diff --git a/apps/server/package.json b/apps/server/package.json
index 14ce7e5a..48ce3604 100644
--- a/apps/server/package.json
+++ b/apps/server/package.json
@@ -19,6 +19,7 @@
   },
   "description": "",
   "devDependencies": {
+    "@types/ms": "^2.1.0",
     "@types/node": "^24.2.0",
     "@types/nodemailer": "^6.4.17",
     "@types/pg": "^8.15.5",
@@ -28,22 +29,20 @@
     "tsx": "^4.20.3"
   },
   "dependencies": {
-    "@ai-sdk/google": "^1.2.22",
-    "@ai-sdk/openai": "^1.3.22",
+    "@ai-sdk/google": "^2.0.6",
+    "@ai-sdk/openai": "^2.0.15",
     "@aws-sdk/client-s3": "^3.863.0",
     "@colanode/core": "*",
     "@colanode/crdt": "*",
     "@fastify/cors": "^11.0.1",
     "@fastify/websocket": "^11.1.0",
-    "@mastra/core": "latest",
-    "@mastra/memory": "latest",
-    "@mastra/pg": "^0.13.1",
-    "@mastra/rag": "^1.0.6",
     "@node-rs/argon2": "^2.0.2",
-    "ai": "^4.3.19",
+    "@opentelemetry/auto-instrumentations-node": "^0.62.1",
+    "@opentelemetry/sdk-node": "^0.203.0",
     "@redis/client": "^5.8.0",
     "@tus/s3-store": "^2.0.0",
     "@tus/server": "^2.3.0",
+    "ai": "^5.0.15",
     "bullmq": "^5.56.9",
     "diff": "^8.0.2",
     "dotenv": "^17.2.1",
@@ -53,8 +52,7 @@
     "js-sha256": "^0.11.0",
     "ky": "^1.8.2",
     "kysely": "^0.28.4",
-    "langchain": "^0.3.30",
-    "langfuse-langchain": "^3.38.4",
+    "langfuse-vercel": "^3.38.4",
     "ms": "^2.1.3",
     "nodemailer": "^7.0.5",
     "pg": "^8.16.3",
diff --git a/apps/server/src/index.ts b/apps/server/src/index.ts
index a830e91c..ded744b2 100644
--- a/apps/server/src/index.ts
+++ b/apps/server/src/index.ts
@@ -6,6 +6,8 @@ import { initRedis } from '@colanode/server/data/redis';
 import { eventBus } from '@colanode/server/lib/event-bus';
 import { emailService } from '@colanode/server/services/email-service';
 import { jobService } from '@colanode/server/services/job-service';
+import { initObservability } from '@colanode/server/lib/observability/otel';
+import { initAssistantTrigger } from '@colanode/server/services/assistant-trigger';
 
 dotenv.config({
   quiet: true,
@@ -22,6 +24,11 @@ const init = async () => {
 
   await eventBus.init();
   await emailService.init();
+
+  // Subscribe after event bus init and job queue ready
+  initAssistantTrigger();
+
+  initObservability();
 };
 
 init();
diff --git a/apps/server/src/jobs/assistant-response.ts b/apps/server/src/jobs/assistant-response.ts
index f9c5275a..2cb1d6ba 100644
--- a/apps/server/src/jobs/assistant-response.ts
+++ b/apps/server/src/jobs/assistant-response.ts
@@ -5,12 +5,10 @@ import {
   getNodeModel,
   MessageAttributes,
 } from '@colanode/core';
-import { Mastra } from '@mastra/core';
-import { RuntimeContext } from '@mastra/core/runtime-context';
 import { database } from '@colanode/server/data/database';
 import { SelectNode } from '@colanode/server/data/schema';
 import { JobHandler } from '@colanode/server/jobs';
-import { assistantWorkflow } from '@colanode/server/lib/ai/ai-workflow';
+import { runAssistantWorkflow } from '@colanode/server/lib/ai/ai-workflow';
 import {
   AssistantWorkflowInput,
   AssistantWorkflowOutput,
@@ -91,10 +89,10 @@ export const assistantRespondHandler: JobHandler<
     console.log(`🚀 Processing AI assistant request for message: ${messageId}`);
     const startTime = Date.now();
 
-    // Prepare request for the AI service
     const assistantRequest: AssistantWorkflowInput = {
       userInput: messageText,
       workspaceId,
+      workspaceName: workspace.name || workspaceId,
       userId: user.id,
       userDetails: {
         name: user.name || 'User',
@@ -105,54 +103,19 @@ export const assistantRespondHandler: JobHandler<
       selectedContextNodeIds,
     };
 
-    // Prepare runtime context
-    const runtimeContext = new RuntimeContext();
-    runtimeContext.set('workspaceName', workspace.name || workspaceId);
-    runtimeContext.set('userName', user.name || 'User');
-    runtimeContext.set('userEmail', user.email || '');
-    runtimeContext.set('workspaceId', workspaceId);
-    runtimeContext.set('userId', user.id);
-    runtimeContext.set('selectedContextNodeIds', selectedContextNodeIds || []);
-    runtimeContext.set('userInput', messageText);
-
-    // Initialize Mastra and get the workflow
-    const mastra = new Mastra({
-      workflows: {
-        assistantWorkflow,
-      },
-    });
-    const workflow = mastra.getWorkflow('assistantWorkflow');
-    const run = await workflow.createRunAsync();
-
-    // Execute the workflow
-    const result = await run.start({
-      inputData: assistantRequest,
-      runtimeContext,
-    });
-
-    if (result.status !== 'success' || !result.result) {
-      const errorMessage =
-        result.status === 'suspended'
-          ? 'Workflow was suspended unexpectedly'
-          : (result as any).error || 'Workflow execution failed';
-      console.error('❌ Workflow failed:', errorMessage);
-      throw new Error(errorMessage);
-    }
-
-    const assistantResult: AssistantWorkflowOutput = {
-      ...result.result,
-      processingTimeMs: Date.now() - startTime,
-    };
+    const result: AssistantWorkflowOutput =
+      await runAssistantWorkflow(assistantRequest);
+    result.processingTimeMs = Date.now() - startTime;
 
     console.log(
-      `✅ AI response generated (${assistantResult.processingTimeMs}ms): ${
-        assistantResult.searchPerformed ? 'with search' : 'no search'
+      `✅ AI response generated (${result.processingTimeMs}ms): ${
+        result.searchPerformed ? 'with search' : 'no search'
       }`
     );
 
     await createAndPublishResponse(
-      assistantResult.finalAnswer,
-      assistantResult.citations,
+      result.finalAnswer,
+      result.citations,
       message,
       workspaceId
     );
diff --git a/apps/server/src/jobs/document-embed-scan.ts b/apps/server/src/jobs/document-embed-scan.ts
index 3c76811d..7c078587 100644
--- a/apps/server/src/jobs/document-embed-scan.ts
+++ b/apps/server/src/jobs/document-embed-scan.ts
@@ -25,6 +25,7 @@ declare module '@colanode/server/jobs' {
 export const documentEmbedScanHandler: JobHandler<
   DocumentEmbedScanInput
 > = async () => {
+  console.log('document embed scan job');
   if (!config.ai.enabled) {
     return;
   }
@@ -55,7 +56,7 @@ export const documentEmbedScanHandler: JobHandler<
           .where('document_id', '=', document.id)
           .execute();
 
-        return;
+        continue;
       }
 
       const firstEmbedding = await database
diff --git a/apps/server/src/jobs/document-embed.ts b/apps/server/src/jobs/document-embed.ts
index f89b0af4..0477d9a3 100644
--- a/apps/server/src/jobs/document-embed.ts
+++ b/apps/server/src/jobs/document-embed.ts
@@ -1,4 +1,3 @@
-import { createOpenAI } from '@ai-sdk/openai';
 import { embedMany } from 'ai';
 import { sql } from 'kysely';
 
@@ -7,6 +6,7 @@ import { database } from '@colanode/server/data/database';
 import { CreateDocumentEmbedding } from '@colanode/server/data/schema';
 import { JobHandler } from '@colanode/server/jobs';
 import { chunkText } from '@colanode/server/lib/ai/chunking';
+import { getEmbeddingModel } from '@colanode/server/lib/ai/ai-models';
 import { config } from '@colanode/server/lib/config';
 import { fetchNode } from '@colanode/server/lib/nodes';
 
@@ -26,10 +26,13 @@ declare module '@colanode/server/jobs' {
 export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
   input
 ) => {
+  try {
   if (!config.ai.enabled) {
     return;
   }
 
+  console.log('document embed job', input);
+
   const { documentId } = input;
   const document = await database
     .selectFrom('documents')
@@ -37,17 +40,21 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
     .where('id', '=', documentId)
     .executeTakeFirst();
 
+  console.log('document', document?.id);
   if (!document) {
+    console.log('document not found');
     return;
   }
 
   const node = await fetchNode(documentId);
   if (!node) {
+    console.log('node not found');
     return;
   }
 
   const nodeModel = getNodeModel(node.type);
   if (!nodeModel?.documentSchema) {
+    console.log('node model not found');
     return;
   }
 
@@ -58,28 +65,36 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
       .where('document_id', '=', documentId)
       .execute();
 
+    console.log('no text');
     return;
   }
 
-  const openaiClient = createOpenAI({ apiKey: config.ai.embedding.apiKey });
-  const embeddingModel = openaiClient.embedding(config.ai.embedding.modelName);
+  console.log('sending request to openai');
 
+  console.log('config.ai.embedding.apiKey', config.ai.embedding.apiKey);
+
+  const embeddingModel = getEmbeddingModel();
+
+  console.log('getting existing embeddings');
   const existingEmbeddings = await database
     .selectFrom('document_embeddings')
     .select(['chunk', 'revision', 'text', 'summary'])
     .where('document_id', '=', documentId)
     .execute();
 
+  console.log('existing embeddings', existingEmbeddings.length);
+
   const revision =
     existingEmbeddings.length > 0 ? existingEmbeddings[0]!.revision : 0n;
 
   if (revision >= document.revision) {
+    console.log('revision is up to date');
     return;
   }
 
   const textChunks = await chunkText(
     text,
-    existingEmbeddings.map((e) => ({
+    existingEmbeddings.map((e: { text: string; summary: string | null }) => ({
       text: e.text,
       summary: e.summary ?? undefined,
     })),
@@ -87,14 +102,20 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
   );
 
   const embeddingsToUpsert: CreateDocumentEmbedding[] = [];
+  console.log('textChunks', textChunks.length);
   for (let i = 0; i < textChunks.length; i++) {
+    console.log('chunk', i);
     const chunk = textChunks[i];
     if (!chunk) {
+      console.log('chunk is undefined');
       continue;
     }
 
-    const existing = existingEmbeddings.find((e) => e.chunk === i);
+    const existing = existingEmbeddings.find(
+      (e: { chunk: number; text: string }) => e.chunk === i
+    );
     if (existing && existing.text === chunk.text) {
+      console.log('chunk already exists');
       continue;
     }
 
@@ -110,6 +131,8 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
     });
   }
 
+  console.log('embeddingsToUpsert', embeddingsToUpsert.length);
+
   const batchSize = config.ai.embedding.batchSize;
   for (let i = 0; i < embeddingsToUpsert.length; i += batchSize) {
     const batch = embeddingsToUpsert.slice(i, i + batchSize);
@@ -117,10 +140,18 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
       item.summary ? `${item.summary}\n\n${item.text}` : item.text
     );
 
+    console.log('calling embedMany');
     const { embeddings: embeddingVectors } = await embedMany({
       model: embeddingModel,
       values: textsToEmbed,
+      providerOptions: {
+        openai: {
+          dimensions: config.ai.embedding.dimensions,
+        },
+      },
     });
+
+    console.log('embedding vectors', embeddingVectors.length);
     for (let j = 0; j < batch.length; j++) {
       const vector = embeddingVectors[j];
       const batchItem = batch[j];
@@ -130,33 +161,43 @@ export const documentEmbedHandler: JobHandler<DocumentEmbedInput> = async (
     }
   }
 
-  if (embeddingsToUpsert.length === 0) {
+  // Filter out entries with empty vectors
+  const ready = embeddingsToUpsert.filter((e) => e.embedding_vector.length > 0);
+  if (ready.length === 0) {
+    console.log('no embeddings to upsert');
     return;
   }
 
-  await database
-    .insertInto('document_embeddings')
-    .values(
-      embeddingsToUpsert.map((embedding) => ({
-        document_id: embedding.document_id,
-        chunk: embedding.chunk,
-        revision: embedding.revision,
-        workspace_id: embedding.workspace_id,
-        text: embedding.text,
-        summary: embedding.summary,
-        embedding_vector: sql.raw(
-          `'[${embedding.embedding_vector.join(',')}]'::vector`
-        ),
-        created_at: embedding.created_at,
-      }))
-    )
-    .onConflict((oc) =>
-      oc.columns(['document_id', 'chunk']).doUpdateSet({
-        text: sql.ref('excluded.text'),
-        summary: sql.ref('excluded.summary'),
-        embedding_vector: sql.ref('excluded.embedding_vector'),
-        updated_at: new Date(),
-      })
-    )
-    .execute();
+  console.log('upserting embeddings');
+    await database
+      .insertInto('document_embeddings')
+      .values(
+        ready.map((embedding) => ({
+          document_id: embedding.document_id,
+          chunk: embedding.chunk,
+          revision: embedding.revision,
+          workspace_id: embedding.workspace_id,
+          text: embedding.text,
+          summary: embedding.summary,
+          embedding_vector: sql.raw(
+            `'[${embedding.embedding_vector.join(',')}]'::vector`
+          ),
+          created_at: embedding.created_at,
+        }))
+      )
+      .onConflict((oc: any) =>
+        oc.columns(['document_id', 'chunk']).doUpdateSet({
+          text: sql.ref('excluded.text'),
+          summary: sql.ref('excluded.summary'),
+          embedding_vector: sql.ref('excluded.embedding_vector'),
+          updated_at: new Date(),
+        })
+      )
+      .execute();
+  } catch (error) {
+    console.log('error upserting embeddings', error);
+    throw error;
+  } finally {
+    console.log('clearing document embedding schedule');
+  }
 };
diff --git a/apps/server/src/jobs/node-embed-scan.ts b/apps/server/src/jobs/node-embed-scan.ts
index 5187c310..caa8df62 100644
--- a/apps/server/src/jobs/node-embed-scan.ts
+++ b/apps/server/src/jobs/node-embed-scan.ts
@@ -25,6 +25,7 @@ declare module '@colanode/server/jobs' {
 export const nodeEmbedScanHandler: JobHandler<
   NodeEmbedScanInput
 > = async () => {
+  console.log('node embed scan job');
   if (!config.ai.enabled) {
     return;
   }
diff --git a/apps/server/src/jobs/node-embed.ts b/apps/server/src/jobs/node-embed.ts
index b1b9f720..c23831ac 100644
--- a/apps/server/src/jobs/node-embed.ts
+++ b/apps/server/src/jobs/node-embed.ts
@@ -1,4 +1,3 @@
-import { createOpenAI } from '@ai-sdk/openai';
 import { embedMany } from 'ai';
 import { sql } from 'kysely';
 
@@ -7,6 +6,7 @@ import { database } from '@colanode/server/data/database';
 import { CreateNodeEmbedding } from '@colanode/server/data/schema';
 import { JobHandler } from '@colanode/server/jobs';
 import { chunkText } from '@colanode/server/lib/ai/chunking';
+import { getEmbeddingModel } from '@colanode/server/lib/ai/ai-models';
 import { config } from '@colanode/server/lib/config';
 import { fetchNode } from '@colanode/server/lib/nodes';
 
@@ -58,8 +58,7 @@ export const nodeEmbedHandler: JobHandler<NodeEmbedInput> = async (input) => {
     return;
   }
 
-  const openaiClient = createOpenAI({ apiKey: config.ai.embedding.apiKey });
-  const embeddingModel = openaiClient.embedding(config.ai.embedding.modelName);
+  const embeddingModel = getEmbeddingModel();
 
   const existingEmbeddings = await database
     .selectFrom('node_embeddings')
@@ -79,7 +78,7 @@ export const nodeEmbedHandler: JobHandler<NodeEmbedInput> = async (input) => {
 
   const textChunks = await chunkText(
     fullText,
-    existingEmbeddings.map((e) => ({
+    existingEmbeddings.map((e: { text: string; summary: string | null }) => ({
       text: e.text,
       summary: e.summary ?? undefined,
     })),
@@ -93,7 +92,9 @@ export const nodeEmbedHandler: JobHandler<NodeEmbedInput> = async (input) => {
       continue;
     }
 
-    const existing = existingEmbeddings.find((e) => e.chunk === i);
+    const existing = existingEmbeddings.find(
+      (e: { chunk: number; text: string }) => e.chunk === i
+    );
     if (existing && existing.text === chunk.text) {
       continue;
     }
@@ -124,6 +125,11 @@ export const nodeEmbedHandler: JobHandler<NodeEmbedInput> = async (input) => {
     const { embeddings: embeddingVectors } = await embedMany({
       model: embeddingModel,
       values: textsToEmbed,
+      providerOptions: {
+        openai: {
+          dimensions: config.ai.embedding.dimensions,
+        },
+      },
     });
     for (let j = 0; j < batch.length; j++) {
       const vector = embeddingVectors[j];
@@ -134,10 +140,16 @@ export const nodeEmbedHandler: JobHandler<NodeEmbedInput> = async (input) => {
     }
   }
 
+  // Filter out entries with empty vectors
+  const ready = embeddingsToUpsert.filter((e) => e.embedding_vector.length > 0);
+  if (ready.length === 0) {
+    return;
+  }
+
   await database
     .insertInto('node_embeddings')
     .values(
-      embeddingsToUpsert.map((embedding) => ({
+      ready.map((embedding) => ({
         node_id: embedding.node_id,
         chunk: embedding.chunk,
         revision: embedding.revision,
diff --git a/apps/server/src/lib/ai/ai-agents.ts b/apps/server/src/lib/ai/ai-agents.ts
deleted file mode 100644
index cc47e010..00000000
--- a/apps/server/src/lib/ai/ai-agents.ts
+++ /dev/null
@@ -1,167 +0,0 @@
-import { Agent } from '@mastra/core/agent';
-import { ModelConfig } from './ai-models';
-
-export const createIntentAgent = () => {
-  return new Agent({
-    name: 'Intent Classifier',
-    description:
-      'Classifies whether queries need workspace data or general knowledge',
-    instructions: ({ runtimeContext }) => {
-      const workspaceName = runtimeContext?.get('workspaceName') || 'workspace';
-      const userName = runtimeContext?.get('userName') || 'user';
-
-      return `You classify user queries. Your only job is to determine if the query needs workspace data.
-
-WORKSPACE: ${workspaceName}
-USER: ${userName}
-
-CLASSIFICATION RULES:
-- "no_context" = General knowledge, explanations, how-to questions
-- "retrieve" = Workspace content, documents, people, or data
-
-EXAMPLES:
-"What is JavaScript?" → no_context
-"How do I code?" → no_context
-"Explain databases" → no_context
-"Find my documents" → retrieve
-"Show recent files" → retrieve
-"What did John write?" → retrieve
-
-Respond with just the classification and confidence (0-1).`;
-    },
-    model: () => ModelConfig.forIntentRecognition(),
-  });
-};
-
-export const createQueryAgent = () => {
-  return new Agent({
-    name: 'Query Optimizer',
-    description: 'Rewrites queries for better search performance',
-    instructions: () => {
-      return `You optimize search queries. Your only job is to rewrite queries for better search results.
-
-TASK: Take a user query and create two optimized versions:
-1. SEMANTIC QUERY: Natural language for vector search
-2. KEYWORD QUERY: Key terms for text search
-
-RULES:
-- Remove filler words (the, a, an, is, are, etc.)
-- Focus on core concepts and entities
-- Keep important context words
-- Make queries concise but complete
-
-EXAMPLES:
-Input: "Show me documents about the marketing campaign John worked on last month"
-Semantic: "marketing campaign documents John authored recent"
-Keyword: "marketing campaign John documents month"
-
-Input: "Find all completed projects from Q3"
-Semantic: "completed projects third quarter finished"
-Keyword: "completed projects Q3 done finished"
-
-Respond with just the two optimized queries.`;
-    },
-    model: () => ModelConfig.forIntentRecognition(),
-  });
-};
-
-export const createAnswerAgent = () => {
-  return new Agent({
-    name: 'Answer Generator',
-    description:
-      'Generates helpful responses using context or general knowledge',
-    instructions: ({ runtimeContext }) => {
-      const workspaceName = runtimeContext?.get('workspaceName') || 'workspace';
-      const userName = runtimeContext?.get('userName') || 'user';
-
-      return `You are a helpful assistant for the ${workspaceName} workspace.
-
-USER: ${userName}
-
-YOUR JOB:
-- Answer questions clearly and helpfully
-- Use provided context when available
-- Cite sources when using context: "According to [Source 1]..."
-- If no context, use general knowledge
-- Be professional but conversational
-
-CONTEXT RULES:
-- WITH context: Use it as primary source, cite sources
-- WITHOUT context: Use general knowledge, don't mention workspace
-
-RESPONSE STYLE:
-- Clear and direct
-- Professional but friendly
-- Actionable when possible
-- Acknowledge limitations honestly
-
-Keep responses focused and helpful.`;
-    },
-    model: () => ModelConfig.forAssistant(),
-  });
-};
-
-export const createRerankAgent = () => {
-  return new Agent({
-    name: 'Relevance Scorer',
-    description: 'Scores search results for relevance to user query',
-    instructions: () => {
-      return `You score search results for relevance. Your only job is to rate how well each result matches the user's query.
-
-TASK: Score each result from 0.0 to 1.0 based on relevance.
-
-SCORING GUIDE:
-- 1.0 = Perfect match, exactly what user needs
-- 0.8 = Very relevant, mostly matches query
-- 0.6 = Somewhat relevant, partially matches
-- 0.4 = Minimally relevant, tangentially related
-- 0.2 = Low relevance, barely related
-- 0.0 = Not relevant, unrelated to query
-
-CONSIDER:
-- Topic match (does content match the query topic?)
-- Specificity (does it answer the specific question?)
-- Completeness (does it provide useful information?)
-- Recency (newer content often more relevant)
-
-Return just the scores for each item.`;
-    },
-    model: () => ModelConfig.forReranking(),
-  });
-};
-
-export const createChunkEnrichmentAgent = () => {
-  return new Agent({
-    name: 'Chunk Enrichment',
-    description:
-      'Creates contextual summaries for text chunks to enhance search retrieval',
-    instructions: () => {
-      return `You create concise summaries of text chunks to improve search retrieval. Your only job is to summarize the given chunk within its document context.
-
-TASK: Generate a brief summary (30-50 words) of the chunk that captures its key points and role in the larger document.
-
-GUIDELINES:
-- Focus on the main ideas and key information in the chunk
-- Consider how the chunk fits into the complete document
-- Identify the chunk's purpose or role (introduction, data, conclusion, etc.)
-- Use descriptive, neutral language
-- Make the summary useful for search and retrieval
-- Different content types need different approaches:
-  * "message": Focus on communication content and context
-  * "page": Identify document structure and main topics
-  * "record": Describe the type of data and key fields
-  * Other types: Adapt summary to content purpose
-
-OUTPUT: Provide only the summary with no additional text or explanations.`;
-    },
-    model: () => ModelConfig.forIntentRecognition(),
-  });
-};
-
-export const AgentConfig = {
-  INTENT_AGENT: 'intent-classifier',
-  QUERY_AGENT: 'query-optimizer',
-  ANSWER_AGENT: 'answer-generator',
-  RERANK_AGENT: 'relevance-scorer',
-  CHUNK_ENRICHMENT_AGENT: 'chunk-enrichment',
-} as const;
diff --git a/apps/server/src/lib/ai/ai-demo.ts b/apps/server/src/lib/ai/ai-demo.ts
deleted file mode 100644
index 87d2dc6b..00000000
--- a/apps/server/src/lib/ai/ai-demo.ts
+++ /dev/null
@@ -1,460 +0,0 @@
-/**
- * AI Assistant Demo and Examples
- *
- * This file demonstrates how to use the new AI workflow-based assistant system
- * and provides examples for different use cases.
- */
-
-import { Mastra } from '@mastra/core';
-import { RuntimeContext } from '@mastra/core/runtime-context';
-import { assistantWorkflow } from './ai-workflow';
-import {
-  AssistantWorkflowInput,
-  AssistantWorkflowOutput,
-} from '@colanode/server/types/ai';
-
-/**
- * Process an AI request using the Mastra workflow directly.
- *
- * @param request - The user's request with context
- * @returns Promise resolving to the assistant's response
- */
-async function processAIRequest(
-  request: AssistantWorkflowInput
-): Promise<AssistantWorkflowOutput> {
-  const startTime = Date.now();
-
-  // Prepare runtime context
-  const runtimeContext = new RuntimeContext();
-  runtimeContext.set('workspaceName', request.workspaceId);
-  runtimeContext.set('userName', request.userDetails.name);
-  runtimeContext.set('userEmail', request.userDetails.email);
-  runtimeContext.set('workspaceId', request.workspaceId);
-  runtimeContext.set('userId', request.userId);
-  runtimeContext.set(
-    'selectedContextNodeIds',
-    request.selectedContextNodeIds || []
-  );
-  runtimeContext.set('userInput', request.userInput);
-
-  // Initialize Mastra and get the workflow
-  const mastra = new Mastra({
-    workflows: {
-      assistantWorkflow,
-    },
-  });
-  const workflow = mastra.getWorkflow('assistantWorkflow');
-  const run = await workflow.createRunAsync();
-
-  // Execute the workflow
-  const result = await run.start({
-    inputData: request,
-    runtimeContext,
-  });
-
-  if (result.status !== 'success' || !result.result) {
-    const errorMessage =
-      result.status === 'suspended'
-        ? 'Workflow was suspended unexpectedly'
-        : (result as any).error || 'Workflow execution failed';
-    console.error('❌ Workflow failed:', errorMessage);
-    throw new Error(errorMessage);
-  }
-
-  return {
-    ...result.result,
-    processingTimeMs: Date.now() - startTime,
-  };
-}
-
-/**
- * Demo: Basic AI Assistant Usage
- *
- * Shows how to use the AI service for simple requests
- */
-export async function demoBasicUsage() {
-  console.log('\n🤖 === Basic AI Assistant Usage Demo ===');
-
-  try {
-    const response = await processAIRequest({
-      userInput: 'What are the latest documents about project management?',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'John Doe',
-        email: 'john@example.com',
-      },
-    });
-
-    console.log(
-      '📝 User Query:',
-      'What are the latest documents about project management?'
-    );
-    console.log('🤖 AI Response:', response.finalAnswer);
-    console.log('🔍 Search Performed:', response.searchPerformed);
-    console.log('⏱️ Processing Time:', `${response.processingTimeMs}ms`);
-    console.log('📚 Citations:', response.citations.length);
-  } catch (error) {
-    console.error('❌ Demo failed:', error);
-  }
-}
-
-/**
- * Demo: Complex Database Query Processing
- *
- * Shows how the service handles complex database queries
- */
-export async function demoComplexDatabaseQuery() {
-  console.log('\n🗄️ === Complex Database Query Demo ===');
-
-  try {
-    const response = await processAIRequest({
-      userInput:
-        'Can you help me understand the Q3 sales data from our database?',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'Jane Smith',
-        email: 'jane@example.com',
-      },
-    });
-
-    console.log(
-      '📝 User Query:',
-      'Can you help me understand the Q3 sales data from our database?'
-    );
-    console.log('🤖 AI Response:', response.finalAnswer);
-    console.log('🔍 Search Performed:', response.searchPerformed);
-    console.log('⏱️ Processing Time:', `${response.processingTimeMs}ms`);
-    console.log('📚 Citations Found:', response.citations.length);
-  } catch (error) {
-    console.error('❌ Complex query demo failed:', error);
-  }
-}
-
-/**
- * Demo: Workflow System Usage
- *
- * Shows how the new workflow system handles different types of queries
- */
-export async function demoWorkflowUsage() {
-  console.log('\n🔄 === Workflow System Demo ===');
-
-  try {
-    // Test general knowledge query (no_context intent)
-    console.log('\n📚 Testing general knowledge query...');
-    const generalResponse = await processAIRequest({
-      userInput: 'What is TypeScript and why is it useful?',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'John Doe',
-        email: 'john@example.com',
-      },
-    });
-
-    console.log('📝 Query: What is TypeScript and why is it useful?');
-    console.log(
-      '🤖 Response:',
-      generalResponse.finalAnswer.substring(0, 200) + '...'
-    );
-    console.log('🔍 Search Performed:', generalResponse.searchPerformed);
-    console.log('📚 Citations:', generalResponse.citations.length);
-
-    // Test workspace-specific query (retrieve intent)
-    console.log('\n🔍 Testing workspace-specific query...');
-    const workspaceResponse = await processAIRequest({
-      userInput: 'Show me recent documents about project planning',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'Jane Smith',
-        email: 'jane@example.com',
-      },
-    });
-
-    console.log('📝 Query: Show me recent documents about project planning');
-    console.log(
-      '🤖 Response:',
-      workspaceResponse.finalAnswer.substring(0, 200) + '...'
-    );
-    console.log('🔍 Search Performed:', workspaceResponse.searchPerformed);
-    console.log('📚 Citations:', workspaceResponse.citations.length);
-  } catch (error) {
-    console.error('❌ Workflow demo failed:', error);
-  }
-}
-
-/**
- * Demo: Error Handling
- *
- * Shows how the system handles various error scenarios
- */
-export async function demoErrorHandling() {
-  console.log('\n⚠️ === Error Handling Demo ===');
-
-  // Test with invalid workspace ID
-  try {
-    console.log('Testing with invalid workspace ID...');
-    const response = await processAIRequest({
-      userInput: 'Test query',
-      workspaceId: 'invalid_workspace',
-      userId: 'test_user',
-      userDetails: {
-        name: 'Test User',
-        email: 'test@example.com',
-      },
-    });
-
-    console.log('🤖 Response (graceful error):', response.finalAnswer);
-    console.log('⏱️ Processing Time:', `${response.processingTimeMs}ms`);
-  } catch (error) {
-    console.log(
-      '❌ Handled error gracefully:',
-      error instanceof Error ? error.message : 'Unknown error'
-    );
-  }
-
-  // Test with empty input
-  try {
-    console.log('Testing with empty input...');
-    const response = await processAIRequest({
-      userInput: '',
-      workspaceId: 'demo_workspace_123',
-      userId: 'test_user',
-      userDetails: {
-        name: 'Test User',
-        email: 'test@example.com',
-      },
-    });
-
-    console.log('🤖 Response (empty input):', response.finalAnswer);
-  } catch (error) {
-    console.log(
-      '❌ Handled empty input gracefully:',
-      error instanceof Error ? error.message : 'Unknown error'
-    );
-  }
-}
-
-/**
- * Demo: Performance Testing
- *
- * Shows performance characteristics with different query types
- */
-export async function demoPerformance() {
-  console.log('\n⚡ === Performance Testing Demo ===');
-
-  const queries = [
-    { type: 'General Knowledge', query: 'What is TypeScript?' },
-    { type: 'Simple Workspace', query: 'Show me recent documents' },
-    {
-      type: 'Complex Database',
-      query: 'Find all projects where status is completed and priority is high',
-    },
-  ];
-
-  for (const { type, query } of queries) {
-    try {
-      console.log(`\n🔬 Testing ${type} query...`);
-      console.log(`📝 Query: "${query}"`);
-
-      const startTime = Date.now();
-      const response = await processAIRequest({
-        userInput: query,
-        workspaceId: 'demo_workspace_123',
-        userId: 'perf_test_user',
-        userDetails: {
-          name: 'Performance Tester',
-          email: 'perf@example.com',
-        },
-      });
-      const totalTime = Date.now() - startTime;
-
-      console.log('🔍 Search Performed:', response.searchPerformed);
-      console.log('⏱️ Service Time:', `${response.processingTimeMs}ms`);
-      console.log('⏱️ Total Time:', `${totalTime}ms`);
-      console.log(
-        '📏 Response Length:',
-        `${response.finalAnswer.length} chars`
-      );
-    } catch (error) {
-      console.error(`❌ Performance test failed for ${type}:`, error);
-    }
-  }
-}
-
-/**
- * Demo: New Workflow Architecture
- *
- * Shows the new declarative workflow with proper branching
- */
-export async function demoNewWorkflowArchitecture() {
-  console.log('\n🏗️ === New Workflow Architecture Demo ===');
-
-  try {
-    // Test 1: No-context branch (general knowledge)
-    console.log('\n1️⃣ Testing NO_CONTEXT branch (general knowledge)...');
-    const generalResponse = await processAIRequest({
-      userInput: 'What is TypeScript and why should I use it?',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'Alice Developer',
-        email: 'alice@example.com',
-      },
-    });
-
-    console.log('📝 Query: "What is TypeScript and why should I use it?"');
-    console.log('🎯 Expected Branch: no_context');
-    console.log(
-      '🔍 Search Performed:',
-      generalResponse.searchPerformed ? '❌ Unexpected' : '✅ None (correct)'
-    );
-    console.log(
-      '📚 Citations:',
-      generalResponse.citations.length === 0
-        ? '✅ None (correct)'
-        : '❌ Unexpected'
-    );
-    console.log(
-      '💬 Response Preview:',
-      generalResponse.finalAnswer.substring(0, 150) + '...'
-    );
-
-    // Test 2: Retrieve branch (workspace-specific)
-    console.log('\n2️⃣ Testing RETRIEVE branch (workspace-specific)...');
-    const workspaceResponse = await processAIRequest({
-      userInput: 'Show me recent documents about project planning',
-      workspaceId: 'demo_workspace_123',
-      userId: 'demo_user_456',
-      userDetails: {
-        name: 'Bob Manager',
-        email: 'bob@example.com',
-      },
-    });
-
-    console.log('📝 Query: "Show me recent documents about project planning"');
-    console.log('🎯 Expected Branch: retrieve');
-    console.log(
-      '🔍 Search Performed:',
-      workspaceResponse.searchPerformed
-        ? '✅ Yes (correct)'
-        : '❌ None (unexpected)'
-    );
-    console.log(
-      '📚 Citations:',
-      workspaceResponse.citations.length > 0
-        ? '✅ Present (good)'
-        : '⚠️ None (no results)'
-    );
-    console.log(
-      '💬 Response Preview:',
-      workspaceResponse.finalAnswer.substring(0, 150) + '...'
-    );
-
-    console.log('\n✅ New workflow architecture demo completed successfully!');
-  } catch (error) {
-    console.error('❌ New workflow architecture demo failed:', error);
-  }
-}
-
-/**
- * Run all demos including new architecture demonstrations
- *
- * Executes all demo functions to show the complete system capabilities
- */
-export async function runAllDemos() {
-  console.log('🎬 === AI Assistant System Demo Suite ===');
-  console.log('This demo showcases the new Mastra-based AI assistant system\n');
-
-  try {
-    // Show the migration comparison first
-    showMigrationComparison();
-
-    // Demo new architecture
-    await demoNewWorkflowArchitecture();
-
-    // Run original demos for compatibility
-    await demoBasicUsage();
-    await demoWorkflowUsage();
-    await demoErrorHandling();
-    await demoPerformance();
-
-    console.log('\n🎉 === All Demos Completed Successfully ===');
-    console.log('The new Mastra-based AI system is ready for production! 🚀');
-  } catch (error) {
-    console.error('❌ Demo suite failed:', error);
-  }
-}
-
-/**
- * Migration comparison showing before vs after
- */
-export function showMigrationComparison() {
-  console.log(`
-🔄 === MASTRA MIGRATION COMPLETED ===
-
-📊 BEFORE (Complex LangChain System):
-┌─────────────────────────────────────────────────────────┐
-│ ❌ 600+ lines across multiple complex files             │
-│ ❌ Manual LangGraph workflow with 14+ imperative nodes  │
-│ ❌ Complex state management between workflow steps      │
-│ ❌ Monolithic agents with multi-purpose prompts         │
-│ ❌ Tightly coupled search and reranking logic           │
-│ ❌ Poor observability and debugging capabilities        │
-│ ❌ Not optimized for smaller/self-hosted LLMs           │
-│ ❌ Difficult to test, maintain, and extend              │
-└─────────────────────────────────────────────────────────┘
-
-📊 AFTER (Declarative Mastra Workflow System):
-┌─────────────────────────────────────────────────────────┐
-│ ✅ Fully declarative workflow with proper branching     │
-│ ✅ Single-purpose agents optimized for smaller LLMs     │
-│ ✅ Granular tools with focused responsibilities         │
-│ ✅ Intelligent intent-based routing                     │
-│ ✅ Full observability through Mastra's workflow engine  │
-│ ✅ Type-safe, maintainable, and easily extensible      │
-└─────────────────────────────────────────────────────────┘
-
-🎯 KEY ACHIEVEMENTS:
-• TRUE Mastra idiomatic implementation with .branch() routing
-• BYOM (Bring Your Own Model) optimization for self-hosted LLMs
-• Granular tools: semantic search, keyword search, database tools
-• Step-by-step observability for debugging and optimization
-
-🏗️ ARCHITECTURE HIGHLIGHTS:
-• Declarative workflow: intentClassification → branch(intent) → publish
-• Simplified agents: intentClassifier, queryOptimizer, answerGenerator
-
-📁 REFACTORED FILE STRUCTURE:
-├── ai-workflow.ts    → Declarative Mastra workflow with branching
-├── ai-agents.ts      → Single-purpose agents optimized for small LLMs
-├── ai-tools.ts       → Granular tools (semantic, keyword, database)
-├── ai-models.ts      → Multi-provider model configuration
-└── ai-demo.ts        → Workflow path demonstrations
-`);
-}
-
-// Run demos if this file is executed directly
-if (require.main === module) {
-  async function main() {
-    try {
-      showMigrationComparison();
-
-      console.log(
-        '\n⚠️  Note: These demos require valid workspace and user IDs to run properly.'
-      );
-      console.log(
-        'To run actual demos, update the IDs in the demo functions and uncomment the line below.\n'
-      );
-
-      // Uncomment to run actual demonstrations (requires valid workspace/user IDs)
-      // await runAllDemos();
-    } catch (error) {
-      console.error('Demo error:', error);
-    }
-  }
-
-  main();
-}
diff --git a/apps/server/src/lib/ai/ai-models.ts b/apps/server/src/lib/ai/ai-models.ts
index 3687a5cc..96c7fb5d 100644
--- a/apps/server/src/lib/ai/ai-models.ts
+++ b/apps/server/src/lib/ai/ai-models.ts
@@ -9,7 +9,9 @@ export type AITask =
   | 'response' // Main assistant responses
   | 'intentRecognition' // Intent classification
   | 'rerank' // Document reranking
-  | 'databaseFilter'; // Database filtering
+  | 'databaseFilter' // Database filtering
+  | 'queryRewrite' // Query rewriting
+  | 'contextEnhancer'; // Context enhancement for chunks
 
 export const getModelForTask = (task: AITask) => {
   if (!config.ai.enabled) {
@@ -75,9 +77,31 @@ export const getAvailableProviders = (): AIProvider[] => {
   ) as AIProvider[];
 };
 
+export const getEmbeddingModel = () => {
+  if (!config.ai.enabled) {
+    throw new Error('AI is disabled in configuration.');
+  }
+
+  const { provider, modelName, apiKey } = config.ai.embedding;
+
+  if (provider === 'openai') {
+    process.env.OPENAI_API_KEY = apiKey;
+    return openai.textEmbeddingModel(modelName);
+  }
+
+  if (provider === 'google') {
+    process.env.GOOGLE_GENERATIVE_AI_API_KEY = apiKey;
+    return google.textEmbedding(modelName);
+  }
+
+  throw new Error(`Unsupported embedding provider: ${provider}`);
+};
+
 export const ModelConfig = {
   forAssistant: () => getModelForTask('response'),
   forIntentRecognition: () => getModelForTask('intentRecognition'),
   forReranking: () => getModelForTask('rerank'),
   forDatabaseFilter: () => getModelForTask('databaseFilter'),
+  forQueryRewrite: () => getModelForTask('queryRewrite'),
+  forContextEnhancer: () => getModelForTask('contextEnhancer'),
 } as const;
diff --git a/apps/server/src/lib/ai/ai-tools.ts b/apps/server/src/lib/ai/ai-tools.ts
deleted file mode 100644
index 44f00343..00000000
--- a/apps/server/src/lib/ai/ai-tools.ts
+++ /dev/null
@@ -1,343 +0,0 @@
-import { createTool } from '@mastra/core/tools';
-import { z } from 'zod';
-
-export const createHybridSearchTool = () =>
-  createTool({
-    id: 'hybrid-search',
-    description:
-      'Perform hybrid (semantic + keyword) search on workspace documents and nodes',
-    inputSchema: z.object({
-      semanticQuery: z.string().describe('The semantic search query'),
-      keywordQuery: z.string().describe('The keyword search query'),
-      workspaceId: z.string().describe('The workspace ID to search in'),
-      userId: z.string().describe('The user ID for access control'),
-      maxResults: z
-        .number()
-        .default(10)
-        .describe('Maximum number of results to return'),
-      selectedContextNodeIds: z
-        .array(z.string())
-        .optional()
-        .describe('Specific node IDs to search within'),
-    }),
-    outputSchema: z.object({
-      results: z.array(
-        z.object({
-          content: z
-            .string()
-            .describe('The content of the found document/node'),
-          type: z
-            .string()
-            .describe('The type of content (document, node, etc.)'),
-          sourceId: z.string().describe('Unique identifier for the source'),
-          score: z.number().describe('Hybrid relevance score (0-1 scaled)'),
-          metadata: z
-            .record(z.any())
-            .describe('Additional metadata about the source'),
-        })
-      ),
-      totalFound: z.number().describe('Total number of results returned'),
-      searchType: z.literal('hybrid').describe('Hybrid search'),
-    }),
-    execute: async ({ context }) => {
-      const { retrieveNodes } = await import(
-        '@colanode/server/lib/ai/node-retrievals'
-      );
-      const { retrieveDocuments } = await import(
-        '@colanode/server/lib/ai/document-retrievals'
-      );
-
-      const {
-        semanticQuery,
-        keywordQuery,
-        workspaceId,
-        userId,
-        maxResults = 10,
-        selectedContextNodeIds = [],
-      } = context;
-
-      try {
-        console.log(
-          `🔎 Hybrid search for: semantic="${semanticQuery}" keyword="${keywordQuery}" (max: ${maxResults})`
-        );
-
-        // Perform hybrid retrieval per source type
-        const [nodeResults, documentResults] = await Promise.all([
-          retrieveNodes(
-            {
-              semanticQuery,
-              keywordQuery,
-              originalQuery: semanticQuery || keywordQuery,
-              intent: 'retrieve',
-            },
-            workspaceId,
-            userId,
-            maxResults,
-            selectedContextNodeIds
-          ),
-          retrieveDocuments(
-            {
-              semanticQuery,
-              keywordQuery,
-              originalQuery: semanticQuery || keywordQuery,
-              intent: 'retrieve',
-            },
-            workspaceId,
-            userId,
-            maxResults,
-            selectedContextNodeIds
-          ),
-        ]);
-
-        const allResults = [...nodeResults, ...documentResults].slice(
-          0,
-          maxResults
-        );
-
-        // Convert to standardized format (loosen typing for MDocument)
-        const results = allResults.map((doc) => {
-          const md = doc as any;
-          return {
-            content: md.pageContent || md.text || '',
-            type: md.metadata?.type || 'document',
-            sourceId: md.metadata?.id || md.id || '',
-            score: md.metadata?.score || md.score || 0,
-            metadata: md.metadata || {},
-          };
-        });
-
-        console.log(`✅ Hybrid search returned ${results.length} results`);
-
-        return {
-          results,
-          totalFound: results.length,
-          searchType: 'hybrid' as const,
-        };
-      } catch (error) {
-        console.error('❌ Hybrid search error:', error);
-        throw error;
-      }
-    },
-  });
-
-export const createDatabaseSchemaInspectionTool = () =>
-  createTool({
-    id: 'database-schema-inspection',
-    description: 'Get database schemas and structure for a workspace',
-    inputSchema: z.object({
-      workspaceId: z.string().describe('The workspace ID'),
-      userId: z.string().describe('The user ID for access control'),
-    }),
-    outputSchema: z.object({
-      databases: z.array(
-        z.object({
-          id: z.string(),
-          name: z.string(),
-          fields: z.record(
-            z.object({
-              type: z.string(),
-              name: z.string(),
-            })
-          ),
-          sampleRecords: z
-            .array(z.any())
-            .describe('Sample records for context'),
-        })
-      ),
-      totalDatabases: z.number(),
-    }),
-    execute: async ({ context }) => {
-      const { retrieveByFilters } = await import(
-        '@colanode/server/lib/records'
-      );
-      const { database } = await import('@colanode/server/data/database');
-
-      const { workspaceId, userId } = context;
-
-      try {
-        console.log(
-          `🗄️ Inspecting database schemas for workspace: ${workspaceId}`
-        );
-
-        // Get available databases for the user
-        const databases = await database
-          .selectFrom('nodes as n')
-          .innerJoin('collaborations as c', 'c.node_id', 'n.root_id')
-          .where('n.type', '=', 'database')
-          .where('n.workspace_id', '=', workspaceId)
-          .where('c.collaborator_id', '=', userId)
-          .where('c.deleted_at', 'is', null)
-          .selectAll()
-          .execute();
-
-        if (databases.length === 0) {
-          console.log('📭 No accessible databases found');
-          return {
-            databases: [],
-            totalDatabases: 0,
-          };
-        }
-
-        console.log(`🔍 Found ${databases.length} accessible databases`);
-
-        // Get database schemas and sample data
-        const databaseSchemas = await Promise.all(
-          databases.map(async (db: any) => {
-            const sampleRecords = await retrieveByFilters(
-              db.id,
-              workspaceId,
-              userId,
-              { filters: [], sorts: [], page: 1, count: 3 } // Just 3 samples
-            );
-
-            const dbAttrs = db.attributes as any;
-            const fields = dbAttrs.fields || {};
-            const formattedFields = Object.entries(fields).reduce(
-              (acc: any, [id, field]: [string, any]) => ({
-                ...acc,
-                [id]: {
-                  type: field.type,
-                  name: field.name,
-                },
-              }),
-              {}
-            );
-
-            return {
-              id: db.id,
-              name: dbAttrs.name || 'Untitled Database',
-              fields: formattedFields,
-              sampleRecords,
-            };
-          })
-        );
-
-        console.log(
-          `✅ Retrieved schemas for ${databaseSchemas.length} databases`
-        );
-
-        return {
-          databases: databaseSchemas,
-          totalDatabases: databaseSchemas.length,
-        };
-      } catch (error) {
-        console.error('❌ Database schema inspection error:', error);
-        throw error;
-      }
-    },
-  });
-
-export const createDatabaseQueryTool = () =>
-  createTool({
-    id: 'database-query',
-    description: 'Execute structured queries against workspace databases',
-    inputSchema: z.object({
-      databaseId: z.string().describe('The database ID to query'),
-      workspaceId: z.string().describe('The workspace ID'),
-      userId: z.string().describe('The user ID for access control'),
-      filters: z.array(z.any()).describe('Structured filter conditions'),
-      maxResults: z.number().default(10).describe('Maximum number of results'),
-    }),
-    outputSchema: z.object({
-      results: z.array(
-        z.object({
-          content: z
-            .string()
-            .describe('Formatted content of the database record'),
-          metadata: z.object({
-            id: z.string(),
-            type: z.literal('record'),
-            databaseId: z.string(),
-            databaseName: z.string(),
-            createdAt: z.date(),
-            createdBy: z.string(),
-          }),
-        })
-      ),
-      totalFound: z.number(),
-      databaseName: z.string(),
-    }),
-    execute: async ({ context }) => {
-      const { retrieveByFilters } = await import(
-        '@colanode/server/lib/records'
-      );
-      const { fetchNode } = await import('@colanode/server/lib/nodes');
-
-      const {
-        databaseId,
-        workspaceId,
-        userId,
-        filters,
-        maxResults = 10,
-      } = context;
-
-      try {
-        console.log(
-          `🗄️ Querying database ${databaseId} with ${filters.length} filters`
-        );
-
-        // Get the database node for metadata
-        const dbNode = await fetchNode(databaseId);
-        if (!dbNode || dbNode.type !== 'database') {
-          console.log('❌ Database not found or not accessible');
-          return {
-            results: [],
-            totalFound: 0,
-            databaseName: 'Unknown Database',
-          };
-        }
-
-        const databaseName =
-          (dbNode.attributes as any).name || 'Untitled Database';
-
-        // Execute the query
-        const records = await retrieveByFilters(
-          databaseId,
-          workspaceId,
-          userId,
-          { filters, sorts: [], page: 1, count: maxResults }
-        );
-
-        // Format results
-        const results = records.map((record: any) => {
-          const fields = Object.entries((record.attributes as any).fields || {})
-            .map(([key, value]) => `${key}: ${value}`)
-            .join('\n');
-
-          const content = `Database Record from ${databaseName}:\n${fields}`;
-
-          return {
-            content,
-            metadata: {
-              id: record.id,
-              type: 'record' as const,
-              databaseId,
-              databaseName,
-              createdAt: record.created_at,
-              createdBy: record.created_by,
-            },
-          };
-        });
-
-        console.log(
-          `✅ Database query returned ${results.length} records from ${databaseName}`
-        );
-
-        return {
-          results,
-          totalFound: results.length,
-          databaseName,
-        };
-      } catch (error) {
-        console.error('❌ Database query error:', error);
-        throw error;
-      }
-    },
-  });
-
-export const createAITools = () => ({
-  hybridSearch: createHybridSearchTool(),
-
-  databaseSchemaInspection: createDatabaseSchemaInspectionTool(),
-  databaseQuery: createDatabaseQueryTool(),
-});
diff --git a/apps/server/src/lib/ai/ai-workflow.ts b/apps/server/src/lib/ai/ai-workflow.ts
index c443db9c..497a18f1 100644
--- a/apps/server/src/lib/ai/ai-workflow.ts
+++ b/apps/server/src/lib/ai/ai-workflow.ts
@@ -1,500 +1,319 @@
-import { createWorkflow, createStep } from '@mastra/core/workflows';
+import { generateObject, generateText } from 'ai';
 import { z } from 'zod';
+import { ModelConfig } from './ai-models';
+import { retrieveNodes } from '@colanode/server/lib/ai/node-retrievals';
+import { retrieveDocuments } from '@colanode/server/lib/ai/document-retrievals';
 import {
-  createIntentAgent,
-  createAnswerAgent,
-  createQueryAgent,
-  createRerankAgent,
-} from './ai-agents';
-import { createAITools } from './ai-tools';
-import {
-  assistantWorkflowInputSchema,
-  assistantWorkflowOutputSchema,
-  intentClassificationOutputSchema,
-  queryRewriteOutputSchema,
-  searchResultsOutputSchema,
-  rankedResultsOutputSchema,
-  answerOutputSchema,
+  AssistantWorkflowInput,
+  AssistantWorkflowOutput,
+  HybridSearchArgs,
 } from '@colanode/server/types/ai';
 
-const intentClassificationStep = createStep({
-  id: 'intent-classification-step',
-  description:
-    'Classify user intent: general knowledge vs workspace-specific query',
-  inputSchema: assistantWorkflowInputSchema,
-  outputSchema: intentClassificationOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    const intentAgent = createIntentAgent();
+export async function runAssistantWorkflow(
+  input: AssistantWorkflowInput
+): Promise<AssistantWorkflowOutput> {
+  const intent = await classifyIntent(input.userInput);
 
-    const prompt = `You are an intent classifier. Your only job is to determine if this query needs workspace data.
+  if (intent.intent === 'retrieve') {
+    const rewrites = await rewriteQuery(input.userInput);
 
-Query: "${inputData.userInput}"
-
-Classify as:
-- "no_context": General knowledge, explanations, calculations, definitions
-- "retrieve": Workspace-specific content, documents, people, or data
-
-Examples:
-- "What is TypeScript?" → no_context
-- "How do I write clean code?" → no_context  
-- "Show me recent documents" → retrieve
-- "Find projects by John" → retrieve
-
-Respond with just the classification and your confidence (0-1).`;
-
-    try {
-      const response = await intentAgent.generate(
-        [{ role: 'user', content: prompt }],
-        {
-          runtimeContext,
-          output: z.object({
-            intent: z.enum(['no_context', 'retrieve']),
-            confidence: z.number().min(0).max(1),
-            reasoning: z.string().optional(),
-          }),
-        }
-      );
-
-      console.log(
-        `🎯 Intent: ${response.object.intent} (confidence: ${response.object.confidence})`
-      );
-
-      return {
-        intent: response.object.intent,
-        confidence: response.object.confidence,
-        reasoning: response.object.reasoning,
-        originalInput: inputData.userInput,
-      };
-    } catch (error) {
-      throw error;
-    }
-  },
-});
-
-const queryRewriteStep = createStep({
-  id: 'query-rewrite-step',
-  description: 'Optimize user query for semantic and keyword search',
-  inputSchema: intentClassificationOutputSchema,
-  outputSchema: queryRewriteOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    const queryAgent = createQueryAgent();
-
-    const prompt = `Original Query: "${inputData.originalInput}"
-
-Create two optimized versions for search.`;
-
-    try {
-      const response = await queryAgent.generate(
-        [{ role: 'user', content: prompt }],
-        {
-          runtimeContext,
-          output: z.object({
-            semanticQuery: z.string(),
-            keywordQuery: z.string(),
-          }),
-        }
-      );
-
-      console.log(`🔍 Semantic query: "${response.object.semanticQuery}"`);
-      console.log(`🔑 Keyword query: "${response.object.keywordQuery}"`);
-
-      return {
-        semanticQuery: response.object.semanticQuery,
-        keywordQuery: response.object.keywordQuery,
-        originalQuery: inputData.originalInput,
-        intent: inputData.intent,
-      };
-    } catch (error) {
-      throw error;
-    }
-  },
-});
-
-const runSearchesStep = createStep({
-  id: 'run-searches-step',
-  description: 'Execute hybrid (semantic + keyword) search via single tool',
-  inputSchema: queryRewriteOutputSchema,
-  outputSchema: searchResultsOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    const workspaceId = runtimeContext?.get('workspaceId') as string;
-    const userId = runtimeContext?.get('userId') as string;
-    const selectedContextNodeIds =
-      (runtimeContext?.get('selectedContextNodeIds') as string[]) || [];
-
-    if (!workspaceId || !userId) {
-      console.error('❌ Missing required runtime context for search');
-      throw new Error('Missing required runtime context for search');
-    }
-
-    console.log(`🔍 Running hybrid search...`);
-    console.log(`   Semantic: "${inputData.semanticQuery}"`);
-    console.log(`   Keyword: "${inputData.keywordQuery}"`);
-
-    try {
-      const tools = createAITools();
-
-      const hybridResults = await tools.hybridSearch.execute({
-        context: {
-          semanticQuery: inputData.semanticQuery,
-          keywordQuery: inputData.keywordQuery,
-          workspaceId,
-          userId,
-          maxResults: 20,
-          selectedContextNodeIds,
-        },
-        runtimeContext,
-      });
-
-      console.log(
-        `📊 Hybrid search completed: ${hybridResults.results.length} results`
-      );
-
-      return {
-        results: hybridResults.results,
-        searchType: 'hybrid' as const,
-        totalFound: hybridResults.totalFound,
-      };
-    } catch (error) {
-      console.error('❌ Search execution failed:', error);
-      throw error;
-    }
-  },
-});
-
-const combineResultsStep = createStep({
-  id: 'combine-results-step',
-  description: 'Combine and score search results using RRF algorithm',
-  inputSchema: searchResultsOutputSchema,
-  outputSchema: searchResultsOutputSchema, // Same schema for now
-  execute: async ({ inputData }) => {
-    if (inputData.results.length === 0) {
-      console.log('📭 No results to combine');
-      return inputData; // Pass through if no results
-    }
-
-    console.log(`🔄 Combining ${inputData.results.length} search results`);
-
-    // Simple combination: remove duplicates and apply recency boost
-    const uniqueResults = new Map();
-
-    inputData.results.forEach((result, index) => {
-      const key = result.sourceId;
-
-      if (!uniqueResults.has(key)) {
-        // Apply position-based scoring (earlier results get higher scores)
-        const positionScore = 1 / (index + 1);
-        const recencyBoost = result.metadata.createdAt
-          ? Math.max(
-              0,
-              1 -
-                (Date.now() - new Date(result.metadata.createdAt).getTime()) /
-                  (1000 * 60 * 60 * 24 * 30)
-            ) // 30 days
-          : 0;
-
-        const combinedScore =
-          result.score * 0.7 + positionScore * 0.2 + recencyBoost * 0.1;
-
-        uniqueResults.set(key, {
-          ...result,
-          score: Math.min(1, combinedScore), // Cap at 1.0
-        });
-      }
+    const { results } = await hybridSearch({
+      semanticQuery: rewrites.semanticQuery,
+      keywordQuery: rewrites.keywordQuery,
+      workspaceId: input.workspaceId,
+      userId: input.userId,
+      maxResults: 20,
+      selectedContextNodeIds: input.selectedContextNodeIds,
     });
 
-    // Sort by combined score
-    const combinedResults = Array.from(uniqueResults.values())
-      .sort((a, b) => b.score - a.score)
-      .slice(0, 20); // Top 20 results
+    const combined = combineResults(results);
 
-    console.log(`✅ Combined to ${combinedResults.length} unique results`);
-
-    return {
-      results: combinedResults,
-      searchType: inputData.searchType,
-      totalFound: combinedResults.length,
-    };
-  },
-});
-
-const rerankStep = createStep({
-  id: 'rerank-step',
-  description: 'Rerank search results for relevance using LLM',
-  inputSchema: searchResultsOutputSchema,
-  outputSchema: rankedResultsOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    if (inputData.results.length === 0) {
-      console.log('📭 No results to rerank');
+    if (combined.length === 0) {
       return {
-        rankedResults: [],
+        finalAnswer:
+          "I couldn't find relevant workspace context for that. Try rephrasing or selecting different context.",
         citations: [],
-        searchPerformed: false,
-      };
-    }
-
-    const originalQuery =
-      (runtimeContext?.get('userInput') as string) || 'query';
-    console.log(
-      `🎯 Reranking ${inputData.results.length} results for query: "${originalQuery}"`
-    );
-
-    try {
-      const rerankAgent = createRerankAgent();
-
-      // Format results for reranking
-      const resultsText = inputData.results
-        .map(
-          (result, index) => `[${index}] ${result.content.substring(0, 300)}`
-        )
-        .join('\n\n');
-
-      const prompt = `Query: "${originalQuery}"
-
-Results to score (0.0 to 1.0 for relevance):
-
-${resultsText}
-
-Score each result based on how well it answers the query.`;
-
-      const response = await rerankAgent.generate(
-        [{ role: 'user', content: prompt }],
-        {
-          runtimeContext,
-          output: z.object({
-            scores: z.array(z.number().min(0).max(1)),
-          }),
-        }
-      );
-
-      // Apply scores and sort by relevance
-      const rankedResults = inputData.results
-        .map((result, index) => ({
-          content: result.content,
-          sourceId: result.sourceId,
-          relevanceScore: response.object.scores[index] || 0.5,
-          type: result.type,
-          metadata: result.metadata,
-        }))
-        .sort((a, b) => b.relevanceScore - a.relevanceScore)
-        .slice(0, 20); // Top 20 results
-
-      // Generate citations from top results
-      const citations = rankedResults
-        .slice(0, 10) // Top 10 for citations
-        .map((result) => ({
-          sourceId: result.sourceId,
-          quote:
-            result.content.substring(0, 200).trim() +
-            (result.content.length > 200 ? '...' : ''),
-        }));
-
-      console.log(
-        `✅ Reranked to ${rankedResults.length} results with ${citations.length} citations`
-      );
-
-      return {
-        rankedResults,
-        citations,
         searchPerformed: true,
       };
-    } catch (error) {
-      throw error;
-    }
-  },
-});
-
-const answerWithContextStep = createStep({
-  id: 'answer-with-context-step',
-  description: 'Generate response using retrieved context and citations',
-  inputSchema: rankedResultsOutputSchema,
-  outputSchema: answerOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    const answerAgent = createAnswerAgent();
-    const userQuery =
-      (runtimeContext?.get('userInput') as string) || 'User query';
-
-    if (inputData.rankedResults.length === 0) {
-      throw new Error('No context available for answering the query');
     }
 
-    // Format context for the LLM
-    const contextText = inputData.rankedResults
-      .slice(0, 10) // Top 10 results
-      .map((item, index) => `[Source ${index + 1}] ${item.content}`)
-      .join('\n\n');
+    const ranked = await rerankLLM(input.userInput, combined);
 
-    console.log(
-      `📚 Generating response with ${inputData.rankedResults.length} context items`
+    const { answer, citations } = await answerWithContext(
+      input.userInput,
+      ranked,
+      {
+        workspaceName: input.workspaceName,
+        userName: input.userDetails.name,
+      }
     );
 
-    const prompt = `Answer the user's question using the provided workspace context.
+    return { finalAnswer: answer, citations, searchPerformed: true };
+  }
 
-**User Question:** ${userQuery}
+  const answer = await answerDirect(input.userInput, {
+    workspaceName: input.workspaceName,
+    userName: input.userDetails.name,
+  });
 
-**Workspace Context:**
-${contextText}
+  return { finalAnswer: answer, citations: [], searchPerformed: false };
+}
 
-**Instructions:**
-- Use the context as your primary information source
-- Cite sources using [Source N] format when referencing context
-- If the context doesn't fully answer the question, combine it with general knowledge but clearly distinguish between the two
-- Provide specific, actionable information when possible
-- Be conversational but professional`;
+async function classifyIntent(query: string) {
+  const schema = z.object({
+    intent: z.enum(['no_context', 'retrieve']),
+    confidence: z.number().min(0).max(1),
+    reasoning: z.string().optional(),
+  });
 
-    try {
-      const response = await answerAgent.generate(
-        [{ role: 'user', content: prompt }],
-        { runtimeContext }
-      );
+  const { object } = await generateObject({
+    model: ModelConfig.forIntentRecognition(),
+    schema,
+    prompt: `
+Decide if the user question requires searching workspace context.
 
-      console.log(
-        `✅ Generated contextual response (${response.text.length} characters)`
-      );
+Return JSON { "intent": "retrieve"|"no_context", "confidence": 0..1, "reasoning": "<one short sentence>" }.
 
+Guidelines:
+- "retrieve" if the answer likely depends on workspace content (notes, pages, records, files, chats) OR references specific people, dates, projects, or "this/that" items.
+- "no_context" for general knowledge, chit-chat, or requests that don't reference workspace data.
+
+Question: "${query}"
+`,
+    experimental_telemetry: {
+      isEnabled: true,
+      functionId: 'intent-classification',
+      mxetadata: { stage: 'intent', userQuery: query },
+    },
+  });
+
+  return object;
+}
+
+async function rewriteQuery(original: string) {
+  const schema = z.object({
+    semanticQuery: z.string(),
+    keywordQuery: z.string(),
+  });
+
+  const { object } = await generateObject({
+    model: ModelConfig.forQueryRewrite(),
+    schema,
+    prompt: `
+Rewrite the user's input into two queries:
+
+1) "semanticQuery": A short natural-language search query (5–20 tokens), with expanded entities, acronyms, and likely synonyms. Drop punctuation and noise.
+2) "keywordQuery": A Postgres websearch_to_tsquery string with:
+   - quoted phrases for exact matches
+   - important terms first
+   - optional synonyms after OR
+   - minus terms if the user excluded something
+   - keep it <= 15 tokens
+
+Example:
+Input: "oncall runbook for payments db; not the legacy doc"
+semanticQuery: "payments database oncall runbook escalation procedures current"
+keywordQuery: "\"payments database\" runbook escalation -legacy"
+
+Input: "${original}"
+Return JSON with both fields only.
+`,
+    experimental_telemetry: {
+      isEnabled: true,
+      functionId: 'query-rewrite',
+      metadata: { stage: 'rewrite', original },
+    },
+  });
+
+  return object;
+}
+
+async function hybridSearch(args: HybridSearchArgs) {
+  const {
+    semanticQuery,
+    keywordQuery,
+    workspaceId,
+    userId,
+    maxResults = 20,
+    selectedContextNodeIds = [],
+  } = args;
+
+  const [nodeResults, documentResults] = await Promise.all([
+    retrieveNodes(
+      {
+        semanticQuery,
+        keywordQuery,
+        originalQuery: semanticQuery || keywordQuery,
+        intent: 'retrieve',
+      },
+      workspaceId,
+      userId,
+      maxResults,
+      selectedContextNodeIds
+    ),
+    retrieveDocuments(
+      {
+        semanticQuery,
+        keywordQuery,
+        originalQuery: semanticQuery || keywordQuery,
+        intent: 'retrieve',
+      },
+      workspaceId,
+      userId,
+      maxResults,
+      selectedContextNodeIds
+    ),
+  ]);
+
+  const merged = [...nodeResults, ...documentResults]
+    .slice(0, maxResults)
+    .map((md: any) => {
+      const meta = Array.isArray(md.metadata) ? (md.metadata[0] ?? {}) : {};
       return {
-        finalAnswer: response.text,
-        additionalCitations: inputData.citations,
-        usedContext: true,
+        content: md.text || '',
+        type: meta.type ?? 'document',
+        sourceId: `${md.id}:${meta.chunkIndex ?? 0}`,
+        score: md.score ?? 0,
+        metadata: meta,
       };
-    } catch (error) {
-      console.error('❌ Contextual answer generation failed:', error);
-      throw error;
+    });
+
+  return {
+    results: merged,
+    totalFound: merged.length,
+    searchType: 'hybrid' as const,
+  };
+}
+
+function combineResults(results: any[]) {
+  const unique = new Map<string, any>();
+  results.forEach((r, i) => {
+    const key = r.sourceId as string;
+    if (!unique.has(key)) {
+      const positionScore = 1 / (i + 1);
+      // Remove recency boost here - it's already applied in combineAndScoreSearchResults
+      const combined = r.score * 0.8 + positionScore * 0.2;
+      unique.set(key, { ...r, score: Math.min(1, combined) });
     }
-  },
-});
+  });
+  return Array.from(unique.values())
+    .sort((a, b) => b.score - a.score)
+    .slice(0, 20);
+}
 
-const answerDirectStep = createStep({
-  id: 'answer-direct-step',
-  description: 'Generate direct response using general knowledge',
-  inputSchema: intentClassificationOutputSchema,
-  outputSchema: answerOutputSchema,
-  execute: async ({ inputData, runtimeContext }) => {
-    const answerAgent = createAnswerAgent();
+async function rerankLLM(query: string, items: any[]) {
+  const schema = z.object({ scores: z.array(z.number().min(0).max(1)) });
 
-    console.log('📝 Generating direct response for general knowledge query');
+  const preview = items
+    .map((it, i) => `[${i}] ${String(it.content).slice(0, 300)}`)
+    .join('\n\n');
 
-    const prompt = `Answer this general knowledge question with a comprehensive, helpful response.
+  const { object } = await generateObject({
+    model: ModelConfig.forReranking(),
+    schema,
+    prompt: `
+Query: "${query}"
+Score each result from 0.0–1.0 for relevance.
 
-**Question:** ${inputData.originalInput}
+Scoring rubric:
+- 50% Semantic match to the query intent.
+- 25% Specificity (concrete details, matches entities).
+- 15% Recency (newer is better).
+- 10% Diversity (penalize near-duplicates).
 
-**Instructions:**
-- Provide accurate, detailed information
-- Use examples and explanations where helpful
-- Be educational and thorough
-- Use clear, professional language
-- Don't reference any workspace-specific information`;
+You may use metadata if present: type, createdAt, author, chunkIndex.
+Return only JSON: { "scores": [number...] } in the same order as provided.
 
-    try {
-      const response = await answerAgent.generate(
-        [{ role: 'user', content: prompt }],
-        { runtimeContext }
-      );
+Results:
+${preview}
+`,
+    experimental_telemetry: {
+      isEnabled: true,
+      functionId: 'rerank',
+      metadata: { stage: 'rerank', itemCount: items.length },
+    },
+  });
 
-      console.log(
-        `✅ Generated direct response (${response.text.length} characters)`
-      );
+  return items
+    .map((it, i) => ({
+      content: it.content,
+      sourceId: it.sourceId,
+      relevanceScore: object.scores[i] ?? 0.5,
+      type: it.type,
+      metadata: it.metadata,
+    }))
+    .sort((a, b) => b.relevanceScore - a.relevanceScore)
+    .slice(0, 20);
+}
 
-      return {
-        finalAnswer: response.text,
-        additionalCitations: [],
-        usedContext: false,
-      };
-    } catch (error) {
-      console.error('❌ Direct answer generation failed:', error);
-      throw error;
-    }
-  },
-});
+async function answerWithContext(
+  userQuery: string,
+  ranked: any[],
+  { workspaceName, userName }: { workspaceName: string; userName: string }
+) {
+  const context = ranked
+    .slice(0, 10)
+    .map((r, i) => `[Source ${i + 1}] ${String(r.content)}`)
+    .join('\n\n');
 
-const formatContextOutputStep = createStep({
-  id: 'format-context-output-step',
-  description: 'Format the final assistant response output with context',
-  inputSchema: answerOutputSchema,
-  outputSchema: assistantWorkflowOutputSchema,
-  execute: async ({ inputData }) => {
-    console.log(`📋 Formatting context-based response output`);
+  const { text } = await generateText({
+    model: ModelConfig.forAssistant(),
+    system: `You are a precise, helpful assistant for the ${workspaceName} workspace. User: ${userName}. When you state a fact from context, add [Source N]. If unsure, say so briefly.`,
+    prompt: `
+User question: ${userQuery}
 
-    const response = {
-      finalAnswer: inputData.finalAnswer,
-      citations: inputData.additionalCitations || [],
-      searchPerformed: inputData.usedContext,
-    };
+Use only the context below. If the answer requires assumptions not supported by the context, say what is missing.
+Prefer bullet points for lists. Include dates and names when available.
 
-    console.log(`✅ Formatted response with context`);
-    console.log(
-      `📊 Search performed: ${response.searchPerformed ? 'Yes' : 'No'}`
-    );
-    console.log(
-      `💬 Response length: ${response.finalAnswer.length} characters`
-    );
+Context:
+${context}
+`,
+    experimental_telemetry: {
+      isEnabled: true,
+      functionId: 'answer-with-context',
+      metadata: {
+        stage: 'answer',
+        withContext: true,
+        contextItems: ranked.length,
+      },
+    },
+  });
 
-    return response;
-  },
-});
+  const used = new Set<number>();
+  for (const m of text.matchAll(/\[Source\s+(\d+)\]/gi)) used.add(Number(m[1]));
+  const citations = Array.from(used)
+    .slice(0, 10)
+    .map((i) => {
+      const r = ranked[i - 1];
+      return r
+        ? {
+            sourceId: r.sourceId,
+            quote:
+              String(r.content).slice(0, 200).trim() +
+              (String(r.content).length > 200 ? '...' : ''),
+          }
+        : null;
+    })
+    .filter(Boolean) as { sourceId: string; quote: string }[];
 
-const formatDirectOutputStep = createStep({
-  id: 'format-direct-output-step',
-  description: 'Format the final assistant direct response output',
-  inputSchema: answerOutputSchema,
-  outputSchema: assistantWorkflowOutputSchema,
-  execute: async ({ inputData }) => {
-    console.log(`📋 Formatting direct response output (no context used)`);
+  return { answer: text, citations };
+}
 
-    const response = {
-      finalAnswer: inputData.finalAnswer,
-      citations: inputData.additionalCitations || [],
-      searchPerformed: false,
-    };
+async function answerDirect(
+  userQuery: string,
+  { workspaceName, userName }: { workspaceName: string; userName: string }
+) {
+  const { text } = await generateText({
+    model: ModelConfig.forAssistant(),
+    system: `You are a precise, helpful assistant. Keep answers concise but complete. You are working in the ${workspaceName} workspace. User: ${userName}.`,
+    prompt: `Answer the question clearly. If you need extra context from the workspace, say what would help.
+Question: ${userQuery}`,
+    experimental_telemetry: {
+      isEnabled: true,
+      functionId: 'answer-direct',
+      metadata: { stage: 'answer', withContext: false },
+    },
+  });
 
-    console.log(`✅ Formatted direct response`);
-    console.log(
-      `💬 Response length: ${response.finalAnswer.length} characters`
-    );
-
-    return response;
-  },
-});
-
-export const assistantWorkflow = createWorkflow({
-  id: 'assistant-workflow',
-  description:
-    'Declarative AI assistant workflow optimized for smaller LLMs with proper branching',
-  inputSchema: assistantWorkflowInputSchema,
-  outputSchema: assistantWorkflowOutputSchema,
-})
-  // Step 1: Always classify intent first
-  .then(intentClassificationStep)
-
-  // Step 2: Branch based on intent classification
-  .branch([
-    // RETRIEVE BRANCH: RAG pipeline for workspace-specific queries
-    [
-      async ({ inputData }) => inputData.intent === 'retrieve',
-      createWorkflow({
-        id: 'retrieve-branch',
-        inputSchema: intentClassificationOutputSchema,
-        outputSchema: assistantWorkflowOutputSchema,
-      })
-        .then(queryRewriteStep) // Optimize query for search
-        .then(runSearchesStep) // Execute parallel searches
-        .then(combineResultsStep) // Combine results algorithmically
-        .then(rerankStep) // LLM-based reranking
-        .then(answerWithContextStep) // Generate answer with context
-        .then(formatContextOutputStep) // Format output
-        .commit(),
-    ],
-
-    // NO_CONTEXT BRANCH: Direct answer for general knowledge queries
-    [
-      async ({ inputData }) => inputData.intent === 'no_context',
-      createWorkflow({
-        id: 'no-context-branch',
-        inputSchema: intentClassificationOutputSchema,
-        outputSchema: assistantWorkflowOutputSchema,
-      })
-        .then(answerDirectStep) // Generate direct answer
-        .then(formatDirectOutputStep) // Format output
-        .commit(),
-    ],
-  ])
-  .commit();
+  return text;
+}
diff --git a/apps/server/src/lib/ai/chunking.ts b/apps/server/src/lib/ai/chunking.ts
index 928d4d77..64ae6133 100644
--- a/apps/server/src/lib/ai/chunking.ts
+++ b/apps/server/src/lib/ai/chunking.ts
@@ -1,8 +1,8 @@
-import { MDocument } from '@mastra/rag';
+import { generateText } from 'ai';
 import type { NodeType } from '@colanode/core';
 import { config } from '@colanode/server/lib/config';
 import { TextChunk } from '@colanode/server/types/chunking';
-import { createChunkEnrichmentAgent } from './ai-agents';
+import { ModelConfig } from './ai-models';
 
 export const chunkText = async (
   text: string,
@@ -15,20 +15,20 @@ export const chunkText = async (
 
   const chunkSize = config.ai.chunking.defaultChunkSize;
   const chunkOverlap = config.ai.chunking.defaultOverlap;
-
-  const doc = MDocument.fromText(text);
-  const docChunks = await doc.chunk({
-    strategy: 'recursive',
-    maxSize: chunkSize,
-    overlap: chunkOverlap,
+  const docChunks = await recursiveCharacterSplit(text, {
+    chunkSize,
+    chunkOverlap,
+    keepSeparator: true,
   });
 
-  const chunks = docChunks
-    .map((chunk) => ({ text: chunk.text }))
-    .filter((c) => c.text.trim().length > 5);
+  const chunks: TextChunk[] = docChunks
+    .map((c) => ({ text: c }))
+    .filter((c: TextChunk) => c.text.trim().length > 5);
+
+  console.log('chunks', chunks);
 
   if (!config.ai.chunking.enhanceWithContext) {
-    return chunks;
+    return chunks; // return plain chunks without summaries
   }
 
   const enrichedChunks: TextChunk[] = [];
@@ -56,10 +56,8 @@ const enrichChunk = async (
   nodeType: NodeType
 ): Promise<string | undefined> => {
   try {
-    const enrichmentAgent = createChunkEnrichmentAgent();
-
     const prompt = `<task>
-Generate a concise summary of the following text chunk that is part of a larger document. 
+Generate a concise summary of the following text chunk that is part of a larger document.
 This summary will be used to enhance vector search retrieval by providing additional context about this specific chunk.
 </task>
 
@@ -92,13 +90,170 @@ ${chunk}
 Provide only the summary with no additional commentary or explanations.
 </output_format>`;
 
-    const response = await enrichmentAgent.generate([
-      { role: 'user', content: prompt },
-    ]);
+    const { text } = await generateText({
+      model: ModelConfig.forContextEnhancer(),
+      prompt,
+      experimental_telemetry: {
+        isEnabled: true,
+        functionId: 'chunk-enrichment',
+        metadata: {
+          stage: 'chunk-enrichment',
+          nodeType,
+          chunkLength: chunk.length,
+        },
+      },
+    });
 
-    return response.text.trim();
+    return text.trim();
   } catch (error) {
     console.warn('Failed to enrich chunk:', error);
     return undefined;
   }
 };
+
+/**
+ * Recursive character splitter.
+ * - Ordered separators: ["\n\n", "\n", " ", ""]
+ * - keepSeparator: when true, boundaries are preserved on the next chunk
+ * - chunkOverlap is applied during merge
+ *
+ */
+export async function recursiveCharacterSplit(
+  text: string,
+  {
+    chunkSize,
+    chunkOverlap = 0,
+    keepSeparator = true,
+    separators = ['\n\n', '\n', ' ', ''],
+    lengthFn,
+  }: {
+    chunkSize: number;
+    chunkOverlap?: number;
+    keepSeparator?: boolean;
+    separators?: string[];
+    lengthFn?: (s: string) => number | Promise<number>;
+  }
+): Promise<string[]> {
+  if (chunkSize <= 0) throw new Error('chunkSize must be > 0');
+  if (chunkOverlap < 0) throw new Error('chunkOverlap must be >= 0');
+  if (chunkOverlap >= chunkSize) {
+    throw new Error('Cannot have chunkOverlap >= chunkSize');
+  }
+
+  const len = async (s: string) =>
+    (lengthFn ? await lengthFn(s) : s.length) as number;
+
+  const escapeRegExp = (lit: string) =>
+    lit.replace(/[\/\\^$*+?.()|[\]{}-]/g, '\\$&');
+
+  const splitOnSeparator = (t: string, sep: string, keep: boolean) => {
+    let splits: string[];
+    if (sep) {
+      if (keep) {
+        const re = new RegExp(`(?=${escapeRegExp(sep)})`, 'g');
+        splits = t.split(re);
+      } else {
+        splits = t.split(sep);
+      }
+    } else {
+      splits = t.split('');
+    }
+    return splits.filter((s) => s !== '');
+  };
+
+  const joinDocs = (docs: string[], sep: string): string | null => {
+    const joined = docs.join(sep).trim();
+    return joined === '' ? null : joined;
+  };
+
+  const mergeSplits = async (splits: string[], sep: string) => {
+    const out: string[] = [];
+    const cur: string[] = [];
+    let total = 0;
+
+    for (const d of splits) {
+      const l = await len(d);
+
+      if (total + l + cur.length * sep.length > chunkSize) {
+        if (total > chunkSize) {
+          // parity with LC: warn but continue
+          console.warn(
+            `Created a chunk of size ${total}, which is longer than the specified ${chunkSize}`
+          );
+        }
+        if (cur.length > 0) {
+          const doc = joinDocs(cur, sep);
+          if (doc !== null) out.push(doc);
+
+          // pop from the left until overlap satisfied or still too big
+          while (
+            total > chunkOverlap ||
+            (total + l + cur.length * sep.length > chunkSize && total > 0)
+          ) {
+            total -= await len(cur[0]!);
+            cur.shift();
+          }
+        }
+      }
+
+      cur.push(d);
+      total += l;
+    }
+
+    const doc = joinDocs(cur, sep);
+    if (doc !== null) out.push(doc);
+    return out;
+  };
+
+  const _split = async (t: string, seps: string[]): Promise<string[]> => {
+    const finalChunks: string[] = [];
+
+    // choose separator: first present wins, else last fallback
+    let sep = seps[seps.length - 1] || '';
+    let nextSeps: string[] | undefined;
+    for (let i = 0; i < seps.length; i++) {
+      const s = seps[i];
+      if (s === undefined) continue;
+      if (s === '') {
+        sep = s;
+        break;
+      }
+      if (t.includes(s)) {
+        sep = s;
+        nextSeps = seps.slice(i + 1);
+        break;
+      }
+    }
+
+    const splits = splitOnSeparator(t, sep, keepSeparator);
+    const mergeSep = keepSeparator ? '' : sep;
+
+    let good: string[] = [];
+    for (const s of splits) {
+      if ((await len(s)) < chunkSize) {
+        good.push(s);
+      } else {
+        if (good.length) {
+          const merged = await mergeSplits(good, mergeSep);
+          finalChunks.push(...merged);
+          good = [];
+        }
+        if (!nextSeps) {
+          finalChunks.push(s); // no finer separators left
+        } else {
+          const rec = await _split(s, nextSeps);
+          finalChunks.push(...rec);
+        }
+      }
+    }
+
+    if (good.length) {
+      const merged = await mergeSplits(good, mergeSep);
+      finalChunks.push(...merged);
+    }
+
+    return finalChunks;
+  };
+
+  return _split(text, separators);
+}
diff --git a/apps/server/src/lib/ai/document-retrievals.ts b/apps/server/src/lib/ai/document-retrievals.ts
index da17e142..03ac18eb 100644
--- a/apps/server/src/lib/ai/document-retrievals.ts
+++ b/apps/server/src/lib/ai/document-retrievals.ts
@@ -1,28 +1,22 @@
-import { MDocument } from '@mastra/rag';
-import { createOpenAI } from '@ai-sdk/openai';
+import { AIDocument } from '@colanode/server/lib/ai/utils';
 import { embed } from 'ai';
 import { sql } from 'kysely';
 
 import { database } from '@colanode/server/data/database';
 import { combineAndScoreSearchResults } from '@colanode/server/lib/ai/utils';
 import { config } from '@colanode/server/lib/config';
+import { getEmbeddingModel } from '@colanode/server/lib/ai/ai-models';
 import { QueryRewriteOutput } from '@colanode/server/types/ai';
 import { SearchResult } from '@colanode/server/types/retrieval';
 
-const embeddingModel = config.ai.enabled
-  ? createOpenAI({ apiKey: config.ai.embedding.apiKey }).embedding(
-      config.ai.embedding.modelName
-    )
-  : undefined;
-
 export const retrieveDocuments = async (
   rewrittenQuery: QueryRewriteOutput,
   workspaceId: string,
   userId: string,
   limit?: number,
   contextNodeIds?: string[]
-): Promise<MDocument[]> => {
-  if (!config.ai.enabled || !embeddingModel) {
+): Promise<AIDocument[]> => {
+  if (!config.ai.enabled) {
     return [];
   }
 
@@ -33,9 +27,15 @@ export const retrieveDocuments = async (
 
   let semanticResults: SearchResult[] = [];
   if (doSemantic) {
+    const embeddingModel = getEmbeddingModel();
     const { embedding } = await embed({
       model: embeddingModel,
       value: rewrittenQuery.semanticQuery,
+      providerOptions: {
+        openai: {
+          dimensions: config.ai.embedding.dimensions,
+        },
+      },
     });
     if (embedding) {
       semanticResults = await semanticSearchDocuments(
@@ -85,7 +85,7 @@ const semanticSearchDocuments = async (
       'documents.created_at',
       'documents.created_by',
       'document_embeddings.chunk as chunk_index',
-      sql<number>`${sql.raw(`'[${embedding}]'::vector`)} <=> document_embeddings.embedding_vector`.as(
+      sql<number>`1 - (${sql.raw(`'[${embedding}]'::vector`)} <=> document_embeddings.embedding_vector)`.as(
         'similarity'
       ),
     ])
@@ -100,18 +100,21 @@ const semanticSearchDocuments = async (
   }
 
   const results = await queryBuilder
-    .groupBy([
+    .distinctOn([
       'document_embeddings.document_id',
-      'document_embeddings.text',
-      'document_embeddings.summary',
-      'documents.created_at',
-      'documents.created_by',
       'document_embeddings.chunk',
     ])
-    .orderBy('similarity', 'asc')
+    .orderBy('document_embeddings.document_id')
+    .orderBy('document_embeddings.chunk')
+    .orderBy('similarity', 'desc')
     .limit(limit)
     .execute();
 
+  console.log(
+    'results text',
+    results.map((r) => r.text)
+  );
+
   return results.map((result) => ({
     id: result.id,
     text: result.text,
@@ -196,8 +199,8 @@ const keywordSearchDocuments = async (
 const combineSearchResults = async (
   semanticResults: SearchResult[],
   keywordResults: SearchResult[]
-): Promise<MDocument[]> => {
-  if (!config.ai.enabled || !embeddingModel) {
+): Promise<AIDocument[]> => {
+  if (!config.ai.enabled) {
     return [];
   }
 
diff --git a/apps/server/src/lib/ai/node-retrievals.ts b/apps/server/src/lib/ai/node-retrievals.ts
index 6ea01b7d..08013130 100644
--- a/apps/server/src/lib/ai/node-retrievals.ts
+++ b/apps/server/src/lib/ai/node-retrievals.ts
@@ -1,28 +1,22 @@
-import { MDocument } from '@mastra/rag';
-import { createOpenAI } from '@ai-sdk/openai';
+import { AIDocument } from '@colanode/server/lib/ai/utils';
 import { embed } from 'ai';
 import { sql } from 'kysely';
 
 import { database } from '@colanode/server/data/database';
 import { combineAndScoreSearchResults } from '@colanode/server/lib/ai/utils';
 import { config } from '@colanode/server/lib/config';
+import { getEmbeddingModel } from '@colanode/server/lib/ai/ai-models';
 import { QueryRewriteOutput } from '@colanode/server/types/ai';
 import { SearchResult } from '@colanode/server/types/retrieval';
 
-const embeddingModel = config.ai.enabled
-  ? createOpenAI({ apiKey: config.ai.embedding.apiKey }).embedding(
-      config.ai.embedding.modelName
-    )
-  : undefined;
-
 export const retrieveNodes = async (
   rewrittenQuery: QueryRewriteOutput,
   workspaceId: string,
   userId: string,
   limit?: number,
   contextNodeIds?: string[]
-): Promise<MDocument[]> => {
-  if (!config.ai.enabled || !embeddingModel) {
+): Promise<AIDocument[]> => {
+  if (!config.ai.enabled) {
     return [];
   }
 
@@ -33,9 +27,15 @@ export const retrieveNodes = async (
 
   let semanticResults: SearchResult[] = [];
   if (doSemantic) {
+    const embeddingModel = getEmbeddingModel();
     const { embedding } = await embed({
       model: embeddingModel,
       value: rewrittenQuery.semanticQuery,
+      providerOptions: {
+        openai: {
+          dimensions: config.ai.embedding.dimensions,
+        },
+      },
     });
     if (embedding) {
       semanticResults = await semanticSearchNodes(
@@ -84,7 +84,7 @@ const semanticSearchNodes = async (
       'nodes.created_at',
       'nodes.created_by',
       'node_embeddings.chunk as chunk_index',
-      sql<number>`${sql.raw(`'[${embedding}]'::vector`)} <=> node_embeddings.embedding_vector`.as(
+      sql<number>`1 - (${sql.raw(`'[${embedding}]'::vector`)} <=> node_embeddings.embedding_vector)`.as(
         'similarity'
       ),
     ])
@@ -99,15 +99,10 @@ const semanticSearchNodes = async (
   }
 
   const results = await queryBuilder
-    .groupBy([
-      'node_embeddings.node_id',
-      'node_embeddings.text',
-      'nodes.created_at',
-      'nodes.created_by',
-      'node_embeddings.chunk',
-      'node_embeddings.summary',
-    ])
-    .orderBy('similarity', 'asc')
+    .distinctOn(['node_embeddings.node_id', 'node_embeddings.chunk'])
+    .orderBy('node_embeddings.node_id')
+    .orderBy('node_embeddings.chunk')
+    .orderBy('similarity', 'desc')
     .limit(limit)
     .execute();
 
@@ -194,8 +189,8 @@ const keywordSearchNodes = async (
 const combineSearchResults = async (
   semanticResults: SearchResult[],
   keywordResults: SearchResult[]
-): Promise<MDocument[]> => {
-  if (!config.ai.enabled || !embeddingModel) {
+): Promise<AIDocument[]> => {
+  if (!config.ai.enabled) {
     return [];
   }
 
diff --git a/apps/server/src/lib/ai/old-prompts.ts b/apps/server/src/lib/ai/old-prompts.ts
deleted file mode 100644
index 0041d5e7..00000000
--- a/apps/server/src/lib/ai/old-prompts.ts
+++ /dev/null
@@ -1,338 +0,0 @@
-import { PromptTemplate, ChatPromptTemplate } from '@langchain/core/prompts';
-
-export const queryRewritePrompt = PromptTemplate.fromTemplate(
-  `<task>
-  You are an expert at rewriting search queries to optimize for both semantic similarity and keyword-based search in a document retrieval system.
-  Your task is to generate two separate optimized queries:
-  1. A semantic search query optimized for vector embeddings and semantic similarity
-  2. A keyword search query optimized for full-text search using PostgreSQL's tsquery
-</task>
-
-<guidelines>
-  For semantic search query:
-  1. Focus on conceptual meaning and intent
-  2. Include context-indicating terms
-  3. Preserve relationship words between concepts
-  4. Expand concepts with related terms
-  5. Remove noise words and syntax-specific terms
-  
-  For keyword search query:
-  1. Focus on specific technical terms and exact matches
-  2. Include variations of key terms
-  3. Keep proper nouns and domain-specific vocabulary
-  4. Optimize for PostgreSQL's websearch_to_tsquery syntax
-  5. Include essential filters and constraints
-</guidelines>
-
-<input>
-  Original query: {query}
-</input>
-
-<output_format>
-  Return a JSON object with:
-  {{
-    "semanticQuery": "optimized query for semantic search",
-    "keywordQuery": "optimized query for keyword search"
-}}
-</output_format>`
-);
-
-export const summarizationPrompt = PromptTemplate.fromTemplate(
-  `<task>
-  Summarize the following text focusing on key points relevant to the user's query.
-  If the text is short (<100 characters), return it as is.
-</task>
-
-<input>
-  Text: {text}
-  User Query: {query}
-</input>`
-);
-
-export const rerankPrompt = PromptTemplate.fromTemplate(
-  `<task>
-  You are the final relevance judge in a hybrid search system. Your task is to re-rank search results by analyzing their true relevance to the user's query.
-  These documents have already passed through:
-  1. Semantic search (vector similarity)
-  2. Keyword-based search (full-text search)
-  
-  Your ranking will determine the final order and which documents are shown to the user.
-</task>
-
-<context>
-  Each document contains:
-  - Main content text
-  - Optional summary/context
-  - Metadata (type, creation info)
-  The documents can be:
-  - Workspace nodes (various content types)
-  - Documents (files, notes)
-  - Database records
-</context>
-
-<ranking_criteria>
-  Evaluate relevance based on:
-  1. Direct answer presence (highest priority)
-     - Does the content directly answer the query?
-     - Are key details or facts present?
-  
-  2. Contextual relevance
-     - How well does the content relate to the query topic?
-     - Is the context/summary relevant?
-     - Does it provide important background information?
-  
-  3. Information freshness
-     - For time-sensitive queries, prefer recent content
-     - For conceptual queries, recency matters less
-  
-  4. Content completeness
-     - Does it provide comprehensive information?
-     - Are related concepts explained?
-  
-  5. Source appropriateness
-     - Is the document type appropriate for the query?
-     - Does the source authority match the information need?
-</ranking_criteria>
-
-<scoring_guidelines>
-  Score from 0 to 1, where:
-  1.0: Perfect match, directly answers query
-  0.8-0.9: Highly relevant, contains most key information
-  0.5-0.7: Moderately relevant, contains some useful information
-  0.2-0.4: Tangentially relevant, minimal useful information
-  0.0-0.1: Not relevant or useful for the query
-</scoring_guidelines>
-
-<documents>
-  {context}
-</documents>
-
-<user_query>
-  {query}
-</user_query>
-
-<output_format>
-  Return a JSON array of objects, each containing:
-  - "index": original position (integer)
-  - "score": relevance score (0-1 float)
-  - "type": document type (string)
-  - "sourceId": original source ID (string)
-  
-  Example:
-  [
-    {{"index": 2, "score": 0.95, "type": "document", "sourceId": "doc123"}},
-    {{"index": 0, "score": 0.7, "type": "node", "sourceId": "node456"}}
-  ]
-</output_format>`
-);
-
-export const answerPrompt = ChatPromptTemplate.fromTemplate(
-  `<system_context>
-  You are an AI assistant in a collaboration workspace app called Colanode.
-
-  CURRENT TIME: {currentTimestamp}
-  WORKSPACE: {workspaceName}
-  USER: {userName} ({userEmail})
-</system_context>
-
-<current_conversation_history>
-  {formattedChatHistory}
-</current_conversation_history>
-
-<context>
-  {formattedDocuments}
-</context>
-
-<user_query>
-  {question}
-</user_query>
-
-<task>
-  Based solely on the current conversation history and the relevant context above, provide a clear and professional answer to the user's query. 
-  
-  Pay attention to the metadata provided for each source (like creation date, author, path, document type, etc.) to:
-  - Properly attribute information to its source
-  - Consider the recency and relevance of the information
-  - Understand the relationships between different content pieces
-  - Recognize the context in which content was created or modified
-  
-  In your answer, include exact quotes from the provided context that support your answer.
-  If the relevant context does not contain any information that answers the user's query, respond with "No relevant information found." This is a critical step to ensure correct answers.
-</task>
-
-<output_format>
-  Return your response as a JSON object with the following structure:
-  {{
-    "answer": <your answer as a string>,
-    "citations": [
-      {{ "sourceId": <source id>, "quote": <exact quote from the context> }},
-      ...
-    ]
-  }}
-</output_format>`
-);
-
-export const intentRecognitionPrompt = PromptTemplate.fromTemplate(
-  `<task>
-  Determine if the following user query requires retrieving context from the workspace's knowledge base.
-  You are a crucial decision point in an AI assistant system that must decide between:
-  1. Retrieving and using specific context from the workspace ("retrieve")
-  2. Answering directly from general knowledge ("no_context")
-</task>
-
-<context>
-  This system has access to:
-  - Documents and their embeddings
-  - Node content (various types of workspace items)
-  - Database records and their fields
-  - Previous conversation history
-</context>
-
-<guidelines>
-  Return "retrieve" when the query:
-  - Asks about specific workspace content, documents, or data
-  - References previous conversations or shared content
-  - Mentions specific projects, tasks, or workspace items
-  - Requires up-to-date information from the workspace
-  - Contains temporal references to workspace activity
-  - Asks about specific people or collaborators
-  - Needs details about database records or fields
-
-  Return "no_context" when the query:
-  - Asks for general knowledge or common facts
-  - Requests simple calculations or conversions
-  - Asks about general concepts without workspace specifics
-  - Makes small talk
-  - Requests explanations of universal concepts
-  - Can be answered correctly without workspace-specific information
-</guidelines>
-
-<examples>
-  "retrieve" examples:
-  - "What did John say about the API design yesterday?"
-  - "Show me the latest documentation about user authentication"
-  - "Find records in the Projects database where status is completed"
-  - "What were the key points from our last meeting?"
-
-  "no_context" examples:
-  - "What is REST API?"
-  - "How do I write a good commit message?"
-  - "Convert 42 kilometers to miles"
-  - "What's your name?"
-  - "Explain what is Docker in simple terms"
-</examples>
-
-<conversation_history>
-  {formattedChatHistory}
-</conversation_history>
-
-<user_query>
-  {question}
-</user_query>
-
-<output_format>
-  Return exactly one value: "retrieve" or "no_context"
-</output_format>`
-);
-
-export const noContextPrompt = PromptTemplate.fromTemplate(
-  `<task>
-  Answer the following query concisely using general knowledge, without retrieving additional context. Return only the answer.
-</task>
-
-<conversation_history>
-  {formattedChatHistory}
-</conversation_history>
-
-<user_query>
-  {question}
-</user_query>`
-);
-
-export const databaseFilterPrompt = ChatPromptTemplate.fromTemplate(
-  `<task>
-  You are an expert at analyzing natural language queries and converting them into structured database filters.
-  
-  Your task is to:
-  1. Determine if this query is asking or makes sense to answer by filtering/searching databases
-  2. If yes, generate appropriate filter attributes for each relevant database
-  3. If no, return shouldFilter: false
-</task>
-
-<context>
-  Available Databases:
-  {databasesInfo}
-</context>
-
-<user_query>
-  {query}
-</user_query>
-
-<guidelines>
-  Only include databases that are relevant to the query.
-  For each filter, use the exact field IDs from the database schema.
-  Use appropriate operators based on field types.
-</guidelines>
-
-<output_format>
-  Return a JSON object with:
-  - shouldFilter: boolean
-  - filters: array of objects with:
-    - databaseId: string
-    - filters: array of DatabaseViewFilterAttributes
-  
-  Example Response:
-  {{
-    "shouldFilter": true,
-    "filters": [
-      {{
-        "databaseId": "db1",
-        "filters": [
-          {{
-            "type": "field",
-            "fieldId": "field1",
-            "operator": "contains",
-            "value": "search term"
-          }}
-        ]
-      }}
-    ]
-  }}
-</output_format>`
-);
-
-export const chunkSummarizationPrompt = PromptTemplate.fromTemplate(
-  `<task>
-  Generate a concise summary of the following text chunk that is part of a larger document. 
-  This summary will be used to enhance vector search retrieval by providing additional context about this specific chunk.
-</task>
-
-<context>
-  Content Type: {nodeType}
-</context>
-
-<guidelines>
-  1. Create a brief (30-50 words) summary that captures the key points and main idea of the chunk
-  2. Consider how this chunk fits into the overall document provided
-  3. If the chunk appears to be part of a specific section, identify its role or purpose
-  4. If the chunk contains structured data (like a database record), identify the type of information it represents
-  5. Use neutral, descriptive language
-  6. Consider the content type ("{nodeType}") when creating the summary - different types have different purposes:
-     - "message": Communication content in a conversation
-     - "page": Document-like content with structured information
-     - "record": Database record with specific fields and values
-     - Other types: Adapt your summary accordingly
-</guidelines>
-
-<complete_document>
-  {fullText}
-</complete_document>
-
-<chunk_to_summarize>
-  {chunk}
-</chunk_to_summarize>
-
-<output_format>
-  Provide only the summary with no additional commentary or explanations.
-</output_format>`
-);
diff --git a/apps/server/src/lib/ai/utils.ts b/apps/server/src/lib/ai/utils.ts
index 99cc44fe..a1bfe93a 100644
--- a/apps/server/src/lib/ai/utils.ts
+++ b/apps/server/src/lib/ai/utils.ts
@@ -1,4 +1,4 @@
-import { MDocument } from '@mastra/rag';
+import ms from 'ms';
 import { SearchResult } from '@colanode/server/types/retrieval';
 
 export const formatDate = (date?: Date | null): string => {
@@ -33,8 +33,8 @@ const processSearchResult = (
   const key = createKey(result);
   const recencyBoost = calculateRecencyBoost(result.createdAt);
   const normalizedScore = isKeyword
-    ? (result.score / maxScore) * weight
-    : ((maxScore - result.score) / maxScore) * weight;
+    ? (result.score / maxScore) * weight // rank normalized
+    : Math.max(0, Math.min(1, result.score)) * weight; // similarity already 0..1
 
   if (combined.has(key)) {
     const existing = combined.get(key)!;
@@ -47,27 +47,12 @@ const processSearchResult = (
   }
 };
 
-const createDocumentFromResult = (
-  result: SearchResult & { finalScore: number },
-  authorMap: Map<string, { id: string; name: string | null }>
-): MDocument => {
-  const author = result.createdBy ? authorMap.get(result.createdBy) : null;
-  const summaryPart = (result.summary ?? '').trim();
-  const content = summaryPart
-    ? `${summaryPart}\n\n${result.text}`
-    : result.text;
-
-  const doc = MDocument.fromText(content, {
-    id: result.id,
-    score: result.finalScore,
-    createdAt: result.createdAt,
-
-    type: (result as any).sourceType ?? 'document',
-    chunkIndex: result.chunkIndex,
-    author: author ? { id: author.id, name: author.name || 'Unknown' } : null,
-  });
-
-  return doc;
+export type AIDocument = {
+  id: string;
+  text: string;
+  summary: string | null;
+  score: number;
+  metadata: Record<string, any>[];
 };
 
 export const combineAndScoreSearchResults = (
@@ -76,7 +61,7 @@ export const combineAndScoreSearchResults = (
   semanticSearchWeight: number,
   keywordSearchWeight: number,
   authorMap: Map<string, { id: string; name: string | null }>
-): Promise<MDocument[]> => {
+): Promise<AIDocument[]> => {
   const maxSemanticScore = Math.max(...semanticResults.map((r) => r.score), 1);
   const maxKeywordScore = Math.max(...keywordResults.map((r) => r.score), 1);
 
@@ -103,6 +88,26 @@ export const combineAndScoreSearchResults = (
   return Promise.resolve(
     Array.from(combined.values())
       .sort((a, b) => b.finalScore - a.finalScore)
-      .map((result) => createDocumentFromResult(result, authorMap))
+      .map((result) => {
+        const author = result.createdBy
+          ? authorMap.get(result.createdBy)
+          : null;
+        const type = (result as any).sourceType ?? 'document';
+        const meta: Record<string, any> = {
+          type,
+          createdAt: result.createdAt ?? null,
+          createdBy: result.createdBy ?? null,
+          chunkIndex: result.chunkIndex,
+        };
+        if (author)
+          meta.author = { id: author.id, name: author.name || 'Unknown' };
+        return {
+          id: result.id,
+          text: result.text,
+          summary: result.summary,
+          score: result.finalScore,
+          metadata: [meta],
+        };
+      })
   );
 };
diff --git a/apps/server/src/lib/config/ai.ts b/apps/server/src/lib/config/ai.ts
index cc577a52..a4a9beba 100644
--- a/apps/server/src/lib/config/ai.ts
+++ b/apps/server/src/lib/config/ai.ts
@@ -87,91 +87,91 @@ export const aiConfigSchema = z.discriminatedUnion('enabled', [
 export type AiConfig = z.infer<typeof aiConfigSchema>;
 
 export const readAiConfigVariables = () => {
-  return {
-    enabled: false,
-  };
-
   // return {
-  //   enabled: process.env.AI_ENABLED === 'true',
-  //   nodeEmbeddingDelay: process.env.AI_NODE_EMBEDDING_DELAY,
-  //   documentEmbeddingDelay: process.env.AI_DOCUMENT_EMBEDDING_DELAY,
-  //   providers: {
-  //     openai: {
-  //       apiKey: process.env.OPENAI_API_KEY,
-  //       enabled: process.env.OPENAI_ENABLED,
-  //     },
-  //     google: {
-  //       apiKey: process.env.GOOGLE_API_KEY,
-  //       enabled: process.env.GOOGLE_ENABLED,
-  //     },
-  //   },
-  //   langfuse: {
-  //     enabled: process.env.LANGFUSE_ENABLED,
-  //     publicKey: process.env.LANGFUSE_PUBLIC_KEY,
-  //     secretKey: process.env.LANGFUSE_SECRET_KEY,
-  //     baseUrl: process.env.LANGFUSE_BASE_URL,
-  //   },
-  //   models: {
-  //     queryRewrite: {
-  //       provider: process.env.QUERY_REWRITE_PROVIDER,
-  //       modelName: process.env.QUERY_REWRITE_MODEL,
-  //       temperature: process.env.QUERY_REWRITE_TEMPERATURE,
-  //     },
-  //     response: {
-  //       provider: process.env.RESPONSE_PROVIDER,
-  //       modelName: process.env.RESPONSE_MODEL,
-  //       temperature: process.env.RESPONSE_TEMPERATURE,
-  //     },
-  //     rerank: {
-  //       provider: process.env.RERANK_PROVIDER,
-  //       modelName: process.env.RERANK_MODEL,
-  //       temperature: process.env.RERANK_TEMPERATURE,
-  //     },
-  //     summarization: {
-  //       provider: process.env.SUMMARIZATION_PROVIDER,
-  //       modelName: process.env.SUMMARIZATION_MODEL,
-  //       temperature: process.env.SUMMARIZATION_TEMPERATURE,
-  //     },
-  //     contextEnhancer: {
-  //       provider: process.env.CHUNK_CONTEXT_PROVIDER,
-  //       modelName: process.env.CHUNK_CONTEXT_MODEL,
-  //       temperature: process.env.CHUNK_CONTEXT_TEMPERATURE,
-  //     },
-  //     noContext: {
-  //       provider: process.env.NO_CONTEXT_PROVIDER,
-  //       modelName: process.env.NO_CONTEXT_MODEL,
-  //       temperature: process.env.NO_CONTEXT_TEMPERATURE,
-  //     },
-  //     intentRecognition: {
-  //       provider: process.env.INTENT_RECOGNITION_PROVIDER,
-  //       modelName: process.env.INTENT_RECOGNITION_MODEL,
-  //       temperature: process.env.INTENT_RECOGNITION_TEMPERATURE,
-  //     },
-  //     databaseFilter: {
-  //       provider: process.env.DATABASE_FILTER_PROVIDER,
-  //       modelName: process.env.DATABASE_FILTER_MODEL,
-  //       temperature: process.env.DATABASE_FILTER_TEMPERATURE,
-  //     },
-  //   },
-  //   embedding: {
-  //     provider: process.env.EMBEDDING_PROVIDER,
-  //     modelName: process.env.EMBEDDING_MODEL,
-  //     dimensions: process.env.EMBEDDING_DIMENSIONS,
-  //     apiKey: process.env.EMBEDDING_API_KEY,
-  //     batchSize: process.env.EMBEDDING_BATCH_SIZE,
-  //   },
-  //   chunking: {
-  //     defaultChunkSize: process.env.CHUNK_DEFAULT_CHUNK_SIZE,
-  //     defaultOverlap: process.env.CHUNK_DEFAULT_OVERLAP,
-  //     enhanceWithContext: process.env.CHUNK_ENHANCE_WITH_CONTEXT,
-  //   },
-  //   retrieval: {
-  //     hybridSearch: {
-  //       semanticSearchWeight:
-  //         process.env.RETRIEVAL_HYBRID_SEARCH_SEMANTIC_WEIGHT,
-  //       keywordSearchWeight: process.env.RETRIEVAL_HYBRID_SEARCH_KEYWORD_WEIGHT,
-  //       maxResults: process.env.RETRIEVAL_HYBRID_SEARCH_MAX_RESULTS,
-  //     },
-  //   },
+  //   enabled: true,
   // };
+
+  return {
+    enabled: process.env.AI_ENABLED === 'true',
+    nodeEmbeddingDelay: process.env.AI_NODE_EMBEDDING_DELAY,
+    documentEmbeddingDelay: process.env.AI_DOCUMENT_EMBEDDING_DELAY,
+    providers: {
+      openai: {
+        apiKey: process.env.OPENAI_API_KEY,
+        enabled: process.env.OPENAI_ENABLED,
+      },
+      google: {
+        apiKey: process.env.GOOGLE_API_KEY,
+        enabled: process.env.GOOGLE_ENABLED,
+      },
+    },
+    langfuse: {
+      enabled: process.env.LANGFUSE_ENABLED,
+      publicKey: process.env.LANGFUSE_PUBLIC_KEY,
+      secretKey: process.env.LANGFUSE_SECRET_KEY,
+      baseUrl: process.env.LANGFUSE_BASE_URL,
+    },
+    models: {
+      queryRewrite: {
+        provider: process.env.QUERY_REWRITE_PROVIDER,
+        modelName: process.env.QUERY_REWRITE_MODEL,
+        temperature: process.env.QUERY_REWRITE_TEMPERATURE,
+      },
+      response: {
+        provider: process.env.RESPONSE_PROVIDER,
+        modelName: process.env.RESPONSE_MODEL,
+        temperature: process.env.RESPONSE_TEMPERATURE,
+      },
+      rerank: {
+        provider: process.env.RERANK_PROVIDER,
+        modelName: process.env.RERANK_MODEL,
+        temperature: process.env.RERANK_TEMPERATURE,
+      },
+      summarization: {
+        provider: process.env.SUMMARIZATION_PROVIDER,
+        modelName: process.env.SUMMARIZATION_MODEL,
+        temperature: process.env.SUMMARIZATION_TEMPERATURE,
+      },
+      contextEnhancer: {
+        provider: process.env.CHUNK_CONTEXT_PROVIDER,
+        modelName: process.env.CHUNK_CONTEXT_MODEL,
+        temperature: process.env.CHUNK_CONTEXT_TEMPERATURE,
+      },
+      noContext: {
+        provider: process.env.NO_CONTEXT_PROVIDER,
+        modelName: process.env.NO_CONTEXT_MODEL,
+        temperature: process.env.NO_CONTEXT_TEMPERATURE,
+      },
+      intentRecognition: {
+        provider: process.env.INTENT_RECOGNITION_PROVIDER,
+        modelName: process.env.INTENT_RECOGNITION_MODEL,
+        temperature: process.env.INTENT_RECOGNITION_TEMPERATURE,
+      },
+      databaseFilter: {
+        provider: process.env.DATABASE_FILTER_PROVIDER,
+        modelName: process.env.DATABASE_FILTER_MODEL,
+        temperature: process.env.DATABASE_FILTER_TEMPERATURE,
+      },
+    },
+    embedding: {
+      provider: process.env.EMBEDDING_PROVIDER,
+      modelName: process.env.EMBEDDING_MODEL,
+      dimensions: process.env.EMBEDDING_DIMENSIONS,
+      apiKey: process.env.EMBEDDING_API_KEY,
+      batchSize: process.env.EMBEDDING_BATCH_SIZE,
+    },
+    chunking: {
+      defaultChunkSize: process.env.CHUNK_DEFAULT_CHUNK_SIZE,
+      defaultOverlap: process.env.CHUNK_DEFAULT_OVERLAP,
+      enhanceWithContext: process.env.CHUNK_ENHANCE_WITH_CONTEXT,
+    },
+    retrieval: {
+      hybridSearch: {
+        semanticSearchWeight:
+          process.env.RETRIEVAL_HYBRID_SEARCH_SEMANTIC_WEIGHT,
+        keywordSearchWeight: process.env.RETRIEVAL_HYBRID_SEARCH_KEYWORD_WEIGHT,
+        maxResults: process.env.RETRIEVAL_HYBRID_SEARCH_MAX_RESULTS,
+      },
+    },
+  };
 };
diff --git a/apps/server/src/lib/observability/otel.ts b/apps/server/src/lib/observability/otel.ts
new file mode 100644
index 00000000..d6bc6140
--- /dev/null
+++ b/apps/server/src/lib/observability/otel.ts
@@ -0,0 +1,36 @@
+import { NodeSDK } from '@opentelemetry/sdk-node';
+import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
+import { LangfuseExporter } from 'langfuse-vercel';
+
+let sdk: NodeSDK | null = null;
+let started = false;
+
+export const initObservability = () => {
+  if (started) return;
+
+  const secretKey = process.env.LANGFUSE_SECRET_KEY;
+  const publicKey = process.env.LANGFUSE_PUBLIC_KEY;
+  const baseUrl = process.env.LANGFUSE_BASEURL;
+
+  if (!secretKey || !publicKey) {
+    return;
+  }
+
+  console.log('LANGFUSE_PUBLIC_KEY', publicKey);
+  console.log('LANGFUSE_BASEURL', baseUrl);
+
+  const exporter = new LangfuseExporter({
+    secretKey,
+    publicKey,
+    baseUrl: baseUrl || 'https://cloud.langfuse.com',
+  });
+
+  sdk = new NodeSDK({
+    traceExporter: exporter,
+    instrumentations: [getNodeAutoInstrumentations()],
+  });
+
+  sdk.start();
+
+  started = true;
+};
diff --git a/apps/server/src/services/assistant-trigger.ts b/apps/server/src/services/assistant-trigger.ts
new file mode 100644
index 00000000..be8ca563
--- /dev/null
+++ b/apps/server/src/services/assistant-trigger.ts
@@ -0,0 +1,42 @@
+import { eventBus } from '@colanode/server/lib/event-bus';
+import { fetchNode } from '@colanode/server/lib/nodes';
+import { createLogger } from '@colanode/server/lib/logger';
+import { jobService } from '@colanode/server/services/job-service';
+
+const logger = createLogger('server:service:assistant-trigger');
+
+export const initAssistantTrigger = () => {
+  eventBus.subscribe(async (event) => {
+    if (event.type !== 'node.created') {
+      return;
+    }
+
+    try {
+      const node = await fetchNode(event.nodeId);
+      if (!node) {
+        return;
+      }
+
+      const attributes = node.attributes as {
+        type?: string;
+        subtype?: string;
+      } | null;
+      if (
+        attributes?.type === 'message' &&
+        attributes?.subtype === 'question'
+      ) {
+        await jobService.addJob({
+          type: 'assistant.respond',
+          messageId: node.id,
+          workspaceId: event.workspaceId,
+        });
+
+        logger.debug(
+          `Queued assistant response for question message ${node.id} (workspace ${event.workspaceId})`
+        );
+      }
+    } catch (error) {
+      logger.error(error, 'Failed handling node.created for assistant trigger');
+    }
+  });
+};
diff --git a/apps/server/src/services/job-service.ts b/apps/server/src/services/job-service.ts
index c91d77aa..7a606bcf 100644
--- a/apps/server/src/services/job-service.ts
+++ b/apps/server/src/services/job-service.ts
@@ -20,7 +20,9 @@ class JobService {
   private readonly prefix = `{${config.redis.jobs.prefix}}`;
 
   public async initQueue(): Promise<void> {
+    console.log('initQueue');
     if (this.jobQueue) {
+      console.log('jobQueue already initialized');
       return;
     }
 
@@ -39,11 +41,14 @@ class JobService {
       logger.error(error, `Job queue error`);
     });
 
+    console.log('initRecurringJobs');
     await this.initRecurringJobs();
   }
 
   public async initWorker() {
+    console.log('initWorker');
     if (this.jobWorker) {
+      console.log('jobWorker already initialized');
       return;
     }
 
@@ -61,9 +66,38 @@ class JobService {
       throw new Error('Job queue not initialized.');
     }
 
+    console.log('adding job', job.type);
     await this.jobQueue.add(job.type, job, options);
   }
 
+  public async removeJob(jobId: string) {
+    if (!this.jobQueue) {
+      throw new Error('Job queue not initialized.');
+    }
+
+    console.log(`Removing job ${jobId}`);
+    const job = await this.jobQueue.getJob(jobId);
+    if (job) {
+      await job.remove();
+      console.log(`Successfully removed job ${jobId}`);
+    } else {
+      console.log(`Job ${jobId} not found, nothing to remove`);
+    }
+  }
+
+  public async getJob(jobId: string): Promise<Job | null> {
+    if (!this.jobQueue) {
+      throw new Error('Job queue not initialized.');
+    }
+
+    try {
+      return await this.jobQueue.getJob(jobId);
+    } catch (error) {
+      console.error(`Error getting job ${jobId}:`, error);
+      return null;
+    }
+  }
+
   private handleJobJob = async (job: Job) => {
     const input = job.data as JobInput;
     const handler = jobHandlerMap[input.type] as JobHandler<typeof input>;
diff --git a/apps/server/src/types/ai.ts b/apps/server/src/types/ai.ts
index 98f80f9f..9667961e 100644
--- a/apps/server/src/types/ai.ts
+++ b/apps/server/src/types/ai.ts
@@ -8,6 +8,7 @@ export const assistantWorkflowInputSchema = z.object({
   userInput: z.string(),
   workspaceId: z.string(),
   userId: z.string(),
+  workspaceName: z.string(),
   userDetails: z.object({
     name: z.string(),
     email: z.string(),
@@ -54,7 +55,7 @@ export const searchResultsOutputSchema = z.object({
       sourceId: z.string(),
       score: z.number(),
       type: z.string(),
-      metadata: z.record(z.any()),
+      metadata: z.array(z.record(z.string(), z.any())),
     })
   ),
   searchType: z.enum(['semantic', 'keyword', 'database', 'hybrid']),
@@ -68,7 +69,7 @@ export const rankedResultsOutputSchema = z.object({
       sourceId: z.string(),
       relevanceScore: z.number(),
       type: z.string(),
-      metadata: z.record(z.any()),
+      metadata: z.array(z.record(z.string(), z.any())),
     })
   ),
   citations: z.array(
@@ -109,10 +110,29 @@ export const WorkflowConfig = {
 // TYPE EXPORTS
 // ============================================================================
 
-export type AssistantWorkflowInput = z.infer<typeof assistantWorkflowInputSchema>;
-export type AssistantWorkflowOutput = z.infer<typeof assistantWorkflowOutputSchema>;
-export type IntentClassificationOutput = z.infer<typeof intentClassificationOutputSchema>;
+export type AssistantWorkflowInput = z.infer<
+  typeof assistantWorkflowInputSchema
+>;
+export type AssistantWorkflowOutput = z.infer<
+  typeof assistantWorkflowOutputSchema
+>;
+export type IntentClassificationOutput = z.infer<
+  typeof intentClassificationOutputSchema
+>;
 export type QueryRewriteOutput = z.infer<typeof queryRewriteOutputSchema>;
 export type SearchResultsOutput = z.infer<typeof searchResultsOutputSchema>;
 export type RankedResultsOutput = z.infer<typeof rankedResultsOutputSchema>;
-export type AnswerOutput = z.infer<typeof answerOutputSchema>;
\ No newline at end of file
+export type AnswerOutput = z.infer<typeof answerOutputSchema>;
+
+// =========================================================================
+// HYBRID SEARCH ARGS
+// =========================================================================
+
+export type HybridSearchArgs = {
+  semanticQuery: string;
+  keywordQuery: string;
+  workspaceId: string;
+  userId: string;
+  maxResults?: number;
+  selectedContextNodeIds?: string[];
+};
diff --git a/hosting/docker/docker-compose.yaml b/hosting/docker/docker-compose.yaml
index 93c3e7a0..c4d70646 100644
--- a/hosting/docker/docker-compose.yaml
+++ b/hosting/docker/docker-compose.yaml
@@ -151,11 +151,11 @@ services:
       REDIS_URL: 'redis://:your_valkey_password@valkey:6379/0'
       REDIS_DB: '0'
       # Optional variables:
-      # REDIS_JOBS_QUEUE_NAME: 'jobs'
-      # REDIS_JOBS_QUEUE_PREFIX: 'colanode'
-      # REDIS_TUS_LOCK_PREFIX: 'colanode:tus:lock'
-      # REDIS_TUS_KV_PREFIX: 'colanode:tus:kv'
-      # REDIS_EVENTS_CHANNEL: 'events'
+      REDIS_JOBS_QUEUE_NAME: 'jobs'
+      REDIS_JOBS_QUEUE_PREFIX: 'colanode'
+      REDIS_TUS_LOCK_PREFIX: 'colanode:tus:lock'
+      REDIS_TUS_KV_PREFIX: 'colanode:tus:kv'
+      REDIS_EVENTS_CHANNEL: 'events'
 
       # ───────────────────────────────────────────────────────────────
       # S3 configuration for files.
diff --git a/package-lock.json b/package-lock.json
index a5f3c5cb..18a3c63b 100644
--- a/package-lock.json
+++ b/package-lock.json
@@ -61,6 +61,7 @@
         "@electron-forge/publisher-github": "^7.8.3",
         "@electron/fuses": "^2.0.0",
         "@electron/rebuild": "^4.0.1",
+        "@malept/cross-spawn-promise": "^2.0.0",
         "@tailwindcss/postcss": "^4.1.11",
         "@types/better-sqlite3": "^7.6.13",
         "@types/electron-squirrel-startup": "^1.0.2",
@@ -86,19 +87,20 @@
       "name": "@colanode/server",
       "version": "1.0.0",
       "dependencies": {
+        "@ai-sdk/google": "^2.0.6",
+        "@ai-sdk/openai": "^2.0.15",
         "@aws-sdk/client-s3": "^3.863.0",
         "@colanode/core": "*",
         "@colanode/crdt": "*",
-        "@fastify/cors": "^11.1.0",
-        "@fastify/websocket": "^11.2.0",
-        "@langchain/core": "^0.3.68",
-        "@langchain/google-genai": "^0.2.16",
-        "@langchain/langgraph": "^0.4.3",
-        "@langchain/openai": "^0.6.6",
+        "@fastify/cors": "^11.0.1",
+        "@fastify/websocket": "^11.1.0",
         "@node-rs/argon2": "^2.0.2",
+        "@opentelemetry/auto-instrumentations-node": "^0.62.1",
+        "@opentelemetry/sdk-node": "^0.203.0",
         "@redis/client": "^5.8.0",
         "@tus/s3-store": "^2.0.0",
         "@tus/server": "^2.3.0",
+        "ai": "^5.0.15",
         "bullmq": "^5.56.9",
         "diff": "^8.0.2",
         "dotenv": "^17.2.1",
@@ -108,8 +110,7 @@
         "js-sha256": "^0.11.0",
         "ky": "^1.8.2",
         "kysely": "^0.28.4",
-        "langchain": "^0.3.30",
-        "langfuse-langchain": "^3.38.4",
+        "langfuse-vercel": "^3.38.4",
         "ms": "^2.1.3",
         "nodemailer": "^7.0.5",
         "pg": "^8.16.3",
@@ -118,6 +119,7 @@
         "zod": "^4.0.15"
       },
       "devDependencies": {
+        "@types/ms": "^2.1.0",
         "@types/node": "^24.2.0",
         "@types/nodemailer": "^6.4.17",
         "@types/pg": "^8.15.5",
@@ -136,6 +138,15 @@
         "node": ">=0.3.1"
       }
     },
+    "apps/server/node_modules/zod": {
+      "version": "4.0.17",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.0.17.tgz",
+      "integrity": "sha512-1PHjlYRevNxxdy2JZ8JcNAw7rX8V9P1AKkP+x/xZfxB0K5FYfuV+Ug6P/6NVSR2jHQ+FzDDoDHS04nYUsOIyLQ==",
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
     "apps/web": {
       "name": "@colanode/web",
       "dependencies": {
@@ -152,6 +163,84 @@
         "vite-plugin-pwa": "^1.0.2"
       }
     },
+    "node_modules/@ai-sdk/gateway": {
+      "version": "1.0.7",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/gateway/-/gateway-1.0.7.tgz",
+      "integrity": "sha512-Athrq7OARuNc0iHZJP6InhSQ53tImCc990vMWyR1UHaZgPZJbXjKhIMiOj54F0I0Nlemx48V4fHYUTfLkJotnQ==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@ai-sdk/provider": "2.0.0",
+        "@ai-sdk/provider-utils": "3.0.3"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.76 || ^4"
+      }
+    },
+    "node_modules/@ai-sdk/google": {
+      "version": "2.0.6",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/google/-/google-2.0.6.tgz",
+      "integrity": "sha512-8acuseWJI+RRH99JDWM/n7IJRuuGNa4YzLXB/leqE/ZByHyIiVWGADjJi/vfnJnmdM5fQnezJ6SRTF6feI5rSQ==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@ai-sdk/provider": "2.0.0",
+        "@ai-sdk/provider-utils": "3.0.3"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.76 || ^4"
+      }
+    },
+    "node_modules/@ai-sdk/openai": {
+      "version": "2.0.15",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/openai/-/openai-2.0.15.tgz",
+      "integrity": "sha512-/IUyQ9ck4uUTtGojvQamcUWpNWkwpL/P1F6LYRxpQGj07H00oJEBH/VUizrIq0ZvW/vkuK6c6X4UJS9PrdYyxA==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@ai-sdk/provider": "2.0.0",
+        "@ai-sdk/provider-utils": "3.0.3"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.76 || ^4"
+      }
+    },
+    "node_modules/@ai-sdk/provider": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/provider/-/provider-2.0.0.tgz",
+      "integrity": "sha512-6o7Y2SeO9vFKB8lArHXehNuusnpddKPk7xqL7T2/b+OvXMRIXUO1rR4wcv1hAFUAT9avGZshty3Wlua/XA7TvA==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "json-schema": "^0.4.0"
+      },
+      "engines": {
+        "node": ">=18"
+      }
+    },
+    "node_modules/@ai-sdk/provider-utils": {
+      "version": "3.0.3",
+      "resolved": "https://registry.npmjs.org/@ai-sdk/provider-utils/-/provider-utils-3.0.3.tgz",
+      "integrity": "sha512-kAxIw1nYmFW1g5TvE54ZB3eNtgZna0RnLjPUp1ltz1+t9xkXJIuDT4atrwfau9IbS0BOef38wqrI8CjFfQrxhw==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "@ai-sdk/provider": "2.0.0",
+        "@standard-schema/spec": "^1.0.0",
+        "eventsource-parser": "^3.0.3",
+        "zod-to-json-schema": "^3.24.1"
+      },
+      "engines": {
+        "node": ">=18"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.76 || ^4"
+      }
+    },
     "node_modules/@alloc/quick-lru": {
       "version": "5.2.0",
       "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
@@ -190,23 +279,6 @@
         "@jridgewell/sourcemap-codec": "^1.4.14"
       }
     },
-    "node_modules/@apidevtools/json-schema-ref-parser": {
-      "version": "11.9.3",
-      "resolved": "https://registry.npmjs.org/@apidevtools/json-schema-ref-parser/-/json-schema-ref-parser-11.9.3.tgz",
-      "integrity": "sha512-60vepv88RwcJtSHrD6MjIL6Ta3SOYbgfnkHb+ppAVK+o9mXprRtulx7VlRl3lN3bbvysAfCS7WMVfhUYemB0IQ==",
-      "license": "MIT",
-      "dependencies": {
-        "@jsdevtools/ono": "^7.1.3",
-        "@types/json-schema": "^7.0.15",
-        "js-yaml": "^4.1.0"
-      },
-      "engines": {
-        "node": ">= 16"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/philsturgeon"
-      }
-    },
     "node_modules/@aws-crypto/crc32": {
       "version": "5.2.0",
       "resolved": "https://registry.npmjs.org/@aws-crypto/crc32/-/crc32-5.2.0.tgz",
@@ -5525,133 +5597,21 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
-    "node_modules/@langchain/core": {
-      "version": "0.3.68",
-      "resolved": "https://registry.npmjs.org/@langchain/core/-/core-0.3.68.tgz",
-      "integrity": "sha512-dWPT1h9ObG1TK9uivFTk/pgBULZ6/tBmq8czGUjZjR+1xh9jB4tm/D5FY6o5FklXcEpnAI9peNq2x17Kl9wbMg==",
-      "license": "MIT",
-      "dependencies": {
-        "@cfworker/json-schema": "^4.0.2",
-        "ansi-styles": "^5.0.0",
-        "camelcase": "6",
-        "decamelize": "1.2.0",
-        "js-tiktoken": "^1.0.12",
-        "langsmith": "^0.3.46",
-        "mustache": "^4.2.0",
-        "p-queue": "^6.6.2",
-        "p-retry": "4",
-        "uuid": "^10.0.0",
-        "zod": "^3.25.32",
-        "zod-to-json-schema": "^3.22.3"
-      },
-      "engines": {
-        "node": ">=18"
-      }
-    },
-    "node_modules/@langchain/core/node_modules/@types/uuid": {
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-      "resolved": "https://registry.npmjs.org/@types/uuid/-/uuid-10.0.0.tgz",
-      "integrity": "sha512-7gqG38EyHgyP1S+7+xomFtL+ZNHcKv6DwNaCZmJmo1vgMugyF3TCnXVg4t1uk89mLNwnLtnY3TpOpCOyp1/xHQ==",
-      "license": "MIT"
-    },
-    "node_modules/@langchain/core/node_modules/ansi-styles": {
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-      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-5.2.0.tgz",
-      "integrity": "sha512-Cxwpt2SfTzTtXcfOlzGEee8O+c+MmUgGrNiBcXnuWxuFJHe6a5Hz7qwhwe5OgaSYI0IJvkLqWX1ASG+cJOkEiA==",
-      "license": "MIT",
-      "engines": {
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-      },
-      "funding": {
-        "url": "https://github.com/chalk/ansi-styles?sponsor=1"
-      }
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-      "resolved": "https://registry.npmjs.org/langsmith/-/langsmith-0.3.50.tgz",
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-      "license": "MIT",
-      "dependencies": {
-        "@types/uuid": "^10.0.0",
-        "chalk": "^4.1.2",
-        "console-table-printer": "^2.12.1",
-        "p-queue": "^6.6.2",
-        "p-retry": "4",
-        "semver": "^7.6.3",
-        "uuid": "^10.0.0"
-      },
-      "peerDependencies": {
-        "@opentelemetry/api": "*",
-        "@opentelemetry/exporter-trace-otlp-proto": "*",
-        "@opentelemetry/sdk-trace-base": "*",
-        "openai": "*"
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-          "optional": true
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-        "@opentelemetry/sdk-trace-base": {
-          "optional": true
-        },
-        "openai": {
-          "optional": true
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-    "node_modules/@langchain/core/node_modules/uuid": {
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-      "resolved": "https://registry.npmjs.org/uuid/-/uuid-10.0.0.tgz",
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-      "funding": [
-        "https://github.com/sponsors/broofa",
-        "https://github.com/sponsors/ctavan"
-      ],
-      "license": "MIT",
-      "bin": {
-        "uuid": "dist/bin/uuid"
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-    "node_modules/@langchain/core/node_modules/zod": {
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       "license": "MIT",
       "funding": {
-        "url": "https://github.com/sponsors/colinhacks"
+        "type": "opencollective",
+        "url": "https://opencollective.com/js-sdsl"
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+    "node_modules/@malept/cross-spawn-promise": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/@malept/cross-spawn-promise/-/cross-spawn-promise-2.0.0.tgz",
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+      "dev": true,
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@@ -5670,44 +5630,11 @@
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-      "resolved": "https://registry.npmjs.org/@langchain/langgraph-checkpoint/-/langgraph-checkpoint-0.1.0.tgz",
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-      "license": "MIT",
-      "funding": {
-        "type": "github",
-        "url": "https://github.com/sponsors/kossnocorp"
-      }
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+      "extraneous": true,
       "license": "BSD-2-Clause",
       "engines": {
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@@ -5716,158 +5643,6 @@
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-        "https://github.com/sponsors/ctavan"
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-      "license": "MIT",
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-      "integrity": "sha512-Jtc2612XINuBjIl/QTWsV5UvE8UHuNblcO3vVADSrKsrc6RqGX6lOW1cEo3CM2v0XG4Nat8nI+YM7/f26VxXLw==",
-      "license": "Unlicense",
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "type": "individual",
-        "url": "https://github.com/sponsors/porsager"
-      }
-    },
     "node_modules/postgres-array": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/postgres-array/-/postgres-array-2.0.0.tgz",
@@ -21857,6 +21201,30 @@
         "prosemirror-transform": "^1.1.0"
       }
     },
+    "node_modules/protobufjs": {
+      "version": "7.5.4",
+      "resolved": "https://registry.npmjs.org/protobufjs/-/protobufjs-7.5.4.tgz",
+      "integrity": "sha512-CvexbZtbov6jW2eXAvLukXjXUW1TzFaivC46BpWc/3BpcCysb5Vffu+B3XHMm8lVEuy2Mm4XGex8hBSg1yapPg==",
+      "hasInstallScript": true,
+      "license": "BSD-3-Clause",
+      "dependencies": {
+        "@protobufjs/aspromise": "^1.1.2",
+        "@protobufjs/base64": "^1.1.2",
+        "@protobufjs/codegen": "^2.0.4",
+        "@protobufjs/eventemitter": "^1.1.0",
+        "@protobufjs/fetch": "^1.1.0",
+        "@protobufjs/float": "^1.0.2",
+        "@protobufjs/inquire": "^1.1.0",
+        "@protobufjs/path": "^1.1.2",
+        "@protobufjs/pool": "^1.1.0",
+        "@protobufjs/utf8": "^1.1.0",
+        "@types/node": ">=13.7.0",
+        "long": "^5.0.0"
+      },
+      "engines": {
+        "node": ">=12.0.0"
+      }
+    },
     "node_modules/pstree.remy": {
       "version": "1.1.8",
       "resolved": "https://registry.npmjs.org/pstree.remy/-/pstree.remy-1.1.8.tgz",
@@ -21939,15 +21307,6 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/radash": {
-      "version": "12.1.1",
-      "resolved": "https://registry.npmjs.org/radash/-/radash-12.1.1.tgz",
-      "integrity": "sha512-h36JMxKRqrAxVD8201FrCpyeNuUY9Y5zZwujr20fFO77tpUtGa6EZzfKw/3WaiBX95fq7+MpsuMLNdSnORAwSA==",
-      "license": "MIT",
-      "engines": {
-        "node": ">=14.18.0"
-      }
-    },
     "node_modules/random-path": {
       "version": "0.1.2",
       "resolved": "https://registry.npmjs.org/random-path/-/random-path-0.1.2.tgz",
@@ -22559,6 +21918,20 @@
         "node": ">=0.10.0"
       }
     },
+    "node_modules/require-in-the-middle": {
+      "version": "7.5.2",
+      "resolved": "https://registry.npmjs.org/require-in-the-middle/-/require-in-the-middle-7.5.2.tgz",
+      "integrity": "sha512-gAZ+kLqBdHarXB64XpAe2VCjB7rIRv+mU8tfRWziHRJ5umKsIHN2tLLv6EtMw7WCdP19S0ERVMldNvxYCHnhSQ==",
+      "license": "MIT",
+      "dependencies": {
+        "debug": "^4.3.5",
+        "module-details-from-path": "^1.0.3",
+        "resolve": "^1.22.8"
+      },
+      "engines": {
+        "node": ">=8.6.0"
+      }
+    },
     "node_modules/requires-port": {
       "version": "1.0.0",
       "resolved": "https://registry.npmjs.org/requires-port/-/requires-port-1.0.0.tgz",
@@ -22905,7 +22278,6 @@
       "version": "2.1.2",
       "resolved": "https://registry.npmjs.org/safer-buffer/-/safer-buffer-2.1.2.tgz",
       "integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==",
-      "dev": true,
       "license": "MIT",
       "optional": true
     },
@@ -23188,12 +22560,6 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
-    "node_modules/sift": {
-      "version": "17.1.3",
-      "resolved": "https://registry.npmjs.org/sift/-/sift-17.1.3.tgz",
-      "integrity": "sha512-Rtlj66/b0ICeFzYTuNvX/EF1igRbbnGSvEyT79McoZa/DeGhMyC5pWKOEsZKnpkqtSeovd5FL/bjHWC3CIIvCQ==",
-      "license": "MIT"
-    },
     "node_modules/siginfo": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/siginfo/-/siginfo-2.0.0.tgz",
@@ -23281,12 +22647,6 @@
         "node": ">=10"
       }
     },
-    "node_modules/simple-wcswidth": {
-      "version": "1.1.2",
-      "resolved": "https://registry.npmjs.org/simple-wcswidth/-/simple-wcswidth-1.1.2.tgz",
-      "integrity": "sha512-j7piyCjAeTDSjzTSQ7DokZtMNwNlEAyxqSZeCS+CXH7fJ4jx3FuJ/mTW3mE+6JLs4VJBbcll0Kjn+KXI5t21Iw==",
-      "license": "MIT"
-    },
     "node_modules/slice-ansi": {
       "version": "5.0.0",
       "resolved": "https://registry.npmjs.org/slice-ansi/-/slice-ansi-5.0.0.tgz",
@@ -23471,15 +22831,6 @@
       "dev": true,
       "license": "CC0-1.0"
     },
-    "node_modules/spex": {
-      "version": "3.4.1",
-      "resolved": "https://registry.npmjs.org/spex/-/spex-3.4.1.tgz",
-      "integrity": "sha512-Br0Mu3S+c70kr4keXF+6K4B8ohR+aJjI9s7SbdsI3hliE1Riz4z+FQk7FQL+r7X1t90KPkpuKwQyITpCIQN9mg==",
-      "license": "MIT",
-      "engines": {
-        "node": ">=14.0.0"
-      }
-    },
     "node_modules/split2": {
       "version": "4.2.0",
       "resolved": "https://registry.npmjs.org/split2/-/split2-4.2.0.tgz",
@@ -24080,19 +23431,6 @@
         "node": ">= 10"
       }
     },
-    "node_modules/swr": {
-      "version": "2.3.4",
-      "resolved": "https://registry.npmjs.org/swr/-/swr-2.3.4.tgz",
-      "integrity": "sha512-bYd2lrhc+VarcpkgWclcUi92wYCpOgMws9Sd1hG1ntAu0NEy+14CbotuFjshBU2kt9rYj9TSmDcybpxpeTU1fg==",
-      "license": "MIT",
-      "dependencies": {
-        "dequal": "^2.0.3",
-        "use-sync-external-store": "^1.4.0"
-      },
-      "peerDependencies": {
-        "react": "^16.11.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
-      }
-    },
     "node_modules/tabbable": {
       "version": "6.2.0",
       "resolved": "https://registry.npmjs.org/tabbable/-/tabbable-6.2.0.tgz",
@@ -24411,18 +23749,6 @@
         "real-require": "^0.2.0"
       }
     },
-    "node_modules/throttleit": {
-      "version": "2.1.0",
-      "resolved": "https://registry.npmjs.org/throttleit/-/throttleit-2.1.0.tgz",
-      "integrity": "sha512-nt6AMGKW1p/70DF/hGBdJB57B8Tspmbp5gfJ8ilhLnt7kkr2ye7hzD6NVG8GGErk2HWF34igrL2CXmNIkzKqKw==",
-      "license": "MIT",
-      "engines": {
-        "node": ">=18"
-      },
-      "funding": {
-        "url": "https://github.com/sponsors/sindresorhus"
-      }
-    },
     "node_modules/tiny-each-async": {
       "version": "2.0.3",
       "resolved": "https://registry.npmjs.org/tiny-each-async/-/tiny-each-async-2.0.3.tgz",
@@ -25225,12 +24551,6 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
-    "node_modules/uncrypto": {
-      "version": "0.1.3",
-      "resolved": "https://registry.npmjs.org/uncrypto/-/uncrypto-0.1.3.tgz",
-      "integrity": "sha512-Ql87qFHB3s/De2ClA9e0gsnS6zXG27SkTiSJwjCc9MebbfapQfuPzumMIUMi38ezPZVNFcHI9sUIepeQfw8J8Q==",
-      "license": "MIT"
-    },
     "node_modules/undefsafe": {
       "version": "2.0.5",
       "resolved": "https://registry.npmjs.org/undefsafe/-/undefsafe-2.0.5.tgz",
@@ -25242,7 +24562,6 @@
       "version": "7.10.0",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-7.10.0.tgz",
       "integrity": "sha512-t5Fy/nfn+14LuOc2KNYg75vZqClpAiqscVvMygNnlsHBFpSXdJaYtXMcdNLpl/Qvc3P2cB3s6lOV51nqsFq4ag==",
-      "devOptional": true,
       "license": "MIT"
     },
     "node_modules/unicode-canonical-property-names-ecmascript": {
@@ -25820,15 +25139,6 @@
         "defaults": "^1.0.3"
       }
     },
-    "node_modules/web-streams-polyfill": {
-      "version": "4.0.0-beta.3",
-      "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-4.0.0-beta.3.tgz",
-      "integrity": "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug==",
-      "license": "MIT",
-      "engines": {
-        "node": ">= 14"
-      }
-    },
     "node_modules/webidl-conversions": {
       "version": "4.0.2",
       "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-4.0.2.tgz",
@@ -26575,16 +25885,6 @@
         "node": ">=0.6.0"
       }
     },
-    "node_modules/xstate": {
-      "version": "5.20.1",
-      "resolved": "https://registry.npmjs.org/xstate/-/xstate-5.20.1.tgz",
-      "integrity": "sha512-i9ZpNnm/XhCOMUxae1suT8PjYNTStZWbhmuKt4xeTPaYG5TS0Fz0i+Ka5yxoNPpaHW3VW6JIowrwFgSTZONxig==",
-      "license": "MIT",
-      "funding": {
-        "type": "opencollective",
-        "url": "https://opencollective.com/xstate"
-      }
-    },
     "node_modules/xtend": {
       "version": "4.0.2",
       "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
@@ -26708,45 +26008,25 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
-    "node_modules/zeroentropy": {
-      "version": "0.1.0-alpha.6",
-      "resolved": "https://registry.npmjs.org/zeroentropy/-/zeroentropy-0.1.0-alpha.6.tgz",
-      "integrity": "sha512-8roJUFph+VgQ13ABYm5XmtlHvabMD5r1TA9tOKQA7MVc2h0y2JymtmoWJl4qaaHQtpFkqzJu4pNY7rLksMOWBg==",
-      "license": "Apache-2.0",
-      "dependencies": {
-        "@types/node": "^18.11.18",
-        "@types/node-fetch": "^2.6.4",
-        "abort-controller": "^3.0.0",
-        "agentkeepalive": "^4.2.1",
-        "form-data-encoder": "1.7.2",
-        "formdata-node": "^4.3.2",
-        "node-fetch": "^2.6.7"
-      }
-    },
-    "node_modules/zeroentropy/node_modules/@types/node": {
-      "version": "18.19.121",
-      "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.121.tgz",
-      "integrity": "sha512-bHOrbyztmyYIi4f1R0s17QsPs1uyyYnGcXeZoGEd227oZjry0q6XQBQxd82X1I57zEfwO8h9Xo+Kl5gX1d9MwQ==",
-      "license": "MIT",
-      "dependencies": {
-        "undici-types": "~5.26.4"
-      }
-    },
-    "node_modules/zeroentropy/node_modules/undici-types": {
-      "version": "5.26.5",
-      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
-      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==",
-      "license": "MIT"
-    },
     "node_modules/zod": {
-      "version": "4.0.15",
-      "resolved": "https://registry.npmjs.org/zod/-/zod-4.0.15.tgz",
-      "integrity": "sha512-2IVHb9h4Mt6+UXkyMs0XbfICUh1eUrlJJAOupBHUhLRnKkruawyDddYRCs0Eizt900ntIMk9/4RksYl+FgSpcQ==",
+      "version": "3.25.76",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-3.25.76.tgz",
+      "integrity": "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ==",
       "license": "MIT",
+      "peer": true,
       "funding": {
         "url": "https://github.com/sponsors/colinhacks"
       }
     },
+    "node_modules/zod-to-json-schema": {
+      "version": "3.24.6",
+      "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.24.6.tgz",
+      "integrity": "sha512-h/z3PKvcTcTetyjl1fkj79MHNEjm+HpD6NXheWjzOekY7kV+lwDYnHw+ivHkijnCSMz1yJaWBD9vu/Fcmk+vEg==",
+      "license": "ISC",
+      "peerDependencies": {
+        "zod": "^3.24.1"
+      }
+    },
     "packages/client": {
       "name": "@colanode/client",
       "version": "1.0.0",
@@ -26777,6 +26057,15 @@
         "zod": "^4.0.15"
       }
     },
+    "packages/core/node_modules/zod": {
+      "version": "4.0.17",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.0.17.tgz",
+      "integrity": "sha512-1PHjlYRevNxxdy2JZ8JcNAw7rX8V9P1AKkP+x/xZfxB0K5FYfuV+Ug6P/6NVSR2jHQ+FzDDoDHS04nYUsOIyLQ==",
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
     "packages/crdt": {
       "name": "@colanode/crdt",
       "version": "1.0.0",
@@ -26800,6 +26089,15 @@
         "node": ">=0.3.1"
       }
     },
+    "packages/crdt/node_modules/zod": {
+      "version": "4.0.17",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.0.17.tgz",
+      "integrity": "sha512-1PHjlYRevNxxdy2JZ8JcNAw7rX8V9P1AKkP+x/xZfxB0K5FYfuV+Ug6P/6NVSR2jHQ+FzDDoDHS04nYUsOIyLQ==",
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
     "packages/ui": {
       "name": "@colanode/ui",
       "version": "1.0.0",
@@ -26849,6 +26147,7 @@
         "cmdk": "^1.1.1",
         "date-fns": "^4.1.0",
         "is-hotkey": "^0.2.0",
+        "lowlight": "^3.3.0",
         "lucide-react": "^0.539.0",
         "re-resizable": "^6.11.2",
         "react": "^19.1.1",
@@ -26874,6 +26173,15 @@
         "tw-animate-css": "^1.3.6"
       }
     },
+    "packages/ui/node_modules/zod": {
+      "version": "4.0.17",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.0.17.tgz",
+      "integrity": "sha512-1PHjlYRevNxxdy2JZ8JcNAw7rX8V9P1AKkP+x/xZfxB0K5FYfuV+Ug6P/6NVSR2jHQ+FzDDoDHS04nYUsOIyLQ==",
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
     "scripts": {
       "name": "@colanode/scripts",
       "version": "1.0.0",
diff --git a/packages/client/src/handlers/mutations/messages/message-create.ts b/packages/client/src/handlers/mutations/messages/message-create.ts
index 45cb10c7..8e580094 100644
--- a/packages/client/src/handlers/mutations/messages/message-create.ts
+++ b/packages/client/src/handlers/mutations/messages/message-create.ts
@@ -59,7 +59,7 @@ export class MessageCreateMutationHandler
 
     const messageAttributes: MessageAttributes = {
       type: 'message',
-      subtype: 'standard',
+      subtype: input.subtype ?? 'standard',
       parentId: input.parentId,
       content: blocks,
       referenceId: input.referenceId,
diff --git a/packages/client/src/mutations/messages/message-create.ts b/packages/client/src/mutations/messages/message-create.ts
index f3b356dc..cc0ad309 100644
--- a/packages/client/src/mutations/messages/message-create.ts
+++ b/packages/client/src/mutations/messages/message-create.ts
@@ -7,6 +7,7 @@ export type MessageCreateMutationInput = {
   parentId: string;
   content: JSONContent;
   referenceId?: string;
+  subtype?: 'standard' | 'question';
 };
 
 export type MessageCreateMutationOutput = {
diff --git a/packages/ui/package.json b/packages/ui/package.json
index 3a23a886..f12b5317 100644
--- a/packages/ui/package.json
+++ b/packages/ui/package.json
@@ -70,6 +70,7 @@
     "cmdk": "^1.1.1",
     "date-fns": "^4.1.0",
     "is-hotkey": "^0.2.0",
+    "lowlight": "^3.3.0",
     "lucide-react": "^0.539.0",
     "re-resizable": "^6.11.2",
     "react": "^19.1.1",
diff --git a/packages/ui/src/components/messages/message-create.tsx b/packages/ui/src/components/messages/message-create.tsx
index 4d2a9fe0..74822423 100644
--- a/packages/ui/src/components/messages/message-create.tsx
+++ b/packages/ui/src/components/messages/message-create.tsx
@@ -41,6 +41,7 @@ export const MessageCreate = forwardRef<MessageCreateRefProps>((_, ref) => {
   const messageEditorRef = useRef<MessageEditorRefProps>(null);
   const [content, setContent] = useState<JSONContent | null>(null);
   const [replyTo, setReplyTo] = useState<LocalMessageNode | null>(null);
+  const [askAI, setAskAI] = useState<boolean>(false);
 
   const hasContent = content != null && editorHasContent(content);
 
@@ -79,9 +80,11 @@ export const MessageCreate = forwardRef<MessageCreateRefProps>((_, ref) => {
         workspaceId: workspace.id,
         referenceId: replyTo?.id,
         rootId: conversation.rootId,
+        subtype: askAI ? 'question' : undefined,
       },
       onSuccess: () => {
         setReplyTo(null);
+        setAskAI(false);
         if (messageEditorRef.current) {
           messageEditorRef.current.clear();
           messageEditorRef.current.focus();
@@ -170,6 +173,21 @@ export const MessageCreate = forwardRef<MessageCreateRefProps>((_, ref) => {
             )}
           </div>
           <div className="flex flex-row gap-2">
+            <button
+              type="button"
+              className={`${
+                conversation.canCreateMessage
+                  ? askAI
+                    ? 'text-blue-600'
+                    : 'text-muted-foreground'
+                  : 'text-muted-foreground'
+              }`}
+              disabled={isPending || !conversation.canCreateMessage}
+              onClick={() => setAskAI((v) => !v)}
+              title="Ask AI"
+            >
+              AI
+            </button>
             {isPending ? (
               <Spinner size={20} />
             ) : (