LycheeMem vs mcp-memory-keeper
LycheeMem: LycheeMem is a compact memory framework for LLM agents that organizes memory into working, semantic, and procedural stores. It features a four-stage pipeline (WMManager, SearchCoordinator, SynthesizerAgent, ReasoningAgent) with a background ConsolidatorAgent, and supports multi-channel retrieval (FTS, vector, tag, temporal). It integrates via API, OpenClaw plugin, and MCP protocol.; mcp-memory-keeper: MCP Memory Keeper is an MCP server that provides persistent context management for Claude AI coding assistants, preventing context loss during compaction. It offers features like session management, context saving/retrieval, checkpoints, search, and git integration. The server works across macOS, Linux, and Windows and can be installed via npx.
TL;DR
- You are building your own agent and want a memory framework with working, semantic and procedural stores.
- You prefer light storage: SQLite with FTS5 plus a LanceDB vector index, with no Neo4j.
- You want typed, action-aware records, including a tool_affordance type for what a tool can do and when to use it.
- You want something stable today: the project has been open source only since March 2026, so APIs may change.
- You want zero extra model cost: memory extraction and retrieval use LLM and embedding calls.
- You expect the action-aware and reinforcement-learning parts to be finished: the README describes them as a direction.
Long coding sessions with Claude Code
Side-by-Side Comparison
LycheeMemmcp-memory-keeperFeatures
LycheeMemmcp-memory-keeperUse Cases
LycheeMemmcp-memory-keeperFAQ
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