Frequently Asked Questions about MCP Memory Server

Q: What is MCP Memory Server? A: MCP (Model Context Protocol) Memory Server is a solution that provides memory capabilities for data-rich AI applications. It uses an efficient knowledge graph (HippoRAG) to manage and retrieve information, enhancing AI Agent performance.

Q: How does MCP Memory Server work? A: The server manages memory on a session basis. It uses the HippoRAG knowledge graph for efficient storage and retrieval. It also supports multiple transport protocols and provides automatic resource management through TTL-based cleanup.

Q: What are the key features of MCP Memory Server? A: Key features include session-based memory management, an efficient knowledge graph, support for multiple transport protocols (stdio and SSE), enhanced search capabilities, and automatic resource management.

Q: What is HippoRAG? A: HippoRAG is an efficient knowledge graph used internally by the MCP Memory Server for memory management. It enables the server to organize, store, and retrieve information in a structured and optimized manner.

Q: How do I install MCP Memory Server? A: You can install it from PyPI using pip: pip install mcp-mem hipporag. Alternatively, you can install it from source by cloning the repository and running pip install -e ..

Q: What is UBOS? A: UBOS is a full-stack AI Agent Development Platform focused on bringing AI Agents to every business department. It helps you orchestrate AI Agents, connect them with your enterprise data, and build custom AI Agents with your LLM model and Multi-Agent Systems.

Q: How does the MCP Memory Server integrate with UBOS? A: The MCP Memory Server can be easily integrated into UBOS by configuring the server and connecting it to the UBOS platform. This allows AI Agents on UBOS to utilize the memory capabilities of the MCP server.

Q: What are some use cases for MCP Memory Server? A: Use cases include AI-powered customer service, financial analysis and trading, healthcare diagnosis and treatment, legal research and case management, and content creation and management.

Q: How do I configure the MCP Memory Server? A: You can configure the server using environment variables or a configuration file. Set the necessary parameters such as the LLM name, embedding model name, and API keys.

Q: How do I run the MCP Memory Server? A: You can run the MCP Memory Server using the command line: mcp-mem. Use mcp-mem --sse for SSE transport, and mcp-mem --sse --host 127.0.0.1 --port 3001 to specify host and port.

Q: How does automatic resource management work? A: The server uses TTL-based cleanup for sessions and instances. Session TTL removes session directories after a period of inactivity, and instance TTL offloads HippoRAG instances from memory after a set amount of idle time.

Q: Can I contribute to the MCP Memory Server project? A: Yes, contributions are welcome! You can submit a Pull Request with your changes.