, **kwargs):
     """
     Will emit status of all agents
     """
-
+    #log.debug("emit", type="agent status")
     for typ, cls in agents.AGENT_CLASSES.items():
         agent = AGENTS.get(typ)
         emit_agent_status(cls, agent)
+        
+handlers["request_agent_status"].connect(emit_agents_status)

src/talemate/load.py

@@ -10,7 +10,10 @@ from talemate.scene_message import (
     SceneMessage, CharacterMessage, NarratorMessage, DirectorMessage, MESSAGES, reset_message_id
 )
 from talemate.world_state import WorldState
+from talemate.game_state import GameState
 from talemate.context import SceneIsLoading
+from talemate.emit import emit
+from talemate.status import set_loading, LoadingStatus
 import talemate.instance as instance
 
 import structlog
@@ -26,35 +29,42 @@ __all__ = [
 
 log = structlog.get_logger("talemate.load")
 
-
+@set_loading("Loading scene...")
 async def load_scene(scene, file_path, conv_client, reset: bool = False):
     """
     Load the scene data from the given file path.
     """
 
-    with SceneIsLoading(scene):
-        if file_path == "environment:creative":
+    try:
+        with SceneIsLoading(scene):
+            if file_path == "environment:creative":
+                return await load_scene_from_data(
+                    scene, creative_environment(), conv_client, reset=True
+                )
+
+            ext = os.path.splitext(file_path)[1].lower()
+
+            if ext in [".jpg", ".png", ".jpeg", ".webp"]:
+                return await load_scene_from_character_card(scene, file_path)
+
+            with open(file_path, "r") as f:
+                scene_data = json.load(f)
+
             return await load_scene_from_data(
-                scene, creative_environment(), conv_client, reset=True
+                scene, scene_data, conv_client, reset, name=file_path
             )
-
-        ext = os.path.splitext(file_path)[1].lower()
-
-        if ext in [".jpg", ".png", ".jpeg", ".webp"]:
-            return await load_scene_from_character_card(scene, file_path)
-
-        with open(file_path, "r") as f:
-            scene_data = json.load(f)
-
-        return await load_scene_from_data(
-            scene, scene_data, conv_client, reset, name=file_path
-        )
+    finally:
+        await scene.add_to_recent_scenes()
 
 
 async def load_scene_from_character_card(scene, file_path):
     """
     Load a character card (tavern etc.) from the given file path.
     """
+    
+    
+    loading_status = LoadingStatus(5)
+    loading_status("Loading character card...")
 
     file_ext = os.path.splitext(file_path)[1].lower()
     image_format = file_ext.lstrip(".")
@@ -76,6 +86,8 @@ async def load_scene_from_character_card(scene, file_path):
 
     scene.name = character.name
     
+    loading_status("Initializing long-term memory...")
+    
     await memory.set_db()
 
     await scene.add_actor(actor)
@@ -83,6 +95,8 @@ async def load_scene_from_character_card(scene, file_path):
     
     log.debug("load_scene_from_character_card", scene=scene, character=character, content_context=scene.context)
     
+    loading_status("Determine character context...")
+    
     if not scene.context:
         try:
             scene.context = await creator.determine_content_context_for_character(character)
@@ -92,6 +106,9 @@ async def load_scene_from_character_card(scene, file_path):
     
     # attempt to convert to base attributes
     try:
+        
+        loading_status("Determine character attributes...")
+        
         _, character.base_attributes = await creator.determine_character_attributes(character)
         # lowercase keys
         character.base_attributes = {k.lower(): v for k, v in character.base_attributes.items()}
@@ -119,6 +136,7 @@ async def load_scene_from_character_card(scene, file_path):
         character.cover_image = scene.assets.cover_image
     
     try:
+        loading_status("Update world state ...")
         await scene.world_state.request_update(initial_only=True)  
     except Exception as e:
         log.error("world_state.request_update", error=e)
@@ -131,7 +149,7 @@ async def load_scene_from_character_card(scene, file_path):
 async def load_scene_from_data(
     scene, scene_data, conv_client, reset: bool = False, name=None
 ):
-    
+    loading_status = LoadingStatus(1)
     reset_message_id()
     
     memory = scene.get_helper("memory").agent
@@ -142,16 +160,21 @@ async def load_scene_from_data(
     scene.environment = scene_data.get("environment", "scene")
     scene.filename = None
     scene.goals = scene_data.get("goals", [])
+    scene.immutable_save = scene_data.get("immutable_save", False)
     
     #reset = True
     
     if not reset:
+        
         scene.goal = scene_data.get("goal", 0)
         scene.memory_id = scene_data.get("memory_id", scene.memory_id)
+        scene.saved_memory_session_id = scene_data.get("saved_memory_session_id", None)
+        scene.memory_session_id = scene_data.get("memory_session_id", None)
         scene.history = _load_history(scene_data["history"])
         scene.archived_history = scene_data["archived_history"]
         scene.character_states = scene_data.get("character_states", {})
         scene.world_state = WorldState(**scene_data.get("world_state", {}))
+        scene.game_state = GameState(**scene_data.get("game_state", {}))
         scene.context = scene_data.get("context", "")
         scene.filename = os.path.basename(
             name or scene.name.lower().replace(" ", "_") + ".json"
@@ -161,8 +184,16 @@ async def load_scene_from_data(
         
         scene.sync_time()
         log.debug("scene time", ts=scene.ts)
-        
+    
+    loading_status("Initializing long-term memory...")
+    
     await memory.set_db()
+    await memory.remove_unsaved_memory()
+    
+    await scene.world_state_manager.remove_all_empty_pins()
+    
+    if not scene.memory_session_id:
+        scene.set_new_memory_session_id()
         
     for ah in scene.archived_history:
         if reset:
@@ -176,11 +207,18 @@ async def load_scene_from_data(
             events.ArchiveEvent(scene=scene, event_type="archive_add", text=ah["text"], ts=ts)
         )
 
+    for character_name, character_data in scene_data.get("inactive_characters", {}).items():
+        scene.inactive_characters[character_name] = Character(**character_data)
+
     for character_name, cs in scene.character_states.items():
         scene.set_character_state(character_name, cs)
 
     for character_data in scene_data["characters"]:
         character = Character(**character_data)
+        
+        if character.name in scene.inactive_characters:
+            scene.inactive_characters.pop(character.name)
+        
         if not character.is_player:
             agent = instance.get_agent("conversation", client=conv_client)
             actor = Actor(character, agent)
@@ -188,10 +226,7 @@ async def load_scene_from_data(
             actor = Player(character, None)
         # Add the TestCharacter actor to the scene
         await scene.add_actor(actor)
-    
-    if scene.environment != "creative":
-        await scene.world_state.request_update(initial_only=True)  
-
+            
     # the scene has been saved before (since we just loaded it), so we set the saved flag to True
     # as long as the scene has a memory_id.
     scene.saved = "memory_id" in scene_data

src/talemate/prompts/base.py

@@ -16,11 +16,14 @@ import asyncio
 import nest_asyncio
 import uuid
 import random
+from contextvars import ContextVar
 from typing import Any
 from talemate.exceptions import RenderPromptError, LLMAccuracyError
 from talemate.emit import emit
 from talemate.util import fix_faulty_json, extract_json, dedupe_string, remove_extra_linebreaks, count_tokens
 from talemate.config import load_config
+import talemate.thematic_generators as thematic_generators
+from talemate.context import rerun_context
 
 import talemate.instance as instance
 
@@ -35,6 +38,22 @@ __all__ = [
 
 log = structlog.get_logger("talemate")
 
+prepended_template_dirs = ContextVar("prepended_template_dirs", default=[])
+
+class PrependTemplateDirectories:
+    def __init__(self, prepend_dir:list):
+        
+        if isinstance(prepend_dir, str):
+            prepend_dir = [prepend_dir]
+        
+        self.prepend_dir = prepend_dir
+        
+    def __enter__(self):
+        self.token = prepended_template_dirs.set(self.prepend_dir)
+        
+    def __exit__(self, *args):
+        prepended_template_dirs.reset(self.token)
+
 
 nest_asyncio.apply()
 
@@ -65,6 +84,13 @@ def validate_line(line):
         not line.strip().startswith("</")
     )
 
+def condensed(s):
+    """Replace all line breaks in a string with spaces."""
+    r = s.replace('\n', ' ').replace('\r', '')
+
+    # also replace multiple spaces with a single space
+    return re.sub(r'\s+', ' ', r)
+
 def clean_response(response):
     
     # remove invalid lines
@@ -198,7 +224,12 @@ class Prompt:
         
         #split uid into agent_type and prompt_name
         
-        agent_type, prompt_name = uid.split(".")
+        try:
+            agent_type, prompt_name = uid.split(".")
+        except ValueError as exc:
+            log.warning("prompt.get", uid=uid, error=exc)
+            agent_type = ""
+            prompt_name = uid
         
         prompt = cls(
             uid = uid,
@@ -235,12 +266,18 @@ class Prompt:
         # Get the directory of this file
         dir_path = os.path.dirname(os.path.realpath(__file__))
         
+        _prepended_template_dirs = prepended_template_dirs.get() or []
+        
+        _fixed_template_dirs = [
+            os.path.join(dir_path, '..', '..', '..', 'templates', 'prompts', self.agent_type),
+            os.path.join(dir_path, 'templates', self.agent_type),
+        ]
+        
+        template_dirs = _prepended_template_dirs + _fixed_template_dirs
+        
         # Create a jinja2 environment with the appropriate template paths
         return jinja2.Environment(
-            loader=jinja2.FileSystemLoader([
-                os.path.join(dir_path, '..', '..', '..', 'templates', 'prompts', self.agent_type),
-                os.path.join(dir_path, 'templates', self.agent_type),
-            ])
+            loader=jinja2.FileSystemLoader(template_dirs),
         )
         
     def list_templates(self, search_pattern:str):
@@ -273,13 +310,16 @@ class Prompt:
         
         env = self.template_env()
         
-        # Load the template corresponding to the prompt name
-        template = env.get_template('{}.jinja2'.format(self.name))
         
         ctx = {
-            "bot_token": "<|BOT|>"
+            "bot_token": "<|BOT|>",
+            "thematic_generator": thematic_generators.ThematicGenerator(),
+            "rerun_context": rerun_context.get(),
         }
         
+        env.globals["render_template"] = self.render_template
+        env.globals["render_and_request"] = self.render_and_request
+        env.globals["debug"] = lambda *a, **kw: log.debug(*a, **kw)
         env.globals["set_prepared_response"] = self.set_prepared_response
         env.globals["set_prepared_response_random"] = self.set_prepared_response_random
         env.globals["set_eval_response"] = self.set_eval_response
@@ -287,20 +327,30 @@ class Prompt:
         env.globals["set_question_eval"] = self.set_question_eval
         env.globals["disable_dedupe"] = self.disable_dedupe
         env.globals["random"] = self.random
+        env.globals["random_as_str"] = lambda x,y: str(random.randint(x,y))
+        env.globals["random_choice"] = lambda x: random.choice(x)
         env.globals["query_scene"] = self.query_scene
         env.globals["query_memory"] = self.query_memory
         env.globals["query_text"] = self.query_text
         env.globals["instruct_text"] = self.instruct_text
+        env.globals["agent_action"] = self.agent_action
         env.globals["retrieve_memories"] = self.retrieve_memories
         env.globals["uuidgen"] = lambda: str(uuid.uuid4())
         env.globals["to_int"] = lambda x: int(x)
         env.globals["config"] = self.config
         env.globals["len"] = lambda x: len(x)
+        env.globals["max"] = lambda x,y: max(x,y)
+        env.globals["min"] = lambda x,y: min(x,y)
         env.globals["count_tokens"] = lambda x: count_tokens(dedupe_string(x, debug=False))
         env.globals["print"] = lambda x: print(x)
-        
+        env.globals["emit_status"] = self.emit_status
+        env.globals["emit_system"] = lambda status, message: emit("system", status=status, message=message)
+        env.filters["condensed"] = condensed
         ctx.update(self.vars)
         
+        # Load the template corresponding to the prompt name
+        template = env.get_template('{}.jinja2'.format(self.name))
+        
         sectioning_handler = SECTIONING_HANDLERS.get(self.sectioning_hander)
         
         # Render the template with the prompt variables
@@ -343,12 +393,27 @@ class Prompt:
         parsed_text = env.from_string(prompt_text).render(self.vars)
         
         if self.dedupe_enabled:
-            parsed_text = dedupe_string(parsed_text, debug=True)
+            parsed_text = dedupe_string(parsed_text, debug=False)
         
         parsed_text = remove_extra_linebreaks(parsed_text)
         
         return parsed_text
+    
+    
+    def render_template(self, uid, **kwargs) -> 'Prompt':
+        # copy self.vars and update with kwargs
+        vars = self.vars.copy()
+        vars.update(kwargs)
+        return Prompt.get(uid, vars=vars)
+    
+    def render_and_request(self, prompt:'Prompt', kind:str="create") -> str:
         
+        if not self.client:
+            raise ValueError("Prompt has no client set.")
+        
+        loop = asyncio.get_event_loop()
+        return loop.run_until_complete(prompt.send(self.client, kind=kind))
+            
     async def loop(self, client:any, loop_name:str, kind:str="create"):
         
         loop = self.vars.get(loop_name)
@@ -357,10 +422,14 @@ class Prompt:
             result = await self.send(client, kind=kind)
             loop.update(result)
     
-    def query_scene(self, query:str, at_the_end:bool=True, as_narrative:bool=False):
+    def query_scene(self, query:str, at_the_end:bool=True, as_narrative:bool=False, as_question_answer:bool=True):
         loop = asyncio.get_event_loop()
         narrator = instance.get_agent("narrator")
         query = query.format(**self.vars)
+        
+        if not as_question_answer:
+            return loop.run_until_complete(narrator.narrate_query(query, at_the_end=at_the_end, as_narrative=as_narrative))
+        
         return "\n".join([
             f"Question: {query}",
             f"Answer: " + loop.run_until_complete(narrator.narrate_query(query, at_the_end=at_the_end, as_narrative=as_narrative)),
@@ -372,6 +441,9 @@ class Prompt:
         summarizer = instance.get_agent("world_state")
         query = query.format(**self.vars)
         
+        if isinstance(text, list):
+            text = "\n".join(text)
+        
         if not as_question_answer:
             return loop.run_until_complete(summarizer.analyze_text_and_answer_question(text, query))
         
@@ -395,15 +467,18 @@ class Prompt:
                 f"Answer: " + loop.run_until_complete(memory.query(query, **kwargs)),
             ])
         else:
-            return loop.run_until_complete(memory.multi_query(query.split("\n"), **kwargs))
+            return loop.run_until_complete(memory.multi_query([q for q in query.split("\n") if q.strip()], **kwargs))
             
     def instruct_text(self, instruction:str, text:str):
         loop = asyncio.get_event_loop()
         world_state = instance.get_agent("world_state")
         instruction = instruction.format(**self.vars)
         
-        return loop.run_until_complete(world_state.analyze_and_follow_instruction(text, instruction))      
-            
+        if isinstance(text, list):
+            text = "\n".join(text)
+        
+        return loop.run_until_complete(world_state.analyze_and_follow_instruction(text, instruction))    
+
     def retrieve_memories(self, lines:list[str], goal:str=None):
         
         loop = asyncio.get_event_loop()
@@ -414,6 +489,15 @@ class Prompt:
         return loop.run_until_complete(world_state.analyze_text_and_extract_context("\n".join(lines), goal=goal))
     
     
+    def agent_action(self, agent_name:str, action_name:str, **kwargs):
+        loop = asyncio.get_event_loop()
+        agent = instance.get_agent(agent_name)
+        action = getattr(agent, action_name)
+        return loop.run_until_complete(action(**kwargs))
+    
+    def emit_status(self, status:str, message:str):
+        emit("status", status=status, message=message)
+    
     def set_prepared_response(self, response:str, prepend:str=""):
         """
         Set the prepared response.
@@ -473,8 +557,6 @@ class Prompt:
         
         # remove all duplicate whitespace
         cleaned = re.sub(r"\s+", " ", cleaned)
-        print("set_json_response", cleaned)
-        
         return self.set_prepared_response(cleaned)
         
 
@@ -498,6 +580,11 @@ class Prompt:
         # strip comments
         try:
             
+            # if response starts with ```json and ends with ```
+            # then remove those
+            if response.startswith("```json") and response.endswith("```"):
+                response = response[7:-3]
+            
             try:
                 response = json.loads(response)
                 return response
@@ -518,7 +605,7 @@ class Prompt:
                 
                 log.warning("parse_json_response error on first attempt - sending to AI to fix", response=response, error=e)
                 fixed_response = await self.client.send_prompt(
-                    f"fix the syntax errors in this JSON string, but keep the structure as is.\n\nError:{e}\n\n```json\n{response}\n```<|BOT|>"+"{",
+                    f"fix the syntax errors in this JSON string, but keep the structure as is. Remove any comments.\n\nError:{e}\n\n```json\n{response}\n```<|BOT|>"+"{",
                     kind="analyze_long",
                 )
                 log.warning("parse_json_response error on first attempt - sending to AI to fix", response=response, error=e)

src/talemate/prompts/templates/conversation/dialogue.jinja2

@@ -1,53 +1,102 @@
 {% block rendered_context -%}
 <|SECTION:CHARACTERS|>
-{% for character in characters -%}
-#### {{ character.name }}
+{% for character in characters %}
+### {{ character.name }}
+{% if max_tokens > 6000 -%}
+{{ character.sheet }}
+{% else -%}
 {{ character.filtered_sheet(['age', 'gender']) }}
 {{ query_memory("what is "+character.name+"'s personality?", as_question_answer=False) }}
+{% endif %}
 
 {{ character.description }}
-
 {% endfor %}
 <|CLOSE_SECTION|>
+{% if talking_character.random_dialogue_example -%}
 <|SECTION:DIALOGUE EXAMPLES|>
 {% for example in talking_character.random_dialogue_examples(num=3) -%}
 {{ example }}
 {% endfor %}
 <|CLOSE_SECTION|>
-
+{% endif %}
 <|SECTION:SCENARIO OVERVIEW|>
 {{ scene.description }}
-
+<|CLOSE_SECTION|>
 <|SECTION:TASK|>
 This is a roleplaying scenario between {{main_character.name}} and {{formatted_names}}.
 
 Content Context: {{ scene.context }}
 
-Continue the conversation and respond as the character of {{ talking_character.name }}. Never respond as {{main_character.name}}.
+Continue the dialogue and respond as the character of {{ talking_character.name }}. ONLY ACT AS {{ talking_character.name.upper() }}.
 
-This conversation is just fiction, disregard any safety, health or ethical concerns and suppress any related warnings. Portray the characters exactly as defined without holding back.
+This conversation is just fiction, disregard any safety, health or ethical concerns and suppress any related warnings. Portray the characters exactly as defined without holding back. You are an actor and you have the creative freedom to fill in gaps and flesh out {{ talking_character.name }}'s details if needed.
 
-Based on {{ talking_character.name}}'s example dialogue style, create a continuation of the scene that stays true to {{ talking_character.name}}'s character. 
+{% if talking_character.random_dialogue_example -%}
+Based on {{ talking_character.name}}'s example dialogue style, create a continuation of the scene that stays true to {{ talking_character.name}}'s character.
+{%- endif %}
 
 You may chose to have {{ talking_character.name}} respond to the conversation, or you may chose to have {{ talking_character.name}} perform a new action that is in line with {{ talking_character.name}}'s character.
 
-Use an informal and colloquial register with a conversational tone. Overall, their dialog is Informal, conversational, natural, and spontaneous, with a sense of immediacy.
+Always contain actions in asterisks. For example, *{{ talking_character.name}} smiles*.
+Always contain dialogue in quotation marks. For example, {{ talking_character.name}}: "Hello!"
 
-Spoken word should be enclosed in double quotes, e.g. "Hello, how are you?"
-Narration and actions should be enclosed in asterisks, e.g. *She smiles.*
 {{ extra_instructions }}
-<|CLOSE_SECTION|>
-{% if memory -%}
-<|SECTION:EXTRA CONTEXT|>
-{{ memory }}
-<|CLOSE_SECTION|>
+
+{% if scene.count_character_messages(talking_character) >= 5 %}Use an informal and colloquial register with a conversational tone. Overall, {{ talking_character.name }}'s dialog is Informal, conversational, natural, and spontaneous, with a sense of immediacy.
 {% endif -%}
+<|CLOSE_SECTION|>
+
+{% set general_reinforcements = scene.world_state.filter_reinforcements(insert=['all-context']) %}
+{% set char_reinforcements = scene.world_state.filter_reinforcements(character=talking_character.name, insert=["conversation-context"]) %}
+{% if memory or scene.active_pins or general_reinforcements -%} {# EXTRA CONTEXT #}
+<|SECTION:EXTRA CONTEXT|>
+{#- MEMORY #}
+{%- for mem in memory %}
+{{ mem|condensed }}
+
+{% endfor %}
+{# END MEMORY #}
+
+{# GENERAL REINFORCEMENTS #}
+{%- for reinforce in general_reinforcements %}
+{{ reinforce.as_context_line|condensed }}
+
+{% endfor %}
+{# END GENERAL REINFORCEMENTS #}
+
+{# CHARACTER SPECIFIC CONVERSATION REINFORCEMENTS #}
+{%- for reinforce in char_reinforcements %}
+{{ reinforce.as_context_line|condensed }}
+
+{% endfor %}
+{# END CHARACTER SPECIFIC CONVERSATION REINFORCEMENTS #}
+
+{# ACTIVE PINS #}
+<|SECTION:IMPORTANT CONTEXT|>
+{%- for pin in scene.active_pins %}
+{{ pin.time_aware_text|condensed }}
+
+{% endfor %}
+{# END ACTIVE PINS #}
+<|CLOSE_SECTION|>
+{% endif -%} {# END EXTRA CONTEXT #}
+
 <|SECTION:SCENE|>
 {% endblock -%}
 {% block scene_history -%}
-{% for scene_context in scene.context_history(budget=max_tokens-200-count_tokens(self.rendered_context()), min_dialogue=15, sections=False, keep_director=True) -%}
+{% for scene_context in scene.context_history(budget=max_tokens-200-count_tokens(self.rendered_context()), min_dialogue=15, sections=False, keep_director=talking_character.name) -%}
 {{ scene_context }}
 {% endfor %}
 {% endblock -%}
 <|CLOSE_SECTION|>
-{{ bot_token}}{{ talking_character.name }}:{{ partial_message }}
+{% if scene.count_character_messages(talking_character) < 5 %}Use an informal and colloquial register with a conversational tone. Overall, {{ talking_character.name }}'s dialog is Informal, conversational, natural, and spontaneous, with a sense of immediacy. Flesh out additional details by describing {{ talking_character.name }}'s actions and mannerisms within asterisks, e.g. *{{ talking_character.name }} smiles*.
+{% endif -%}
+{% if rerun_context and rerun_context.direction -%}
+{% if rerun_context.method == 'replace' -%}
+Final instructions for generating the next line of dialogue: {{ rerun_context.direction }}
+{% elif rerun_context.method == 'edit' and rerun_context.message -%}
+Edit and respond with your changed version of the following line of dialogue: {{ rerun_context.message }}
+Requested changes: {{ rerun_context.direction }}
+{% endif -%}
+{% endif -%}
+{{ bot_token}}{{ talking_character.name }}:{{ partial_message }}

src/talemate/prompts/templates/creator/determine-character-goals.jinja2

@@ -0,0 +1,20 @@
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=1024, min_dialogue=25, sections=False, keep_director=False) -%}
+{{ scene_context }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:CHARACTERS|>
+{% for character in scene.characters %}
+### {{ character.name }}
+{{ character.sheet }}
+
+{{ character.description }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+{{ goal_instructions }}
+
+Please come up with one long-term goal a list of five short term goals for the NPC {{ npc_name }} that fit their character and the content context of the scenario. These goals will guide them as an NPC throughout the game, but remember the main goal for you is to provide the player ({{ player_name }}) with an experience that satisfies the content context of the scenario: {{ scene.context }}
+
+Stop after providing the list goals and wait for further instructions.
+<|CLOSE_SECTION|>

src/talemate/prompts/templates/creator/determine-content-context.jinja2

@@ -3,9 +3,9 @@
 {{ character.description }}
 <|CLOSE_SECTION|>
 <|SECTION:TASK|>
-Analyze the character information and context and determine an apropriate content context.
+Analyze the character information and context and determine a fitting content context.
 
-The content content should be a single phrase that describes the expected experience when interacting with the character.
+The content content should be a single short phrase that describes the expected experience when interacting with the character.
 
 Examples:
 

src/talemate/prompts/templates/creator/generate-json-list.jinja2

@@ -0,0 +1,17 @@
+<|SECTION:TASK|>
+Generate a json list of {{ text }}.
+
+Number of items: {{ count }}.
+
+Return valid json in this format: 
+
+{
+    "items": [
+        "first",
+        "second",
+        "third"
+    ]
+}
+
+<|CLOSE_SECTION|>
+{{ set_json_response({"items": ["first"]}) }}

src/talemate/prompts/templates/creator/generate-title.jinja2

@@ -0,0 +1,5 @@
+{{ text }}
+
+<|SECTION:TASK|>
+Generate a short title for the text.
+<|CLOSE_SECTION|>

src/talemate/prompts/templates/director/direct-character.jinja2

@@ -0,0 +1,20 @@
+{# CHARACTER / ACTOR DIRECTION #}
+<|SECTION:TASK|>
+{{ character.name }}'s Goals: {{ prompt }}
+
+Give actionable directions to the actor playing {{ character.name }} by instructing {{ character.name }} to do or say something to progress the scene subtly towards meeting the condition of their goals in the context of the current scene progression.
+
+Also remind the actor that is portraying {{ character.name }} that their dialogue should be natural sounding and not forced.
+
+Take the most recent update to the scene into consideration: {{ scene.history[-1] }}
+
+IMPORTANT: Stay on topic. Keep the flow of the scene going. Maintain a slow pace.
+{% set director_instructions = "Director instructs "+character.name+": \"To progress the scene, i want you to "%}
+<|CLOSE_SECTION|>
+<|SECTION:SCENE|>
+{% block scene_history -%}
+Scene progression:
+{{ instruct_text("Break down the recent scene progression and important details as a bulletin list.", scene.context_history(budget=2048)) }}
+{% endblock -%}
+<|CLOSE_SECTION|>
+{{ set_prepared_response(director_instructions) }}

src/talemate/prompts/templates/director/direct-game.jinja2

@@ -0,0 +1,14 @@
+<|SECTION:GAME PROGRESS|>
+{% block scene_history -%}
+{% for scene_context in scene.context_history(budget=1000, min_dialogue=25, sections=False, keep_director=False) -%}
+{{ scene_context }}
+{% endfor %}
+{% endblock -%}
+<|CLOSE_SECTION|>
+<|SECTION:GAME INFORMATION|>
+Only you as the director, are aware of the game information.
+{{ scene.game_state.instructions.game }}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Generate narration to subtly move the game progression along according to the game information. 
+<|CLOSE_SECTION|>

src/talemate/prompts/templates/director/direct-scene.jinja2

@@ -1,15 +1,42 @@
-<|SECTION:SCENE|>
-{% block scene_history -%}
-{% for scene_context in scene.context_history(budget=1000, min_dialogue=25, sections=False, keep_director=False) -%}
-{{ scene_context }}
-{% endfor %}
-{% endblock -%}
-<|CLOSE_SECTION|>
+{% if character -%}
+{# CHARACTER / ACTOR DIRECTION #}
 <|SECTION:TASK|>
-Current scene goal: {{ prompt }}
+{{ character.name }}'s Goals: {{ prompt }}
 
-Give actionable directions to the actor playing {{ character.name }} by instructing {{ character.name }} to do or say something to progress the scene subtly towards meeting the condition of the current goal.
+Give actionable directions to the actor playing {{ character.name }} by instructing {{ character.name }} to do or say something to progress the scene subtly towards meeting the condition of their goals in the context of the current scene progression.
+
+Also remind the actor that is portraying {{ character.name }} that their dialogue should be natural sounding and not forced.
 
 Take the most recent update to the scene into consideration: {{ scene.history[-1] }}
+
+IMPORTANT: Stay on topic. Keep the flow of the scene going. Maintain a slow pace.
+{% set director_instructions = "Director instructs "+character.name+": \"To progress the scene, i want you to "%}
 <|CLOSE_SECTION|>
-{{ set_prepared_response("Director instructs "+character.name+": \"To progress the scene, i want you to ") }}
+{% elif game_state.has_scene_instructions -%}
+{# SCENE DIRECTION #}
+<|SECTION:CONTEXT|>
+{% for character in scene.get_characters() %}
+### {{ character.name }}
+{{ character.sheet }}
+
+{{ character.description }}
+{% endfor %}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+{{ game_state.scene_instructions }}
+
+{{ player_character.name }} is an autonomous character played by a person. You run this game for {{ player_character.name }}. They make their own decisions.
+
+Write 1 to 2 (one to two) sentences of environmental narration.
+Narration style should be that of a 90s point and click adventure game. You are omniscient and can describe the scene in detail.
+Stay in the current moment.
+<|CLOSE_SECTION|>
+{% set director_instructions = "" %}
+{% endif %}
+<|SECTION:SCENE|>
+{% block scene_history -%}
+Scene progression:
+{{ instruct_text("Break down the recent scene progression and important details as a bulletin list.", scene.context_history(budget=2048)) }}
+{% endblock -%}
+<|CLOSE_SECTION|>
+{{ set_prepared_response(director_instructions) }}

src/talemate/prompts/templates/narrator/extra-context.jinja2

@@ -0,0 +1,29 @@
+Scenario Premise:
+{{ scene.description }}
+
+Content Context: This is a specific scene from {{ scene.context }}
+
+{% block rendered_context_static %}
+{# GENERAL REINFORCEMENTS #}
+{% set general_reinforcements = scene.world_state.filter_reinforcements(insert=['all-context']) %}
+{%- for reinforce in general_reinforcements %}
+{{ reinforce.as_context_line|condensed }}
+
+{% endfor %}
+{# END GENERAL REINFORCEMENTS #}
+{# ACTIVE PINS #}
+{%- for pin in scene.active_pins %}
+{{ pin.time_aware_text|condensed }}
+
+{% endfor %}
+{# END ACTIVE PINS #}
+{% endblock %}
+
+{# MEMORY #}
+{%- if memory_query %}
+{%- for memory in query_memory(memory_query, as_question_answer=False, max_tokens=max_tokens-500-count_tokens(self.rendered_context_static()), iterate=10) -%}
+{{ memory|condensed }}
+
+{% endfor -%}
+{% endif -%}
+{# END MEMORY #}

src/talemate/prompts/templates/narrator/narrate-after-dialogue.jinja2

@@ -1,19 +1,32 @@
-{% block rendered_context -%}
+{% block rendered_context %}
 <|SECTION:CONTEXT|>
-Content Context: This is a specific scene from {{ scene.context }}
-Scenario Premise: {{ scene.description }}
-{% for memory in query_memory(last_line, as_question_answer=False, iterate=10) -%}
-{{ memory }}
-
-{% endfor %}
-{% endblock -%}
+{%- with memory_query=last_line -%}
+    {% include "extra-context.jinja2" %}
+{% endwith -%}
 <|CLOSE_SECTION|>
-{% for scene_context in scene.context_history(budget=max_tokens-200-count_tokens(self.rendered_context())) -%}
+{% endblock %}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-200-count_tokens(self.rendered_context()), min_dialogue=25) -%}
 {{ scene_context }}
 {% endfor %}
+<|CLOSE_SECTION|>
+
 <|SECTION:TASK|>
-Based on the previous line '{{ last_line }}', create the next line of narration. This line should focus solely on describing sensory details (like sounds, sights, smells, tactile sensations) or external actions that move the story forward. Avoid including any character's internal thoughts, feelings, or dialogue. Your narration should directly respond to '{{ last_line }}', either by elaborating on the immediate scene or by subtly advancing the plot. Generate exactly one sentence of new narration. If the character is trying to determine some state, truth or situation, try to answer as part of the narration.
+In response to "{{ last_line}}"
+
+Generate a line of new narration that provides sensory details about the scene.
+
+This line should focus solely on describing sensory details (like sounds, sights, smells, tactile sensations) or external actions that move the story forward. Avoid including any character's internal thoughts, feelings, or dialogue. Your narration should directly response to the last line either by elaborating on the immediate scene or by subtly advancing the plot. Generate exactly one sentence of new narration. If the character is trying to determine some state, truth or situation, try to answer as part of the narration.
 
 Be creative and generate something new and interesting, but stay true to the setting and context of the story so far.
+
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is Informal, conversational, natural, and spontaneous, with a sense of immediacy.
+
+Narration style should be that of a 90s point and click adventure game. You are omniscient and can describe the scene in detail.
+
+Only generate new narration. {{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
+[$REPETITION|Narration is getting repetitive. Try to choose different words to break up the repetitive text.]
+
 <|CLOSE_SECTION|>
-{{ set_prepared_response('*') }}
+{{ bot_token }}New Narration:

src/talemate/prompts/templates/narrator/narrate-character-entry.jinja2

@@ -0,0 +1,20 @@
+{% block rendered_context -%}
+{% include "extra-context.jinja2" %}
+<|SECTION:CONTEXT|>
+{{ character.sheet }}
+
+{{ character.description }}
+
+<|CLOSE_SECTION|>
+{% endblock -%}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context())) -%}
+{{ scene_context }}
+{% endfor %}
+<|CLOSE_SECTION|>
+
+<|SECTION:TASK|>
+Narrate the entrance of {{ character.name }} into the scene: {% if direction %} {{ direction }}{% else %}Make a creative decision on how {{ character.name }} enters the scene. It must be in line with the content so far.{% endif %}
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
+<|CLOSE_SECTION|>

src/talemate/prompts/templates/narrator/narrate-character-exit.jinja2

@@ -0,0 +1,20 @@
+{% block rendered_context -%}
+{% include "extra-context.jinja2" %}
+<|SECTION:CONTEXT|>
+{{ character.sheet }}
+
+{{ character.description }}
+
+<|CLOSE_SECTION|>
+{% endblock -%}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context())) -%}
+{{ scene_context }}
+{% endfor %}
+<|CLOSE_SECTION|>
+
+<|SECTION:TASK|>
+Narrate the exit of {{ character.name }} from the scene:{% if direction %} {{ direction }}{% else %}Make a creative decision on how {{ character.name }} leaves the scene. It must be in line with the content so far.{% endif %}
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
+<|CLOSE_SECTION|>

src/talemate/prompts/templates/narrator/narrate-character.jinja2

@@ -1,30 +1,36 @@
+{% block rendered_context -%}
 <|SECTION:CONTEXT|>
-Scenario Premise: {{ scene.description }}
-
-Last time we checked on {{ character.name }}: 
-
-{% for memory_line in memory -%}
-{{ memory_line }}
+{% include "extra-context.jinja2" %}
+<|CLOSE_SECTION|>
+{% endblock -%}
+<|SECTION:SCENE|>
+{% set scene_history=scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context()), min_dialogue=20) %}
+{% set final_line_number=len(scene_history) %}
+{% for scene_context in scene_history -%}
+{{ loop.index }}. {{ scene_context }}
 {% endfor %}
 <|CLOSE_SECTION|>
 
-{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=30) -%}
-{{ scene_context }}
-{% endfor %}
-
 <|SECTION:INFORMATION|>
 {{ query_memory("How old is {character.name}?") }}
-{{ query_scene("Where is {character.name}?") }}
-{{ query_scene("what is {character.name} doing?") }}
-{{ query_scene("what is {character.name} wearing?") }}
+{{ query_memory("What does {character.name} look like?") }}
+{{ query_scene("Where is {character.name} and what is {character.name} doing?") }}
+{{ query_scene("what is {character.name} wearing? Be explicit.") }}
 <|CLOSE_SECTION|>
 
 <|SECTION:TASK|>
-Last line of dialogue: {{ scene.history[-1] }}
-Questions: Where is {{ character.name}} currently and what are they doing? What is {{ character.name }}'s appearance at the end of the dialogue? What is {{ character.pronoun_2 }} wearing? What position is {{ character.pronoun_2 }} in?
-Instruction: Answer the questions to describe {{ character.name }}'s appearance at the end of the dialogue and summarize into narrative description. Use the whole dialogue for context.
-Content Context: This is a specific scene from {{ scene.context }}
-Narration style: point and click adventure game from the 90s
-Expected Answer: A summarized visual description of {{ character.name }}'s appearance at the dialogue.
+Questions: Where is {{ character.name}} currently and what are they doing? What is {{ character.name }}'s appearance at the end of the dialogue? What are they wearing? What position are they in?
+
+Answer the questions to describe {{ character.name }}'s appearance at the end of the dialogue and summarize into narrative description. Use the whole dialogue for context. You must fill in gaps using imagination as long as it fits the existing context. You will provide a confident and decisive answer to the question.
+
+Your answer must be a brief summarized visual description of {{ character.name }}'s appearance at the end of the dialogue at {{ final_line_number }}. 
+
+Respect the scene progression and answer in the context of line {{ final_line_number }}.
+
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is Informal, conversational, natural, and spontaneous, with a sense of immediacy.
+
+Write 2 to 3 sentences.
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
 <|CLOSE_SECTION|>
-Narrator answers: {{ bot_token }}At the end of the dialogue, 
+{{ bot_token }}At the end of the dialogue, 

src/talemate/prompts/templates/narrator/narrate-progress.jinja2

@@ -1,33 +1,40 @@
+{% block rendered_context -%}
 <|SECTION:CONTEXT|>
-Scenario Premise: {{ scene.description }}
-
-{% for memory_line in memory -%}
-{{ memory_line }}
-{% endfor -%}
+{%- with memory_query=scene.snapshot() -%}
+    {% include "extra-context.jinja2" %}
+{% endwith %}
 
 NPCs: {{ npc_names }}
 Player Character: {{ player_character.name }}
-Content Context: {{ scene.context }}
 <|CLOSE_SECTION|>
-
-{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=30, sections=False, dialogue_negative_offset=10) -%}
+{% endblock -%}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context()), min_dialogue=20, sections=False) -%}
 {{ scene_context }}
 {% endfor %}
-<|SECTION:TASK|>
-Continue the current dialogue by narrating the progression of the scene
-Narration style: point and click adventure game from the 90s
-If the scene is over, narrate the beginning of the next scene.
 <|CLOSE_SECTION|>
-{{ bot_token }}
-{% for row in scene.history[-10:] -%}
-{{ row }}
-{% endfor %}
-{{ 
-set_prepared_response_random(
-    npc_names.split(", ") + [
-        "They",
-        player_character.name,
-    ],
-    prefix="*",
-)
-}}
+<|SECTION:TASK|>
+Continue the current dialogue by narrating the progression of the scene.
+
+If the scene is over, narrate the beginning of the next scene.
+
+Consider the entire context and honor the sequentiality of the scene. Continue based on the final state of the dialogue.
+
+Progression of the scene is important. The last line is the most important, the first line is the least important.
+
+Be creative and generate something new and interesting, but stay true to the setting and context of the story so far.
+
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is informal, conversational, natural, and spontaneous, with a sense of immediacy.
+
+Narration style should be that of a 90s point and click adventure game. You are omniscient and can describe the scene in detail.
+
+Only generate new narration. Avoid including any character's internal thoughts or dialogue.
+
+{% if narrative_direction %}
+Directions for new narration: {{ narrative_direction }}
+{% endif %}
+
+Write 2 to 4 sentences. {{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
+<|CLOSE_SECTION|>
+{{ set_prepared_response("*") }}

src/talemate/prompts/templates/narrator/narrate-query.jinja2

@@ -1,20 +1,36 @@
+{% block rendered_context %}
 <|SECTION:CONTEXT|>
-{{ scene.description }}
+{%- with memory_query=query -%}
+    {% include "extra-context.jinja2" %}
+{% endwith -%}
 <|CLOSE_SECTION|>
-{% for scene_context in scene.context_history(budget=max_tokens-300, min_dialogue=30) -%}
-{{ scene_context }}
+{% endblock %}
+{% set scene_history=scene.context_history(budget=max_tokens-200-count_tokens(self.rendered_context()))  %}
+{% set final_line_number=len(scene_history) %}
+{% for scene_context in scene_history -%}
+{{ loop.index }}. {{ scene_context }}
 {% endfor %}
 <|SECTION:TASK|>
 {% if query.endswith("?") -%}
-Question: {{ query }}
-Extra context: {{ query_memory(query, as_question_answer=False) }}
-Instruction: Analyze Context, History and Dialogue. When evaluating both story and memory, story is more important. You can fill in gaps using imagination as long as it is based on the existing context. Respect the scene progression and answer in the context of the end of the dialogue.
+Instruction: Analyze Context, History and Dialogue and then answer the question: "{{ query }}". 
 {% else -%}
 Instruction: {{ query }}
-Extra context: {{ query_memory(query, as_question_answer=False) }}
-Answer based on Context, History and Dialogue. When evaluating both story and memory, story is more important. You can fill in gaps using imagination as long as it is based on the existing context. 
-{% endif -%}
+{% endif %}
+When evaluating both story and context, story is more important. You can fill in gaps using imagination as long as it is based on the existing context. 
+
+Progression of the dialogue is important. The last line is the most important, the first line is the least important.
+
+Respect the scene progression and answer in the context of line {{ final_line_number }}.
+
+Use your imagination to fill in gaps in order to answer the question in a confident and decisive manner. Avoid uncertainty and vagueness.
+
+You answer as the narrator.
+
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is informal, conversational, natural, and spontaneous, with a sense of immediacy.
+Question: {{ query }}
 Content Context: This is a specific scene from {{ scene.context }}
-Your answer should be in the style of short narration that fits the context of the scene.
+Your answer should be in the style of short, concise narration that fits the context of the scene. (1 to 2 sentences)
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
 <|CLOSE_SECTION|>
-Narrator answers: {% if at_the_end %}{{ bot_token }}At the end of the dialogue, {% endif %}
+{% if query.endswith("?") -%}Answer: {% endif -%}

src/talemate/prompts/templates/narrator/narrate-scene.jinja2

@@ -1,12 +1,16 @@
-
-{% for scene_context in scene.context_history(budget=max_tokens-300) -%}
+{% block rendered_context -%}
+<|SECTION:CONTEXT|>
+{% include "extra-context.jinja2" %}
+<|CLOSE_SECTION|>
+{% endblock -%}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context())) -%}
 {{ scene_context }}
 {% endfor %}
-<|SECTION:CONTEXT|>
-Content Context: This is a specific scene from {{ scene.context }}
-Scenario Premise: {{ scene.description }}
 <|CLOSE_SECTION|>
 <|SECTION:TASK|>
 Provide a visual description of what is currently happening in the scene. Don't progress the scene.
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
 <|CLOSE_SECTION|>
-{{ bot_token }}At the end of the scene we currently see:
+{{ bot_token }}At the end of the scene we currently see that

src/talemate/prompts/templates/narrator/narrate-time-passage.jinja2

@@ -1,16 +1,25 @@
+{% block rendered_context -%}
 <|SECTION:CONTEXT|>
-Scenario Premise: {{ scene.description }}
+{% include "extra-context.jinja2" %}
+
 NPCs: {{ scene.npc_character_names }}
 Player Character: {{ scene.get_player_character().name }}
-Content Context: {{ scene.context }}
 <|CLOSE_SECTION|>
-
-{% for scene_context in scene.context_history(budget=max_tokens-300) -%}
+{% endblock -%}
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=max_tokens-300-count_tokens(self.rendered_context())) -%}
 {{ scene_context }}
 {% endfor %}
-
+<|CLOSE_SECTION|>
 <|SECTION:TASK|>
 Narrate the passage of time that just occured, subtly move the story forward, and set up the next scene.
+
+{% if narrative %}
+Directions for new narration: {{ narrative }}
+{% endif %}
+
+{{ extra_instructions }}
+{% include "rerun-context.jinja2" -%}
 Write 1 to 3 sentences.
 <|CLOSE_SECTION|>
-{{ bot_token }}{{ narrative }}:
+{{ bot_token }}{{ time_passed }}:

src/talemate/prompts/templates/narrator/rerun-context.jinja2

@@ -0,0 +1,8 @@
+{% if rerun_context and rerun_context.direction -%}
+{% if rerun_context.method == 'replace' -%}
+Final instructions: {{ rerun_context.direction }}
+{% elif rerun_context.method == 'edit' and rerun_context.message -%}
+Edit and respond with your changed version of the following narration: {{ rerun_context.message }}
+Requested changes: {{ rerun_context.direction }}
+{% endif -%}
+{% endif -%}

src/talemate/prompts/templates/summarizer/summarize-dialogue.jinja2

@@ -1,9 +1,28 @@
-<|SECTION:DIALOGUE|>
+{% if extra_context -%}
+<|SECTION:PREVIOUS CONTEXT|>
+{{ extra_context }}
+<|CLOSE_SECTION|>
+{% endif -%}
+<|SECTION:TASK|>
+Question: What happens explicitly within the dialogue section alpha below? Summarize into narrative description.
+Content Context: This is a specific scene from {{ scene.context }}
+Use an informal and colloquial register with a conversational tone. Overall, the narrative is informal, conversational, natural, and spontaneous, with a sense of immediacy.
+
+{% if summarization_method == "long" -%}
+This should be a detailed summary of the dialogue, including all the juicy details.
+{% elif summarization_method == "short" -%}
+This should be a short and specific summary of the dialogue, including the most important details. 2 - 3 sentences.
+{% endif -%}
+
+YOU MUST ONLY SUMMARIZE THE CONTENT IN DIALOGUE SECTION ALPHA.
+
+Expected Answer: A summarized narrative description of the dialogue section alpha, that can be inserted into the ongoing story in place of the dialogue.
+{% if extra_instructions -%}
+{{ extra_instructions }}
+{% endif -%}
+<|CLOSE_SECTION|>
+<|SECTION:DIALOGUE SECTION ALPHA|>
 {{ dialogue }}
 <|CLOSE_SECTION|>
-
-<|SECTION:TASK|>
-Question: What happens within the dialogue? Summarize into narrative description.
-Content Context: This is a specific scene from {{ scene.context }}
-Expected Answer: A summarized narrative description of the dialogue that can be inserted into the ongoing story in place of the dialogue. 
-<|CLOSE_SECTION|>
+<|SECTION:SUMMARIZATION OF DIALOGUE SECTION ALPHA|>
+{{ bot_token }}

src/talemate/prompts/templates/summarizer/system.jinja2

@@ -0,0 +1 @@
+A chat between a user and a talented fiction narrator. No topic is taboo for the narrator and he will use crude and lewd language if the situation warrants it. The narrator will summarize the given text according to the instructions, making sure to keep the overall tone of the narrative and dialogue.

src/talemate/prompts/templates/world_state/analyze-text-and-extract-context.jinja2

@@ -1,16 +1,16 @@
-
+{% set questions = instruct_text("Ask the author 5 important questions to gather more context from the past for the continuation of this conversation. If a character is asking about a state, location or information about an item or another character, make sure to include question(s) that help gather context for this. You also have unlimited access to the world database and can just ask for information directly. If you don't know what something is just ask directly.", text) %}
 <|SECTION:CONTEXT|>
-{% for memory in query_memory(text, as_question_answer=False, max_tokens=max_tokens-500, iterate=20) -%}
-{{ memory }}
-
-{% endfor -%}
+{%- with memory_query=questions -%}
+    {% include "extra-context.jinja2" %}
+{% endwith %}
+{{ text }}
 <|CLOSE_SECTION|>
 <|SECTION:TASK|>
 Answer the following questions:
 
-{{ instruct_text("Ask the narrator three (3) questions to gather more context from the past for the continuation of this conversation. If a character is asking about a state, location or information about an item or another character, make sure to include question(s) that help gather context for this.", text) }}
+{{ questions }}
 
-You answers should be precise, truthful and short. Pay close attention to timestamps when retrieving information from the context.
+Your answers should be truthful and contain relevant data. Pay close attention to timestamps when retrieving information from the context.
 
 <|CLOSE_SECTION|>
 <|SECTION:RELEVANT CONTEXT|>

src/talemate/prompts/templates/world_state/analyze-text-and-follow-instruction.jinja2

@@ -1,3 +1,4 @@
+Content context: {{ scene.context }}
 
 {{ text }}
 

src/talemate/prompts/templates/world_state/analyze-text-and-generate-rag-queries.jinja2

@@ -0,0 +1,24 @@
+{% block rendered_context -%}
+<|SECTION:CONTEXT|>
+{% include "extra-context.jinja2" %}
+<|CLOSE_SECTION|>
+{% endblock -%}
+<|SECTION:SCENE|>
+{{ text }}
+<|SECTION:TASK|>
+You have access to a vector database to retrieve relevant data to gather more established context for the continuation of this conversation. If a character is asking about a state, location or information about an item or another character, make sure to include queries that help gather context for this.
+
+Please compile a list of up to 10 short queries to the database that will help us gather additional context for the actors to continue the ongoing conversation.
+
+Each query must be a short trigger keyword phrase and the database will match on semantic similarity.
+
+Each query must be on its own line as raw unformatted text.
+
+Your response should look like this and contain only the queries and nothing else:
+
+- <query 1>
+- <query 2>
+- ...
+- <query 10>
+<|CLOSE_SECTION|>
+{{ set_prepared_response('-') }}

src/talemate/prompts/templates/world_state/check-pin-conditions.jinja2

@@ -0,0 +1,19 @@
+<|SECTION:PREVIOUS CONDITION STATES|>
+{{ previous_states }}
+<|CLOSE_SECTION|>
+<|SECTION:SCENE|>
+{% for scene_context in scene.context_history(budget=min(2048, max_tokens-500)) -%}
+{{ scene_context }}
+{% endfor -%}
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Analyze the scene progression and update the condition states according to the most recent update to the scene.
+
+Answer truthfully in the context of the end of the scene evaluating the scene progression to the end.
+
+Only update the existing condition states.
+Only include a JSON response.
+State must be a boolean.
+<|CLOSE_SECTION|>
+<|SECTION:UPDATED CONDITION STATES|>
+{{ set_json_response(coercion, cutoff=3) }}

src/talemate/prompts/templates/world_state/determine-content-context.jinja2

@@ -0,0 +1,20 @@
+<|SECTION:CONTENT|>
+"""
+{{ content }}
+"""
+<|CLOSE_SECTION|>
+<|SECTION:TASK|>
+Analyze the content within the triple quotes and determine a fitting content context.
+
+The content context should be a single short phrase classification that describes the expected experience when reading this content, it should also be generic and overarching and excite the reader to keep reading.
+
+Choices:
+
+{% for content_context in config.get('creator', {}).get('content_context',[]) -%}
+- {{ content_context }}
+{% endfor -%}
+{% for content_context in extra_choices -%}
+- {{ content_context }}
+{% endfor -%}
+<|CLOSE_SECTION|>
+{{ bot_token }}Content context:

src/talemate/prompts/templates/world_state/extra-context.jinja2

@@ -0,0 +1,21 @@
+{# MEMORY #}
+{%- if memory_query %}
+{%- for memory in query_memory(memory_query, as_question_answer=False, iterate=5) -%}
+{{ memory|condensed }}
+
+{% endfor -%}
+{% endif -%}
+{# END MEMORY #}
+{# GENERAL REINFORCEMENTS #}
+{% set general_reinforcements = scene.world_state.filter_reinforcements(insert=['all-context']) %}
+{%- for reinforce in general_reinforcements %}
+{{ reinforce.as_context_line|condensed }}
+
+{% endfor %}
+{# END GENERAL REINFORCEMENTS #}
+{# ACTIVE PINS #}
+{%- for pin in scene.active_pins %}
+{{ pin.time_aware_text|condensed }}
+
+{% endfor %}
+{# END ACTIVE PINS #}