brainy/docs/conversation/MCP_INTEGRATION.md
David Snelling ced639cab1 feat: add infinite agent memory with MCP integration
Implement comprehensive conversation management system enabling AI agents
like Claude Code to maintain infinite context and history. Provides semantic
search, smart context retrieval, and automatic artifact linking using Brainy's
existing Triple Intelligence infrastructure.

Core Features:
- ConversationManager API for message storage and retrieval
- MCP protocol integration with 6 tools for Claude Code
- Context ranking using semantic, temporal, and graph scoring
- Neural clustering for theme discovery and deduplication
- Virtual filesystem integration for code artifact linking
- CLI commands for setup and management

Zero new infrastructure required - uses existing Brainy features:
- Storage via brain.add() with NounType.Message
- Relationships via brain.relate() with VerbType.Precedes
- Search via brain.find() with Triple Intelligence
- Clustering via brain.neural()
- Artifacts via brain.vfs()

One-command setup: brainy conversation setup

Version: 3.19.0
2025-09-29 15:37:11 -07:00

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Markdown

# MCP Integration - Claude Code Setup
**One command. Infinite memory.** This guide shows you how to give Claude Code infinite context and conversation history using Brainy's Model Control Protocol (MCP) integration.
## Quick Setup
### One-Time Configuration
```bash
# Install Brainy globally (if not already installed)
npm install -g @soulcraft/brainy
# Set up MCP server for Claude Code
brainy conversation setup
```
**That's it!** Claude Code now has infinite memory.
### What This Does
The setup command:
1. Creates `~/.brainy-memory/` directory
2. Initializes Brainy database with filesystem storage
3. Creates MCP server script
4. Registers server with Claude Code
5. Ready to use - no configuration needed
## How It Works
### Automatic Integration
Once set up, Claude Code **automatically**:
**On Every Message:**
- Saves your message with semantic embeddings
- Saves Claude's response with metadata
- Links code artifacts to conversations
- Tracks problem-solving phase and confidence
- Indexes everything for instant retrieval
**On Conversation Start:**
- Retrieves relevant past context
- Finds similar previous conversations
- Loads linked code artifacts
- Presents context to Claude seamlessly
**Result:** Claude never loses context or momentum, even across completely separate conversations.
### User Experience
**Before:**
```
You: "How do I implement JWT auth?"
Claude: [Implements authentication]
[Days later, new conversation]
You: "Can you fix the JWT token validation?"
Claude: "I don't have context about your JWT implementation..."
```
**After:**
```
You: "How do I implement JWT auth?"
Claude: [Implements authentication, saves to Brainy]
[Days later, new conversation]
You: "Can you fix the JWT token validation?"
Claude: "I found 3 related past conversations about JWT...
Here's the implementation from /auth/middleware.ts..."
[Full context automatically retrieved]
```
## MCP Tools
The MCP server exposes 6 conversation tools that Claude Code uses automatically:
### 1. conversation_save_message
Saves a message to conversation history.
**Used by:** Claude Code automatically after each message
**Parameters:**
- `content`: Message text
- `role`: 'user' | 'assistant' | 'system' | 'tool'
- `conversationId`: Conversation identifier
- `phase`: Problem-solving phase
- `confidence`: Confidence score
- `artifacts`: Array of artifact IDs
- `toolsUsed`: Tools used in this message
### 2. conversation_get_context
Retrieves relevant past context.
**Used by:** Claude Code at conversation start or when context is needed
**Parameters:**
- `query`: What to retrieve context for
- `limit`: Max messages (default: 10)
- `maxTokens`: Token budget (default: 50000)
- `relevanceThreshold`: Min similarity (default: 0.7)
- `includeArtifacts`: Include code/files
- `includeSimilarConversations`: Include similar past conversations
**Returns:**
- Ranked messages with relevance scores
- Linked artifacts
- Similar past conversations
- Metadata (query time, tokens, etc.)
### 3. conversation_search
Searches all conversations semantically.
**Used by:** When Claude needs to find specific past information
**Parameters:**
- `query`: Search query
- `role`: Filter by role
- `conversationId`: Filter by conversation
- `timeRange`: Time range filter
### 4. conversation_get_thread
Gets complete conversation thread.
**Used by:** When Claude needs full conversation history
**Parameters:**
- `conversationId`: Conversation ID
- `includeArtifacts`: Include linked artifacts
### 5. conversation_save_artifact
Saves code/file artifacts.
**Used by:** When Claude creates files
**Parameters:**
- `path`: File path
- `content`: File content
- `conversationId`: Conversation ID
- `messageId`: Message that created it
- `type`: 'code' | 'config' | 'data' | 'document'
- `language`: Programming language
### 6. conversation_find_similar
Finds similar past conversations.
**Used by:** For discovering related work
**Parameters:**
- `conversationId`: Conversation to find similar to
- `limit`: Max results
- `threshold`: Similarity threshold
## MCP Server Architecture
### Server Location
```
~/.brainy-memory/
├── mcp-server.js # MCP server script
└── data/ # Brainy database
├── nouns/ # Messages
├── verbs/ # Relationships
└── index/ # Search indexes
```
### Server Script
The generated server script (`~/.brainy-memory/mcp-server.js`):
```javascript
import { Brainy } from '@soulcraft/brainy'
import { BrainyMCPService } from '@soulcraft/brainy'
import { MCPConversationToolset } from '@soulcraft/brainy'
// Initialize Brainy
const brain = new Brainy({
storage: {
type: 'filesystem',
path: '~/.brainy-memory/data'
},
silent: true
})
await brain.init()
// Create MCP service
const mcpService = new BrainyMCPService(brain)
const conversationTools = new MCPConversationToolset(brain)
// Handle MCP requests via stdio
process.stdin.on('data', async (data) => {
const request = JSON.parse(data.toString())
let response
if (request.toolName?.startsWith('conversation_')) {
response = await conversationTools.handleRequest(request)
} else {
response = await mcpService.handleRequest(request)
}
process.stdout.write(JSON.stringify(response) + '\\n')
})
```
### Claude Code Configuration
Located at `~/.config/claude-code/mcp-servers.json`:
```json
{
"brainy-memory": {
"command": "node",
"args": ["~/.brainy-memory/mcp-server.js"],
"env": {
"NODE_ENV": "production"
}
}
}
```
## Advanced Configuration
### Custom Storage Location
Edit `~/.brainy-memory/mcp-server.js`:
```javascript
const brain = new Brainy({
storage: {
type: 'filesystem',
path: '/path/to/custom/location'
}
})
```
### Cloud Storage (Multi-Device Sync)
Use S3-compatible storage for sync across machines:
```javascript
const brain = new Brainy({
storage: {
type: 's3',
bucket: 'my-brainy-memory',
region: 'us-east-1'
}
})
```
**Requirements:** Set AWS credentials in environment:
```bash
export AWS_ACCESS_KEY_ID=xxx
export AWS_SECRET_ACCESS_KEY=yyy
```
### Memory Storage (Testing)
For testing or temporary use:
```javascript
const brain = new Brainy({
storage: { type: 'memory' }
})
```
**Note:** Memory storage is lost on server restart.
### Context Retrieval Options
Customize context retrieval behavior:
```javascript
const context = await conversationManager.getRelevantContext(query, {
limit: 15, // More messages
maxTokens: 80000, // Larger context window
relevanceThreshold: 0.6, // Lower threshold = more results
weights: {
semantic: 0.7, // Adjust relevance weights
temporal: 0.6,
graph: 0.4
}
})
```
## Troubleshooting
### Server Not Starting
**Check logs:**
```bash
# Server writes to stderr
tail -f ~/.brainy-memory/server.log
```
**Common issues:**
1. **Node version**: Requires Node.js 22 LTS
2. **Permissions**: Ensure ~/.brainy-memory is writable
3. **Port conflicts**: MCP uses stdio, no ports needed
### Claude Code Not Using Memory
**Verify setup:**
```bash
# Check MCP server is registered
cat ~/.config/claude-code/mcp-servers.json
# Test server manually
echo '{"type":"system_info","infoType":"version","requestId":"test","version":"1.0.0"}' | node ~/.brainy-memory/mcp-server.js
```
**Expected output:**
```json
{"success":true,"requestId":"test","version":"1.0.0","data":{"version":"1.0.0"}}
```
### Memory Not Persisting
**Check storage:**
```bash
# Verify data directory exists
ls -la ~/.brainy-memory/data/
# Check database files
du -sh ~/.brainy-memory/data/
```
**If empty:** Server may be using memory storage. Check `mcp-server.js` configuration.
### Performance Issues
**Optimize database:**
```bash
# Rebuild indexes
brainy conversation stats # This triggers index optimization
```
**Check size:**
```bash
# Show storage usage
du -sh ~/.brainy-memory/
```
**If too large:** Consider cloud storage or compaction strategies.
## CLI Commands
Manage conversations via CLI:
### View Statistics
```bash
brainy conversation stats
```
Output:
```
📊 Conversation Statistics
Overall:
Conversations: 42
Messages: 1,337
Total Tokens: 567,890
Avg Messages/Conversation: 31.8
Avg Tokens/Message: 425.1
By Role:
user: 650
assistant: 687
By Phase:
implementation: 423
planning: 201
testing: 98
```
### Search Messages
```bash
brainy conversation search -q "authentication" -l 10
```
### Get Context
```bash
brainy conversation context -q "JWT token validation" -l 15
```
### View Thread
```bash
brainy conversation thread -c conv_abc123
```
### Export Conversation
```bash
brainy conversation export -c conv_abc123 -o backup.json
```
### Import Conversation
```bash
brainy conversation import -o backup.json
```
## Security & Privacy
### Local-First by Default
- All data stored locally in `~/.brainy-memory/`
- No external services or APIs
- Complete privacy and control
### Data Encryption
For sensitive conversations, use encrypted filesystem:
```bash
# Create encrypted volume
hdiutil create -size 1g -encryption AES-256 -volname BrainyMemory ~/brainy-secure.dmg
# Mount and use
hdiutil attach ~/brainy-secure.dmg
brainy conversation setup --path /Volumes/BrainyMemory
```
### Access Control
MCP server runs with your user permissions. No additional authentication needed for local use.
## Next Steps
- [API Reference](./API_REFERENCE.md) - Complete API documentation
- [Examples](./EXAMPLES.md) - Usage examples and patterns
- [Advanced Features](./ADVANCED.md) - Advanced configuration and optimization
## Support
Issues or questions:
- GitHub: [soulcraftlabs/brainy/issues](https://github.com/soulcraftlabs/brainy/issues)
- Documentation: [docs.brainy.ai](https://docs.brainy.ai)