What are the runtime dependencies?

Python 3.8 or higher, pip, and a virtual environment (recommended). The startup script installs all required Python dependencies automatically.

Where are memories and data stored?

Memories and tag indexes are stored as JSON files in the `data` directory (`memories.json` and `tags.json`). Logs are stored in the `logs` directory.

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TxtAI Assistant MCP Server

Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API

Local serverstdioPython

What is the TxtAI Assistant MCP Server?

Model Context Protocol (MCP) server implementation for semantic vector search and memory management using TxtAI. This server provides a robust API for storing

What you get

We extend txtai's capabilities by integrating it with the Model Context Protocol (MCP), enabling AI assistants like Claude and Cline to leverage its powerful semantic search capabilities. Special thanks to the txtai team for creating such a powerful and flexible tool.

🔍 Semantic search across stored memories
💾 Persistent storage with file-based backend
🏷️ Tag-based memory organization and retrieval
📊 Memory statistics and health monitoring
🔄 Automatic data persistence
📝 Comprehensive logging

Configuration and credentials

Python 3.8 or higher - pip (Python package installer) - virtualenv (recommended)

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Maintainer setup documentation.

Configuration

Python 3.8 or higher - pip (Python package installer) - virtualenv (recommended)

Frequently asked questions

It integrates the txtai semantic search engine with the Model Context Protocol, allowing AI assistants like Claude and Cline to directly store and search memories using MCP tools while benefiting from txtai’s neural search, zero‑shot classification, and multi‑language support.