t6_mem0_v2/docs/examples/claude-code.mdx

---
title: 'Claude Code Integration'
description: 'Use T6 Mem0 v2 with Claude Code for AI-powered development'
---

# Claude Code Integration

Integrate the T6 Mem0 v2 MCP server with Claude Code to give your AI coding assistant persistent memory across sessions.

## Prerequisites

- Claude Code CLI installed
- T6 Mem0 v2 MCP server installed locally
- Python 3.11+ environment
- Running Supabase and Neo4j instances

## Installation

### 1. Install Dependencies

```bash
cd /path/to/t6_mem0_v2
pip install -r requirements.txt
```

### 2. Configure Environment

Create `.env` file with required credentials:

```bash
# OpenAI
OPENAI_API_KEY=your_openai_key_here

# Supabase (Vector Store)
SUPABASE_CONNECTION_STRING=postgresql://user:pass@host:port/database

# Neo4j (Graph Store)
NEO4J_URI=neo4j://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_neo4j_password

# Mem0 Configuration
MEM0_COLLECTION_NAME=t6_memories
MEM0_EMBEDDING_DIMS=1536
MEM0_VERSION=v1.1
```

### 3. Verify MCP Server

Test the stdio transport:

```bash
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | python -m mcp_server.main
```

Expected output:
```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "tools": [
      {"name": "add_memory", "description": "Add new memory from messages..."},
      {"name": "search_memories", "description": "Search memories by semantic similarity..."},
      ...
    ]
  }
}
```

## Claude Code Configuration

### Option 1: MCP Server Configuration (Recommended)

Add to your Claude Code MCP settings file (`~/.config/claude/mcp.json`):

```json
{
  "mcpServers": {
    "t6-mem0": {
      "command": "python",
      "args": ["-m", "mcp_server.main"],
      "cwd": "/path/to/t6_mem0_v2",
      "env": {
        "OPENAI_API_KEY": "${OPENAI_API_KEY}",
        "SUPABASE_CONNECTION_STRING": "${SUPABASE_CONNECTION_STRING}",
        "NEO4J_URI": "neo4j://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "${NEO4J_PASSWORD}",
        "MEM0_COLLECTION_NAME": "t6_memories",
        "MEM0_EMBEDDING_DIMS": "1536",
        "MEM0_VERSION": "v1.1"
      }
    }
  }
}
```

### Option 2: Direct Python Integration

Use the MCP SDK directly in Python:

```python
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Configure server
server_params = StdioServerParameters(
    command="python",
    args=["-m", "mcp_server.main"],
    env={
        "OPENAI_API_KEY": "your_key_here",
        "SUPABASE_CONNECTION_STRING": "postgresql://...",
        "NEO4J_URI": "neo4j://localhost:7687",
        "NEO4J_USER": "neo4j",
        "NEO4J_PASSWORD": "your_password"
    }
)

# Connect and use
async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        # Initialize session
        await session.initialize()

        # List available tools
        tools = await session.list_tools()
        print(f"Available tools: {[tool.name for tool in tools.tools]}")

        # Add a memory
        result = await session.call_tool(
            "add_memory",
            arguments={
                "messages": [
                    {"role": "user", "content": "I prefer TypeScript over JavaScript"},
                    {"role": "assistant", "content": "Got it, I'll remember that!"}
                ],
                "user_id": "developer_123"
            }
        )

        # Search memories
        results = await session.call_tool(
            "search_memories",
            arguments={
                "query": "What languages does the developer prefer?",
                "user_id": "developer_123",
                "limit": 5
            }
        )
```

## Usage Examples

### Example 1: Storing Code Preferences

```python
# User tells Claude Code their preferences
User: "I prefer using async/await over callbacks in JavaScript"

# Claude Code automatically calls add_memory
await session.call_tool(
    "add_memory",
    arguments={
        "messages": [
            {
                "role": "user",
                "content": "I prefer using async/await over callbacks in JavaScript"
            },
            {
                "role": "assistant",
                "content": "I'll remember your preference for async/await!"
            }
        ],
        "user_id": "developer_123",
        "metadata": {
            "category": "coding_preference",
            "language": "javascript"
        }
    }
)
```

### Example 2: Recalling Project Context

```python
# Later in a new session
User: "How should I structure this async function?"

# Claude Code searches memories first
memories = await session.call_tool(
    "search_memories",
    arguments={
        "query": "JavaScript async preferences",
        "user_id": "developer_123",
        "limit": 3
    }
)

# Claude uses retrieved context to provide personalized response
# "Based on your preference for async/await, here's how I'd structure it..."
```

### Example 3: Project-Specific Memory

```python
# Store project-specific information
await session.call_tool(
    "add_memory",
    arguments={
        "messages": [
            {
                "role": "user",
                "content": "This project uses Supabase for the database and Neo4j for the knowledge graph"
            },
            {
                "role": "assistant",
                "content": "Got it! I'll remember the tech stack for this project."
            }
        ],
        "user_id": "developer_123",
        "agent_id": "project_t6_mem0",
        "metadata": {
            "project": "t6_mem0_v2",
            "category": "tech_stack"
        }
    }
)
```

## Available Tools in Claude Code

Once configured, these tools are automatically available:

| Tool | Description | Use Case |
|------|-------------|----------|
| `add_memory` | Store information | Save preferences, project details, learned patterns |
| `search_memories` | Semantic search | Find relevant context from past conversations |
| `get_all_memories` | Get all memories | Review everything Claude knows about you |
| `update_memory` | Modify memory | Correct or update stored information |
| `delete_memory` | Remove specific memory | Clear outdated information |
| `delete_all_memories` | Clear all memories | Start fresh for new project |

## Best Practices

### 1. Use Meaningful User IDs

```python
# Good - descriptive IDs
user_id = "developer_john_doe"
agent_id = "project_ecommerce_backend"

# Avoid - generic IDs
user_id = "user1"
agent_id = "agent"
```

### 2. Add Rich Metadata

```python
metadata = {
    "project": "t6_mem0_v2",
    "category": "bug_fix",
    "file": "mcp_server/http_server.py",
    "timestamp": "2025-10-15T10:30:00Z",
    "session_id": "abc-123-def"
}
```

### 3. Search Before Adding

```python
# Check if information already exists
existing = await session.call_tool(
    "search_memories",
    arguments={
        "query": "Python coding style preferences",
        "user_id": "developer_123"
    }
)

# Only add if not found or needs updating
if not existing or needs_update:
    await session.call_tool("add_memory", ...)
```

### 4. Regular Cleanup

```python
# Periodically clean up old project memories
await session.call_tool(
    "delete_all_memories",
    arguments={
        "agent_id": "old_project_archived"
    }
)
```

## Troubleshooting

### MCP Server Won't Start

**Error**: `ModuleNotFoundError: No module named 'mcp_server'`

**Solution**: Ensure you're running from the correct directory:
```bash
cd /path/to/t6_mem0_v2
python -m mcp_server.main
```

### Database Connection Errors

**Error**: `Cannot connect to Supabase/Neo4j`

**Solution**: Verify services are running and credentials are correct:
```bash
# Test Neo4j
curl http://localhost:7474

# Test Supabase connection
psql $SUPABASE_CONNECTION_STRING -c "SELECT 1"
```

### Environment Variables Not Loading

**Error**: `KeyError: 'OPENAI_API_KEY'`

**Solution**: Load `.env` file or set environment variables:
```bash
# Load from .env
export $(cat .env | xargs)

# Or set directly
export OPENAI_API_KEY=your_key_here
```

### Slow Response Times

**Issue**: Tool calls taking longer than expected

**Solutions**:
- Check network latency to Supabase
- Verify Neo4j indexes are created
- Reduce `limit` parameter in search queries
- Consider caching frequently accessed memories

## Advanced Usage

### Custom Memory Categories

```python
# Define custom categories
CATEGORIES = {
    "preferences": "User coding preferences and style",
    "bugs": "Known bugs and their solutions",
    "architecture": "System design decisions",
    "dependencies": "Project dependencies and versions"
}

# Store with category
await session.call_tool(
    "add_memory",
    arguments={
        "messages": [...],
        "metadata": {
            "category": "architecture",
            "importance": "high"
        }
    }
)
```

### Multi-Agent Collaboration

```python
# Different agents for different purposes
AGENTS = {
    "code_reviewer": "Reviews code for best practices",
    "debugger": "Helps debug issues",
    "architect": "Provides architectural guidance"
}

# Store agent-specific knowledge
await session.call_tool(
    "add_memory",
    arguments={
        "messages": [...],
        "user_id": "developer_123",
        "agent_id": "code_reviewer",
        "metadata": {"role": "code_review"}
    }
)
```

### Session Management

```python
import uuid
from datetime import datetime

# Create session tracking
session_id = str(uuid.uuid4())
session_start = datetime.now().isoformat()

# Store with session context
metadata = {
    "session_id": session_id,
    "session_start": session_start,
    "context": "debugging_authentication"
}
```

## Next Steps

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