AI Toolkit

Требуется: Python 3.10+, uv. # Установка: git clone https://github.com/shaneholloman/mcp-memory-service.git cd mcp-memory-service && pip install -e . # Claude Desktop — claude_desktop_config.json: { "mcpServers": { "memory": { "command": "python", "args": ["-m", "mcp_memory_service"], "env": { "MCP_MEMORY_CHROMA_PATH": "/path/to/chroma_db", "MCP_MEMORY_BACKUPS_PATH": "/path/to/backups" } } } } # Claude Code (CLI): claude mcp add memory --env MCP_MEMORY_CHROMA_PATH=/path/to/db -- python -m mcp_memory_service # OpenCode — ~/.config/opencode/opencode.json: { "mcp": { "memory": { "type": "local", "command": ["python", "-m", "mcp_memory_service"], "environment": { "MCP_MEMORY_CHROMA_PATH": "/path/to/chroma_db" } } } }

# pip (Python SDK) pip install mem0ai # Node.js SDK npm install mem0ai # Пример: базовое использование (Python, in-memory для быстрого старта) import os from mem0 import Memory os.environ["OPENAI_API_KEY"] = "YOUR_KEY" m = Memory() user_id = "alice" # Сохранить факты из диалога m.add("I prefer Python over JavaScript", user_id=user_id) m.add("I'm building a trading bot with 3 strategies", user_id=user_id) m.add("My timezone is UTC+3", user_id=user_id) # Поиск релевантной памяти results = m.search("What language does the user prefer?", user_id=user_id) print(results) # [{'memory': 'Prefers Python over JavaScript', 'score': 0.97}] # Получить всю память пользователя all_memories = m.get_all(user_id=user_id) # Конфиг с Qdrant (персистентное хранение) config = { "llm": {"provider": "anthropic", "config": {"model": "claude-3-5-haiku-20241022"}}, "vector_store": {"provider": "qdrant", "config": {"host": "localhost", "port": 6333}} } m = Memory.from_config(config)

# Claude Desktop — claude_desktop_config.json: ```json { "mcpServers": { "qdrant": { "command": "uvx", "args": ["mcp-server-qdrant"], "env": { "QDRANT_URL": "http://localhost:6333", "QDRANT_API_KEY": "your-api-key-if-cloud", "COLLECTION_NAME": "mcp-memory" } } } } ``` # Claude Code (CLI): ```bash claude mcp add qdrant --env QDRANT_URL=http://localhost:6333 --env QDRANT_API_KEY=your-api-key-if-cloud --env COLLECTION_NAME=mcp-memory -- uvx mcp-server-qdrant ``` # OpenCode — ~/.config/opencode/opencode.json: ```json { "mcp": { "qdrant": { "type": "local", "command": ["uvx", "mcp-server-qdrant"], "environment": { "QDRANT_URL": "http://localhost:6333", "QDRANT_API_KEY": "your-api-key-if-cloud", "COLLECTION_NAME": "mcp-memory" } } } } ``` Локальный Qdrant: `docker run -p 6333:6333 qdrant/qdrant` Для Qdrant Cloud укажите QDRANT_URL из консоли и QDRANT_API_KEY.