LycheeMem vs locus
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.; locus: Locus provides persistent project-aware memory for AI coding tools, using three memory layers: structural, semantic, and episodic. It works with any MCP-compatible client and offers 14 tools and 3 resources for exploring, searching, and managing memory. Carbon Copy enables passive conversation capture for later recall.
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.
Maintain project context across AI coding sessions to avoid losing architectural decisions and file structure awareness
Side-by-Side Comparison
LycheeMemlocusFeatures
LycheeMemlocusUse Cases
LycheeMemlocusFAQ
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