A token-lean MCP memory server for AI coding agents: a knowledge graph your agent can search without reading the whole thing. Drop-in replacement for the reference memory server.
View on GitHub ↗Homepage ↗Long-term memory for AI coding agents that doesn't burn the context window.
Quick start · What the agent sees · Why it stays lean · Tools · Agent protocol · Settings · Contributing
memory-lean is an MCP server that gives Claude Code, Claude Desktop, Cursor and any other MCP client a knowledge graph that persists between sessions. It is a drop-in replacement for the reference memory server (@modelcontextprotocol/server-memory): same memory.jsonl file, same nine tool names, plus two extras. What changes is how much the tools send back.
Call, on a real graph of 237 entities Reference server memory-lean ------:---:---: readgraph ~77,800 tokens ~2,000 tokens 40× smaller searchnodes (average of 5 common words) ~31,000 tokens ~770 tokens 40× smaller
One file. No dependencies. Nothing to build.
Claude Code: install the plugin. It adds the server, a skill with the agent instructions, and a short reminder at the start of each session.
Any other agent: add the server to the agent's MCP config, then give the agent the instructions.
The config file and its exact shape differ per agent.
Copy-paste setup for each agent is in docs/SETUP.md:
Agent Server config Instructions --------- Claude Code plugin included in the plugin Xiaomi MiMo Code mimocode.jsonc skill or AGENTS.md opencode opencode.json AGENTS.md OpenAI Codex CLI codex mcp add AGENTS.md Cursor .cursor/mcp.json AGENTS.md or a rule VS Code (Copilot) .vscode/mcp.json copilot-instructions.md Gemini CLI settings.json GEMINI.md Windsurf mcpconfig.json a rule Cline clinemcpsettings.json .clinerules/ Zed settings.json AGENTS.md
From the project README.
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