English | 中文
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):
