claude-mem-lite

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claude-mem-lite

claude-mem-lite is a persistent memory (also called long-term memory or cross-session context) system for Claude Code — Anthropic's CLI coding agent. It runs as an MCP server plus a set of Claude Code hooks, automatically capturing coding observations, decisions, and bug fixes during sessions, then providing hybrid full-text + semantic search to recall them later.

Compared to general-purpose LLM memory frameworks like mem0 or the MCP reference memory server, claude-mem-lite is purpose-built for Claude Code's hook lifecycle: episode batching cuts LLM calls 7–10× vs the original claude-mem (an estimated ~600× lower total cost — see the cost model below; this is an architecture estimate, not a measured benchmark), while the hybrid FTS5 + TF-IDF retriever benchmarks at 0.88 Recall@10 / 0.96 Precision@10.

中文简介：claude-mem-lite 是 Claude Code 的轻量级持久化记忆 / 长期记忆 / 跨会话上下文插件，基于 MCP 协议 + 钩子机制，自动捕获编码会话中的决策、修复和上下文，并通过 FTS5 + TF-IDF 混合检索召回。详见 中文 README。

Zero external services. Single SQLite database. Minimal overhead.

Why claude-mem-lite?

A ground-up redesign of claude-mem, replacing its heavyweight architecture with a smarter, leaner approach.

Architecture comparison

	claude-mem (original)	claude-mem-lite
LLM calls	Every tool use triggers a Sonnet call	Only on episode flush (5-10 ops batched)
LLM input	Raw tool_input + tool_output JSON	Pre-processed action summaries
Conversation	Multi-turn, accumulates full history	Stateless single-turn extraction
Noise filtering	LLM decides via "WHEN TO SKIP" prompt	Deterministic code-level Tier 1 filter
Runtime	Long-running worker process (1.8MB .cjs)	On-demand spawn, exits immediately
Dependencies	Bun + Python/uv + Chroma vector DB	Node.js only (3 npm packages)
Source size	~2.3MB compiled bundles	~50KB readable source
Data directory	~/.claude-mem/	~/.claude-mem-lite/ (hidden, auto-migrates)

Token & cost efficiency

For a typical 50-tool-call session (illustrative cost model — the ratios below are architecture estimates derived from batch size, token counts, and model pricing, not a measured end-to-end benchmark):

	claude-mem	claude-mem-lite	Ratio (estimated)
LLM calls	~50 (every tool use)	~5-8 (per episode)	~7-10x fewer
Tokens per call	1,000-5,000 (raw JSON + history)	200-500 (summaries only)	~5-10x smaller
Total tokens	~100K-250K	~1K-4K	~50-100x less
Model cost	Sonnet ($3/$15 per M)	Haiku ($0.25/$1.25 per M)	~12x cheaper
Combined savings			~600x lower cost (estimated)

Quality comparison

Dimension	Winner	Why
Classification accuracy	Tie	Both produce correct type/title/narrative
Noise filtering	lite	Code-level filtering is deterministic; LLM "WHEN TO SKIP" is unreliable
Observation coherence	lite	Episode batching groups related edits into one coherent observation
Code-level detail	original	Sees full diffs, but rarely useful for memory search
Search recall	Tie	Users search semantic concepts ("auth bug"), not code lines
Hook latency	lite	Async background workers; original blocks 2-5s per hook

Design philosophy

The original sends everything to the LLM and hopes it filters well. claude-mem-lite filters first with code, then sends only what matters to a smaller model. This is not a downgrade; it's a smarter architecture that produces equivalent search quality at a fraction of the cost.

Comparison: memory systems for AI coding agents

How claude-mem-lite differs from the major neighbors in the LLM-memory space (verified May 2026):