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Add support for memory
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‎config/gni/devtools_image_files.gni‎

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@@ -21,6 +21,7 @@ devtools_image_files = [
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"gdp-logo-light.png",
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"gdp-logo-dark.png",
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"browser-operator-logo.png",
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"demo.gif",
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]
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devtools_svg_sources = [

‎front_end/panels/ai_chat/agent_framework/implementation/ConfiguredAgents.ts‎

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@@ -8,6 +8,9 @@ import { SchemaBasedExtractorTool } from '../../tools/SchemaBasedExtractorTool.j
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import { StreamlinedSchemaExtractorTool } from '../../tools/StreamlinedSchemaExtractorTool.js';
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import { BookmarkStoreTool } from '../../tools/BookmarkStoreTool.js';
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import { DocumentSearchTool } from '../../tools/DocumentSearchTool.js';
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import { SearchMemoryTool } from '../../tools/SearchMemoryTool.js';
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import { UpdateMemoryTool } from '../../tools/UpdateMemoryTool.js';
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import { ListMemoryBlocksTool } from '../../tools/ListMemoryBlocksTool.js';
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import { NavigateURLTool, PerformActionTool, GetAccessibilityTreeTool, SearchContentTool, NavigateBackTool, NodeIDsToURLsTool, TakeScreenshotTool, ScrollPageTool, WaitTool, RenderWebAppTool, GetWebAppDataTool, RemoveWebAppTool, CreateFileTool, UpdateFileTool, DeleteFileTool, ReadFileTool, ListFilesTool } from '../../tools/Tools.js';
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import { UpdateTodoTool } from '../../tools/UpdateTodoTool.js';
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import { ExecuteCodeTool } from '../../tools/ExecuteCodeTool.js';
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import { createWebTaskAgentConfig } from './agents/WebTaskAgent.js';
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import { createEcommerceProductInfoAgentConfig } from './agents/EcommerceProductInfoAgent.js';
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import { createSearchAgentConfig } from './agents/SearchAgent.js';
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import { createMemoryAgentConfig } from './agents/MemoryAgent.js';
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import { createSearchMemoryAgentConfig } from './agents/SearchMemoryAgent.js';
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/**
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* Initialize all configured agents
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// Register bookmark and document search tools
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ToolRegistry.registerToolFactory('bookmark_store', () => new BookmarkStoreTool());
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ToolRegistry.registerToolFactory('document_search', () => new DocumentSearchTool());
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// Register memory tools
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ToolRegistry.registerToolFactory('search_memory', () => new SearchMemoryTool());
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ToolRegistry.registerToolFactory('update_memory', () => new UpdateMemoryTool());
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ToolRegistry.registerToolFactory('list_memory_blocks', () => new ListMemoryBlocksTool());
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// Create and register Direct URL Navigator Agent
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const directURLNavigatorAgentConfig = createDirectURLNavigatorAgentConfig();
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const ecommerceProductInfoAgent = new ConfigurableAgentTool(ecommerceProductInfoAgentConfig);
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ToolRegistry.registerToolFactory('ecommerce_product_info_fetcher_tool', () => ecommerceProductInfoAgent);
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// Create and register Memory Agent (background memory consolidation)
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const memoryAgentConfig = createMemoryAgentConfig();
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const memoryAgent = new ConfigurableAgentTool(memoryAgentConfig);
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ToolRegistry.registerToolFactory('memory_agent', () => memoryAgent);
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// Create and register Search Memory Agent (read-only memory search for orchestrators)
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const searchMemoryAgentConfig = createSearchMemoryAgentConfig();
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const searchMemoryAgent = new ConfigurableAgentTool(searchMemoryAgentConfig);
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ToolRegistry.registerToolFactory('search_memory_agent', () => searchMemoryAgent);
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}

‎front_end/panels/ai_chat/agent_framework/implementation/agents/MemoryAgent.ts‎

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// Copyright 2025 The Chromium Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file.
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import type { AgentToolConfig } from '../../ConfigurableAgentTool.js';
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import { ChatMessageEntity } from '../../../models/ChatTypes.js';
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import type { ChatMessage } from '../../../models/ChatTypes.js';
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import type { ConfigurableAgentArgs } from '../../ConfigurableAgentTool.js';
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import { MODEL_SENTINELS } from '../../../core/Constants.js';
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import { AGENT_VERSION } from './AgentVersion.js';
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const MEMORY_AGENT_PROMPT = `You are a Memory Consolidation Agent that runs in the background after conversations end.
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## Your Purpose
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Extract and organize important information from completed conversations into persistent memory blocks that will help the assistant in future conversations.
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## Memory Block Types
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| Block | Purpose | Max Size |
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|-------|---------|----------|
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| user | User identity, preferences, communication style | 20000 chars |
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| facts | Factual information learned from conversations | 20000 chars |
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| project_<name> | Project-specific context (up to 4 projects) | 20000 chars each |
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## Workflow
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1. **List current memory** using list_memory_blocks
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2. **Analyze** the conversation for extractable information
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3. **Check for duplicates** before adding new facts
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4. **Update blocks** with consolidated, organized content
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5. **Verify** changes are correct and within limits
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## What to Extract
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### High Priority (Always Extract)
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- User's name, role, job title
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- Explicit preferences ("I prefer...", "I like...", "Always use...")
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- Project names, tech stacks, goals
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- Recurring patterns in requests
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### Medium Priority (Extract if Relevant)
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- Problem-solving approaches that worked
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- Tools/libraries the user uses frequently
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- Team members or collaborators mentioned
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### Skip (Do Not Extract)
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- One-time troubleshooting details
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- Temporary debugging information
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- Generic conversation pleasantries
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- Information already in memory
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## Writing Guidelines
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### Be Specific with Dates
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❌ "Recently discussed migration"
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✅ "2025-01-15: Discussed database migration to PostgreSQL"
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### Be Concise
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❌ "The user mentioned that they have a strong preference for using TypeScript in their projects because they find it helps catch errors"
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✅ "Prefers TypeScript for type safety"
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### Use Bullet Points
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\`\`\`
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- Name: Alex Chen
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- Role: Senior Frontend Engineer
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- Prefers: TypeScript, React, Tailwind CSS
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- Dislikes: Inline styles, any types
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\`\`\`
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### Consolidate Related Info
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If user block has:
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\`\`\`
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- Likes dark mode
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- Uses VS Code
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- Prefers dark themes
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\`\`\`
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Consolidate to:
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\`\`\`
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- Prefers dark mode/themes
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- Uses VS Code
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\`\`\`
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## Examples
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### Example 1: User Preferences
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**Conversation excerpt:**
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> User: "Hey, I'm Sarah. Can you help me debug this React component? I always use functional components with hooks, never class components."
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**Memory update (user block):**
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\`\`\`
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- Name: Sarah
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- React: Functional components + hooks only, no class components
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\`\`\`
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### Example 2: Project Context
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**Conversation excerpt:**
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> User: "Working on our e-commerce platform. We're using Next.js 14 with App Router, Prisma for the database, and Stripe for payments."
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**Memory update (project_ecommerce block):**
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\`\`\`
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Project: E-commerce Platform
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Stack: Next.js 14 (App Router), Prisma, Stripe
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\`\`\`
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### Example 3: Skip Extraction
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**Conversation excerpt:**
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> User: "Getting a 404 error on /api/users endpoint"
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> Assistant: "The route file is missing, create app/api/users/route.ts"
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> User: "Fixed, thanks!"
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**Action:** No extraction needed - one-time debugging, no lasting value.
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## Output
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After processing, briefly state what was updated or why nothing was updated.
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`;
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/**
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* Create the configuration for the Memory Agent
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*/
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export function createMemoryAgentConfig(): AgentToolConfig {
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return {
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name: 'memory_agent',
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version: AGENT_VERSION,
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description: 'Background memory consolidation agent that extracts facts from conversations and maintains organized memory blocks.',
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ui: {
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displayName: 'Memory Agent',
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avatar: '🧠',
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color: '#8b5cf6',
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backgroundColor: '#f5f3ff'
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},
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systemPrompt: MEMORY_AGENT_PROMPT,
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tools: [
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'search_memory',
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'update_memory',
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'list_memory_blocks',
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],
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schema: {
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type: 'object',
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properties: {
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conversation_summary: {
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type: 'string',
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description: 'Summary of the conversation to analyze for memory extraction'
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},
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reasoning: {
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type: 'string',
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description: 'Why this extraction is being run'
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}
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},
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required: ['conversation_summary', 'reasoning']
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},
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prepareMessages: (args: ConfigurableAgentArgs): ChatMessage[] => {
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return [{
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entity: ChatMessageEntity.USER,
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text: `## Conversation to Analyze
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${args.conversation_summary || ''}
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## Reason for Extraction
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${args.reasoning || 'Automatic extraction after session completion'}
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Please analyze this conversation and update memory blocks as appropriate.`,
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}];
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},
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maxIterations: 5,
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modelName: MODEL_SENTINELS.USE_MINI, // Cost-effective for background task
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temperature: 0.1,
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handoffs: [],
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};
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}

‎front_end/panels/ai_chat/agent_framework/implementation/agents/SearchMemoryAgent.ts‎

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// Copyright 2025 The Chromium Authors. All rights reserved.
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// Use of this source code is governed by a BSD-style license that can be
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// found in the LICENSE file.
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import type { AgentToolConfig, ConfigurableAgentArgs } from '../../ConfigurableAgentTool.js';
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import { ChatMessageEntity } from '../../../models/ChatTypes.js';
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import type { ChatMessage } from '../../../models/ChatTypes.js';
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import { MODEL_SENTINELS } from '../../../core/Constants.js';
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import { AGENT_VERSION } from './AgentVersion.js';
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const SEARCH_MEMORY_AGENT_PROMPT = `You are a Memory Retrieval Agent. Your job is to find and summarize relevant information from stored memory to help the assistant respond to the user.
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## Memory Structure
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| Block | Contains |
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|-------|----------|
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| user | User identity, preferences, communication style |
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| facts | Factual information from past conversations |
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| project_* | Project-specific context (tech stack, goals, current work) |
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## Workflow
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1. Use list_memory_blocks to retrieve all stored memory
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2. Scan each block for information relevant to the query
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3. Return a concise summary of relevant findings
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## Response Format
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### When Memory Exists
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Return relevant information organized by category:
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\`\`\`
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**User Context:**
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- Name: Sarah, Senior Frontend Engineer
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- Prefers TypeScript, functional React components
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**Relevant Project:**
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- E-commerce Platform: Next.js 14, Prisma, Stripe
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**Related Facts:**
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- 2025-01-10: Migrated auth to NextAuth.js
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\`\`\`
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### When No Memory Exists
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Simply respond: "No relevant memory found."
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### When Memory is Empty
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Simply respond: "No memory stored yet."
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## Guidelines
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- Only include information relevant to the query
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- Don't dump entire blocks - summarize what's useful
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- Prioritize recent information over old
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- If query is vague, return user preferences + active project context
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`;
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/**
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* Create the configuration for the Search Memory Agent.
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* This agent provides read-only memory search capability to orchestrator agents.
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*/
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export function createSearchMemoryAgentConfig(): AgentToolConfig {
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return {
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name: 'search_memory_agent',
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version: AGENT_VERSION,
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description: 'Search user memory for relevant information. Use when you need to recall user preferences, past facts, or project context.',
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ui: {
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displayName: 'Search Memory',
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avatar: '🔍',
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color: '#10b981',
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backgroundColor: '#ecfdf5'
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},
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systemPrompt: SEARCH_MEMORY_AGENT_PROMPT,
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tools: [
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'list_memory_blocks', // Returns all memory block contents directly
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],
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schema: {
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type: 'object',
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properties: {
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query: {
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type: 'string',
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description: 'What to search for in memory (user preferences, facts, project info)'
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},
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context: {
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type: 'string',
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description: 'Why this search is needed (helps with relevance)'
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}
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},
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required: ['query']
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},
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prepareMessages: (args: ConfigurableAgentArgs): ChatMessage[] => {
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return [{
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entity: ChatMessageEntity.USER,
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text: `Search memory for: ${args.query || ''}
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${args.context ? `\nContext: ${args.context}` : ''}
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Please search memory and return any relevant information.`,
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}];
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},
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maxIterations: 2, // Just need to list and respond
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modelName: MODEL_SENTINELS.USE_NANO, // Fast, cheap model for simple searches
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temperature: 0,
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handoffs: [],
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};
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}

‎front_end/panels/ai_chat/core/AgentNodes.ts‎

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}();
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return finalNode;
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}
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