What runtime dependencies does MemCP require?
MemCP requires Python 3.10+, a Neo4j database (version 5.26 or later), and an OpenAI API key. An Anthropic API key is optional for using Claude models.
MemCP - Memory Context Protocol for AI Agents MCP Server
@evanmschultz
Basic information
About MemCP - Memory Context Protocol for AI Agents MCP Server
A memory MCP for IDE AI agent context support based on the mcp_server example by Zep AI
MemCP - Memory Context Protocol for AI Agents MCP Server config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"memcp": {
"command": "uv",
"args": [
"sync"
]
}
}
}MemCP - Memory Context Protocol for AI Agents MCP Server tools
8Add an episode to the knowledge graph (text, JSON, messages)
Search for entity nodes in the graph
Search for relationships between entities
Delete a relationship between entities
Delete an episode from the knowledge graph
Get details about a specific relationship
Retrieve recent episodes
Reset the knowledge graph (use with caution)
Overview
What is MemCP?
MemCP is an extensible memory MCP server for AI coding agents that builds, maintains, and queries a temporal knowledge graph of facts, entities, and relationships. It modularizes and extends the Zep AI Graphiti MCP server and works with any MCP-compatible IDE or LLM client app through local SSE or stdio connections.
How to use MemCP?
Install prerequisites (Python 3.10+, Neo4j 5.26+, OpenAI API key), clone the repository, install using uv sync or pip install memcp, copy and edit the .env file, then run memcp to start the server. For Cursor IDE, add a configuration block with "transport": "sse" and "url": "http://localhost:8000/sse" to the MCP servers settings.
Key features of MemCP
- Temporal knowledge graph that evolves with new information
- Automatic entity and relationship extraction
- Works with any MCP-compatible client
- Flexible configuration via env vars, TOML, or CLI
- Persists knowledge in Neo4j across sessions
- Supports both SSE and stdio transports
Use cases of MemCP
- AI coding agents that recall user preferences across sessions
- Maintaining a running context of project requirements and procedures
- Building a searchable memory of past episodes for agent decision-making
- Enabling multi-session continuity for LLM-based development tools
Frequently asked questions
How does MemCP store data?
MemCP stores knowledge in a Neo4j database for persistence across sessions. You can run Neo4j with Docker if you prefer not to install it locally.
What transports does MemCP support?
MemCP supports both SSE (HTTP) and stdio transports for integration with different MCP-compatible clients.
Are there any known limitations?
MemCP makes a large number of API calls, which incur significant costs. It has been tested primarily with the Cursor IDE, and use with other clients may require additional configuration.
How is MemCP configured?
MemCP can be configured via command-line arguments (highest priority), a `config.toml` file, or default values (lowest priority). Environment variables from a `.env` file are automatically loaded on startup.
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