MCP ZepAi Server

MCP ZepAi Server is an MCP server that integrates AI assistants with Zep, a dedicated long-term memory store designed for AI applications. It connects Model Context Protocol clients directly to Zep services, allowing language models to store, persist, and recall historical interactions, conversational memory, and context over extended periods. Developers building conversational agents, personalized assistants, and complex retrieval-augmented generation pipelines use this integration to maintain user context without relying solely on transient context windows. By interfacing with the Zep API via a local Python process managed by uv, the server enables tools to automatically query stored facts, manage session history, and surface relevant episodic knowledge during ongoing conversations. This setup helps AI agents deliver coherent responses tailored to returning users, tracks contextual details across multiple distinct interaction sessions, and provides a structured data layer for managing long-term memory in production AI environments.

Category: AI Memory & Context

Tags: embeddings, long-term memory, persistence, rag, zep

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How to install and configure MCP ZepAi Server

1. Clone or download the repository from https://github.com/annamariakv/Mcp-ZepAi to a local directory on your machine. 2. Ensure uv and Python are installed on your system. 3. Obtain an API key from Zep. 4. Open your MCP client configuration file (such as claude_desktop_config.json) and add the server definition: json { "mcpServers": { "zepai": { "command": "uv", "args": [ "run", "--directory", "<path_to_the_mcp_directory>", "python", "-m", "mcp_zepai" ], "cwd": "<path_to_the_mcp_directory>", "env": { "ZEP_API_KEY": "<your_api_key_here>" } } } } 5. Replace <path_to_the_mcp_directory> with your actual repository path and <your_api_key_here> with your valid ZEP_API_KEY. 6. Restart your MCP client.

What you can do with MCP ZepAi Server

• Persisting user preferences and key details across distinct chat sessions to provide consistent, contextual conversations over time. - Enhancing retrieval-augmented generation pipelines by fetching relevant episodic memory and historical context directly through Zep. - Managing long-term memory records for autonomous customer support agents needing past interaction data. - Structuring multi-session user dialogue history to prevent context window overflow while maintaining conversational accuracy.

Key facts

• https://github.com/annamariakv/Mcp-ZepAi
• AI Memory & Context, Databases & Data Stores
• embeddings, long-term memory, persistence, rag, zep

Part of MCP Servers

How do I install MCP ZepAi Server?

To install the server, clone the repository locally and ensure you have uv and Python installed. Then, configure your MCP client by adding the server block using the uv command to run the python -m mcp_zepai module, passing your local directory path and your ZEP_API_KEY environment variable.

What can MCP ZepAi Server do?

MCP ZepAi Server connects your Model Context Protocol client to Zep, enabling long-term memory storage and retrieval. It allows language models to persist dialogue history, extract relevant past context, and access persistent conversational memory across multiple sessions.

What prerequisites are needed for MCP ZepAi Server?

You need a local Python environment, the uv package runner installed, the MCP ZepAi server repository files, and a valid ZEP_API_KEY from Zep set as an environment variable in your configuration.

Which MCP clients work with MCP ZepAi Server?

Any client that supports the Model Context Protocol over standard input and output can use this server, including Claude Desktop, Cursor, and other custom developer environments configured with local command-based MCP servers.