claude-mem-lite

Name: claude-mem-lite
Author: sdsrss

50stars3forks

How to install claude-mem-lite?

sdsrss/claude-mem-lite

$git clone https://github.com/sdsrss/claude-mem-lite

Installs into the current project.

Summary

Persistent long-term memory for Claude Code via MCP — captures coding decisions, bugfixes, and context across sessions. Hybrid FTS5 + TF-IDF search with episode batching. Single SQLite DB, no external services. Alternative to claude-mem with 600x lower cost.

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README.md

1	[English](README.md) \| [中文](README.zh-CN.md)
2
3	# claude-mem-lite
4
5	`claude-mem-lite` is a persistent memory (also called long-term memory or cross-session context) system for [Claude Code](https://docs.anthropic.com/en/docs/claude-code) — Anthropic's CLI coding agent. It runs as an [MCP](https://modelcontextprotocol.io/) 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.
6
7	Compared to general-purpose LLM memory frameworks like [`mem0`](https://github.com/mem0ai/mem0) or the MCP reference [`memory`](https://github.com/modelcontextprotocol/servers/tree/main/src/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](https://github.com/thedotmack/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.
8
9	> 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](README.zh-CN.md)。
10
11	Zero external services. Single SQLite database. Minimal overhead.
12
13	## Why claude-mem-lite?
14
15	A ground-up redesign of [claude-mem](https://github.com/thedotmack/claude-mem), replacing its heavyweight architecture with a smarter, leaner approach.
16
17	### Architecture comparison
18
19	\| \| claude-mem (original) \| claude-mem-lite \|
20	\|---\|---\|---\|
21	\| LLM calls \| Every tool use triggers a Sonnet call \| Only on episode flush (5-10 ops batched) \|
22	\| LLM input \| Raw `tool_input` + `tool_output` JSON \| Pre-processed action summaries \|
23	\| Conversation \| Multi-turn, accumulates full history \| Stateless single-turn extraction \|
24	\| Noise filtering \| LLM decides via "WHEN TO SKIP" prompt \| Deterministic code-level Tier 1 filter \|
25	\| Runtime \| Long-running worker process (1.8MB .cjs) \| On-demand spawn, exits immediately \|
26	\| Dependencies \| Bun + Python/uv + Chroma vector DB \| Node.js only (3 npm packages) \|
27	\| Source size \| ~2.3MB compiled bundles \| ~50KB readable source \|
28	\| Data directory \| `~/.claude-mem/` \| `~/.claude-mem-lite/` (hidden, auto-migrates) \|
29
30	### Token & cost efficiency
31
32	For a typical 50-tool-call session (illustrative cost model — the ratios below are
33	architecture estimates derived from batch size, token counts, and model pricing, not
34	a measured end-to-end benchmark):
35
36	\| \| claude-mem \| claude-mem-lite \| Ratio (estimated) \|
37	\|---\|---\|---\|---\|
38	\| LLM calls \| ~50 (every tool use) \| ~5-8 (per episode) \| ~7-10x fewer \|
39	\| Tokens per call \| 1,000-5,000 (raw JSON + history) \| 200-500 (summaries only) \| ~5-10x smaller \|
40	\| Total tokens \| ~100K-250K \| ~1K-4K \| ~50-100x less \|
41	\| Model cost \| Sonnet ($3/$15 per M) \| Haiku ($0.25/$1.25 per M) \| ~12x cheaper \|
42	\| Combined savings \| \| \| ~600x lower cost (estimated) \|
43
44	### Quality comparison
45
46	\| Dimension \| Winner \| Why \|
47	\|---\|---\|---\|
48	\| Classification accuracy \| Tie \| Both produce correct type/title/narrative \|
49	\| Noise filtering \| lite \| Code-level filtering is deterministic; LLM "WHEN TO SKIP" is unreliable \|
50	\| Observation coherence \| lite \| Episode batching groups related edits into one coherent observation \|
51	\| Code-level detail \| original \| Sees full diffs, but rarely useful for memory search \|
52	\| Search recall \| Tie \| Users search semantic concepts ("auth bug"), not code lines \|
53	\| Hook latency \| lite \| Async background workers; original blocks 2-5s per hook \|
54
55	### Design philosophy
56
57	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.
58
59	### Comparison: memory systems for AI coding agents
60
61	How claude-mem-lite differs from the major neighbors in the LLM-memory space (verified May 2026):
62
63	\| \| claude-mem-lite \| [`mem0`](https://github.com/mem0ai/mem0) \| MCP reference [`memory`](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) \| [claude-mem](https://github.com/thedotmack/claude-mem) (original) \|
64	\|---\|---\|---\|---\|---\|
65	\| Target client \| Claude Code only \| Any LLM app via SDK \| Any MCP client \| Claude Code only \|
66	\| Capture model \| Auto via hooks \| Manual `memory.add()` \| Manual tool calls (`create_entities`, `add_observations`) \| Auto via hooks \|
67	\| Code-aware retrieval \| FTS5 + 100+ synonym pairs (incl. CJK↔EN) \| General-purpose \| Generic graph nodes \| Code-aware \|
68	\| Search \| Hybrid: FTS5 BM25 + TF-IDF cosine via RRF \| Hybrid: semantic + BM25 + entity linking \| Knowledge-graph traversal \| FTS5 + Chroma vector \|
69	\| Storage \| Single local SQLite \| Pluggable; Qdra

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claude-mem-lite

50stars3forks

View on GitHub

How to install claude-mem-lite?

sdsrss/claude-mem-lite

$git clone https://github.com/sdsrss/claude-mem-lite

Installs into the current project.

Summary

Files · 1

View on GitHub

README.md

1	[English](README.md) \| [中文](README.zh-CN.md)
2
3	# claude-mem-lite
4
5	`claude-mem-lite` is a persistent memory (also called long-term memory or cross-session context) system for [Claude Code](https://docs.anthropic.com/en/docs/claude-code) — Anthropic's CLI coding agent. It runs as an [MCP](https://modelcontextprotocol.io/) 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.
6
7	Compared to general-purpose LLM memory frameworks like [`mem0`](https://github.com/mem0ai/mem0) or the MCP reference [`memory`](https://github.com/modelcontextprotocol/servers/tree/main/src/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](https://github.com/thedotmack/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.
8
9	> 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](README.zh-CN.md)。
10
11	Zero external services. Single SQLite database. Minimal overhead.
12
13	## Why claude-mem-lite?
14
15	A ground-up redesign of [claude-mem](https://github.com/thedotmack/claude-mem), replacing its heavyweight architecture with a smarter, leaner approach.
16
17	### Architecture comparison
18
19	\| \| claude-mem (original) \| claude-mem-lite \|
20	\|---\|---\|---\|
21	\| LLM calls \| Every tool use triggers a Sonnet call \| Only on episode flush (5-10 ops batched) \|
22	\| LLM input \| Raw `tool_input` + `tool_output` JSON \| Pre-processed action summaries \|
23	\| Conversation \| Multi-turn, accumulates full history \| Stateless single-turn extraction \|
24	\| Noise filtering \| LLM decides via "WHEN TO SKIP" prompt \| Deterministic code-level Tier 1 filter \|
25	\| Runtime \| Long-running worker process (1.8MB .cjs) \| On-demand spawn, exits immediately \|
26	\| Dependencies \| Bun + Python/uv + Chroma vector DB \| Node.js only (3 npm packages) \|
27	\| Source size \| ~2.3MB compiled bundles \| ~50KB readable source \|
28	\| Data directory \| `~/.claude-mem/` \| `~/.claude-mem-lite/` (hidden, auto-migrates) \|
29
30	### Token & cost efficiency
31
32	For a typical 50-tool-call session (illustrative cost model — the ratios below are
33	architecture estimates derived from batch size, token counts, and model pricing, not
34	a measured end-to-end benchmark):
35
36	\| \| claude-mem \| claude-mem-lite \| Ratio (estimated) \|
37	\|---\|---\|---\|---\|
38	\| LLM calls \| ~50 (every tool use) \| ~5-8 (per episode) \| ~7-10x fewer \|
39	\| Tokens per call \| 1,000-5,000 (raw JSON + history) \| 200-500 (summaries only) \| ~5-10x smaller \|
40	\| Total tokens \| ~100K-250K \| ~1K-4K \| ~50-100x less \|
41	\| Model cost \| Sonnet ($3/$15 per M) \| Haiku ($0.25/$1.25 per M) \| ~12x cheaper \|
42	\| Combined savings \| \| \| ~600x lower cost (estimated) \|
43
44	### Quality comparison
45
46	\| Dimension \| Winner \| Why \|
47	\|---\|---\|---\|
48	\| Classification accuracy \| Tie \| Both produce correct type/title/narrative \|
49	\| Noise filtering \| lite \| Code-level filtering is deterministic; LLM "WHEN TO SKIP" is unreliable \|
50	\| Observation coherence \| lite \| Episode batching groups related edits into one coherent observation \|
51	\| Code-level detail \| original \| Sees full diffs, but rarely useful for memory search \|
52	\| Search recall \| Tie \| Users search semantic concepts ("auth bug"), not code lines \|
53	\| Hook latency \| lite \| Async background workers; original blocks 2-5s per hook \|
54
55	### Design philosophy
56
57	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.
58
59	### Comparison: memory systems for AI coding agents
60
61	How claude-mem-lite differs from the major neighbors in the LLM-memory space (verified May 2026):
62
63	\| \| claude-mem-lite \| [`mem0`](https://github.com/mem0ai/mem0) \| MCP reference [`memory`](https://github.com/modelcontextprotocol/servers/tree/main/src/memory) \| [claude-mem](https://github.com/thedotmack/claude-mem) (original) \|
64	\|---\|---\|---\|---\|---\|
65	\| Target client \| Claude Code only \| Any LLM app via SDK \| Any MCP client \| Claude Code only \|
66	\| Capture model \| Auto via hooks \| Manual `memory.add()` \| Manual tool calls (`create_entities`, `add_observations`) \| Auto via hooks \|
67	\| Code-aware retrieval \| FTS5 + 100+ synonym pairs (incl. CJK↔EN) \| General-purpose \| Generic graph nodes \| Code-aware \|
68	\| Search \| Hybrid: FTS5 BM25 + TF-IDF cosine via RRF \| Hybrid: semantic + BM25 + entity linking \| Knowledge-graph traversal \| FTS5 + Chroma vector \|
69	\| Storage \| Single local SQLite \| Pluggable; Qdra

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