能力标签
claude-mem-lite MCP工具
🔌
MCP工具

claude-mem-lite MCP工具

基于 JavaScript · 让 AI 助手直接操作你的系统与工具
英文名:claude-mem-lite
⭐ 43 Stars 🍴 2 Forks 💻 JavaScript 📄 MIT 🏷 AI 7.8分
7.8AI 综合评分
ClaudeMCP工具记忆系统FTS5搜索持久化
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,claude-mem-lite MCP工具 获评「推荐使用」。这款MCP工具在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.8 分,适合有一定技术背景的用户使用。

📚 深度解析

claude-mem-lite MCP工具 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 claude-mem-lite MCP工具,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。claude-mem-lite MCP工具 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 claude-mem-lite MCP工具 评为 AI 评分 7.8 分,属于同类工具中的优质选择。

📋 工具概览

为Claude Code设计的开源MCP工具,提供轻量级持久化记忆能力。采用FTS5全文搜索引擎,支持记忆批量管理,帮助AI助手跨会话保留上下文信息,适合需要长期记忆管理的开发者和Claude集成应用场景。

claude-mem-lite MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

GitHub Stars
⭐ 43
开发语言
JavaScript
支持平台
Windows / macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
7.8 分
工具类型
MCP工具
Forks
2

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

为Claude Code设计的开源MCP工具,提供轻量级持久化记忆能力。采用FTS5全文搜索引擎,支持记忆批量管理,帮助AI助手跨会话保留上下文信息,适合需要长期记忆管理的开发者和Claude集成应用场景。

claude-mem-lite MCP工具 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/sdsrss/claude-mem-lite

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "claude-mem-lite-mcp--": {
      "command": "npx",
      "args": ["-y", "claude-mem-lite"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 claude-mem-lite MCP工具 执行以下任务...
Claude: [自动调用 claude-mem-lite MCP工具 MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入:"列出所有可用的 MCP 工具"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "claude-mem-lite_mcp__": {
      "command": "npx",
      "args": ["-y", "claude-mem-lite"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 95/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

English | 中文

Features

  • Automatic capture -- Hooks into Claude Code lifecycle (PostToolUse, SessionStart, Stop, UserPromptSubmit) to record observations without manual effort
  • Hybrid search -- FTS5 BM25 + TF-IDF vector cosine similarity, merged via Reciprocal Rank Fusion (RRF). FTS5 handles keyword matching; 512-dim TF-IDF vectors capture semantic similarity for recall beyond exact terms
  • Timeline browsing -- Navigate observations chronologically with anchor-based context windows
  • Episode batching -- Groups related file operations into coherent episodes before LLM encoding
  • Error-triggered recall -- Automatically searches memory when Bash errors occur, surfacing relevant past fixes
  • Proactive file history -- When editing a file, automatically shows relevant past observations for that file
  • Session summaries -- LLM-generated summaries at session end (via background workers using claude -p)
  • Project-scoped context -- Injects recent memory into CLAUDE.md and session startup for immediate context
  • Observation types -- Categorized as decision, bugfix, feature, refactor, discovery, or change
  • Importance grading -- LLM assigns 1-3 importance levels (routine / notable / critical) to each observation
  • Observation relations -- Bidirectional links between related observations based on file overlap
  • User prompt capture -- Records user prompts via UserPromptSubmit hook for intent tracking
  • Read file tracking -- Tracks files read during sessions for richer episode context
  • Zero data loss -- If LLM fails, observations are saved with degraded (inferred) metadata instead of being discarded
  • Two-tier dedup -- Jaccard similarity (5-minute window) + MinHash signatures (7-day cross-session window) prevent duplicates
  • Synonym expansion -- Abbreviations like K8s, DB, auth automatically expand to full forms in FTS5 search (100+ pairs including CJK↔EN cross-language mappings)
  • CJK synonym extraction -- Unsegmented Chinese text is scanned for known vocabulary words (数据库→database, 搜索→search, etc.) enabling cross-language memory recall
  • Stop-word filtering -- English stop words filtered from both TF-IDF vocabulary (reclaiming ~18% of vector dimensions) and FTS queries (preventing false negatives from noise terms like "how", "the", "does")
  • Persisted vocabulary -- TF-IDF vocabulary persisted to vocab_state table, preventing vector staleness when document frequencies shift. Vectors stay valid until explicit rebuild
  • Pseudo-relevance feedback (PRF) -- Top results seed expansion queries for broader recall
  • Concept co-occurrence -- Shared concepts across observations expand search to related topics
  • Context-aware re-ranking -- Active file overlap boosts relevance (exact match + directory-level half-weight)
  • Superseded detection -- Marks older observations as outdated when newer ones cover the same files with higher importance
  • Adaptive time windows -- Session startup recall uses velocity-based time windows (high/medium/low activity tiers)
  • Token-budgeted context -- Greedy knapsack algorithm selects session-start context within a 2,000-token budget, prioritizing by recency and importance
  • Observation compression -- Old low-value observations can be compressed into weekly summaries to reduce noise
  • Secret scrubbing -- Automatic redaction of API keys, tokens, PEM blocks, connection strings, and 15+ credential patterns
  • Atomic writes -- All file writes (episodes, CLAUDE.md) use write-to-tmp + rename to prevent corruption on crash
  • Robust locking -- PID-aware lock files with automatic stale/orphan cleanup (>30s timeout or dead PID)
  • Stale session cleanup -- Sessions active for >24h are automatically marked as abandoned on next start
  • Resource registry -- Indexes installed skills and agents with FTS5 search, composite scoring, and invocation tracking; searchable via mem_registry MCP tool
  • Unified resource discovery -- Shared filesystem traversal layer (resource-discovery.mjs) used by both runtime scanner and offline indexer, supporting flat directories, plugin nesting, and loose .md files
  • Domain synonym expansion -- Registry search queries expand to domain synonyms (e.g., "fix" → debug, bugfix, troubleshoot, diagnose, repair)
  • Multi-provider LLM mode -- Provider priority ANTHROPIC_API_KEY (direct Anthropic API) → OPENROUTER_API_KEY (OpenRouter, OpenAI-compatible — point it at any model via OPENROUTER_MODEL) → claude -p CLI fallback when no key is set
  • Lesson-learned indexing -- lesson_learned field indexed in FTS5 with weight 8, making past debugging insights directly searchable
  • Cross-source normalization -- mem_search normalizes scores across observations, sessions, and prompts before merging, preventing any source from dominating results
  • Exponential recency decay -- Type-differentiated half-lives (decisions: 90d, discoveries: 60d, bugfixes: 14d, changes: 7d) consistently applied in all ranking paths
  • Prompt-time memory injection -- UserPromptSubmit hook automatically searches and injects relevant past observations with recency and importance weighting
  • Smart skill invocation -- Auto-loaded and searched managed skills/agents include portable ~ paths with Read() guidance; native plugin skills recommend Skill("full:name"); prevents Skill() misuse for managed resources that aren't registered with Claude Code's native handler
  • Dual injection dedup -- user-prompt-search.js and handleUserPrompt coordinate via temp file to prevent duplicate memory injection
  • Plugin cache hook self-heal -- Claude Code runtime reads plugin hooks from ~/.claude/plugins/cache/<mp>/<plugin>/<ver>/hooks/hooks.json, not from the marketplace source. When install.mjs-managed settings.json hooks coexist with a stale cache hooks.json (e.g. from a previous marketplace install or a plugin auto-update), the runtime registers hooks twice → every session start / user prompt fires twice. install.mjs and hook-update.mjs now clear cache hooks.json in every version dir, and hook.mjs session-start self-heals on every session (gated by hasInstallManagedHooks so plugin-only users are not affected). install.mjs status reports cache pollution state (since v2.31.1/2.31.2).
  • Result-dedup cooldown -- User-prompt memory injection uses result-overlap detection (>80% ID overlap → skip) instead of time-based cooldown, allowing topic switches within seconds while preventing redundant injections
  • OR query fallback -- When AND-joined FTS5 queries return zero results, automatically relaxes to OR-joined queries for broader recall (applied in both user-prompt-search and hook-memory paths)
  • Configurable LLM model -- Switch between Haiku (fast/cheap) and Sonnet (deeper analysis) via CLAUDE_MEM_MODEL env var
  • DB auto-recovery -- Detects and cleans corrupted WAL/SHM files on startup; periodic WAL checkpoints prevent unbounded growth
  • Schema auto-migration -- Idempotent ALTER TABLE migrations run on every startup, safely adding new columns and indexes without data loss
  • Exploration bonus -- New resources in the registry get a fair chance in composite ranking; zombie resources (high recommend, zero adopt) are penalized in scoring
  • LLM concurrency control -- File-based semaphore limits background workers to 2 concurrent LLM calls, preventing resource contention
  • stdin overflow protection -- Hook input truncated at 256KB with regex-based action salvage for oversized tool outputs
  • Cross-session handoff -- Captures session state (request, completed work, next steps, key files) on /clear or /exit, then injects context when the next session detects continuation intent via explicit keywords or FTS5 term overlap
  • Git-SHA continuation anchor (v2.31.0) -- Handoff rows include git_sha_at_handoff; any handoff matching the current HEAD counts as continuation regardless of TTL. Code state is a stronger continuation signal than wall-clock time
  • Startup dashboard (v2.31.0) -- SessionStart hook aggregates git status + ~/.claude/tasks/*.json + ~/.claude/plans/*.md + most-recent exit handoff + recent event count into a single structured block injected via hookSpecificOutput.additionalContext
  • Activity namespace (v2.31.0) -- Dedicated events table + FTS5 for non-memdir types (bugfix, lesson, bug, discovery, refactor, feature, observation, decision) that don't compete with WHAT_NOT_TO_SAVE semantics on the observations table. CLI: claude-mem-lite activity save|search|recent|show. Slash commands: /lesson, /bug. hook-llm routes non-memdir summary types through persistHaikuSummary so upgrades from observations→events are atomic
  • In-place observation updates -- mem_update tool modifies existing observations atomically (field update + FTS text rebuild + vector re-computation in one transaction), preserving original IDs and references
  • Bulk export -- mem_export tool exports observations as JSON or JSONL, with project/type/date filtering and 1000-row pagination cap with batch guidance
  • FTS integrity management -- mem_fts_check tool verifies FTS5 index health or rebuilds indexes on demand, useful after database recovery or when search results seem wrong
  • Atomic multi-table writes -- saveObservation wraps observations + observation_files + observation_vectors INSERTs in a single db.transaction(), preventing orphaned rows on crash
  • Modular NLP pipeline -- Synonym maps, stop words, scoring constants, and query building extracted into focused modules (synonyms.mjs, stop-words.mjs, scoring-sql.mjs, nlp.mjs) for independent testing and maintenance
  • Porter-aligned PRF -- Pseudo-relevance feedback terms are now stemmed with the same Porter algorithm used by FTS5, ensuring PRF expansion terms match the search index

Requirements

  • Node.js >= 20
  • Claude Code CLI installed and configured (claude command available)
  • SQLite3 support (provided by better-sqlite3, compiled on install)
  • Platform: Linux or macOS (see Platform Support)

Installation

What happens during installation

  1. Install dependencies -- npm install --omit=dev (compiles native better-sqlite3)
  2. Register MCP server -- mem-lite server with 20 tools (9 core exposed via tools/list + 11 hidden-but-callable; see the Usage section for the full table). The pre-v2.78 generic server name mem is renamed to mem-lite for namespace hygiene; the tool names themselves (mem_search, mem_recall, ...) are unchanged.
  3. Configure hooks -- PostToolUse, SessionStart, Stop, UserPromptSubmit lifecycle hooks
  4. Create data directory -- ~/.claude-mem-lite/ (hidden) for database, runtime, and managed resource files
  5. Auto-migrate -- If ~/.claude-mem/ (original claude-mem) or ~/claude-mem-lite/ (pre-v0.5 unhidden) exists, migrates database and runtime files to ~/.claude-mem-lite/, preserving the original untouched
  6. Initialize database -- SQLite with WAL mode, FTS5 indexes created on first server start

Restart Claude Code after installation to activate.

Plugin install:

/plugin install claude-mem-lite # Install / update /plugin uninstall claude-mem-lite # Uninstall

git clone install:

node install.mjs install # Install and configure node install.mjs uninstall # Remove (keep data) node install.mjs uninstall --purge # Remove and delete all data node install.mjs status # Show current status node install.mjs doctor # Diagnose issues node install.mjs cleanup-hooks # Remove only stale claude-mem-lite hooks from settings.json node install.mjs update # Force-check for updates and install them (direct install / npx mode)

npx install:

npx claude-mem-lite # Install / reinstall npx claude-mem-lite uninstall # Remove (keep data) npx claude-mem-lite doctor # Diagnose issues


Notes:
- Plugin mode only reports available updates; it does not self-update plugin files.
  To upgrade an installed plugin to the latest published version, run **inside Claude Code**:
  
/plugin marketplace update sdsrss /plugin install claude-mem-lite@sdsrss ``` (The first command refreshes the local marketplace clone; the second reinstalls from it. Without the first command, /plugin install reuses the stale local clone and you stay on whichever version you originally pulled.) - Direct install / npx mode keeps auto-update enabled and uses staged replacement with rollback on install failure. - If you disabled the plugin but still have old mem hooks in ~/.claude/settings.json, run node install.mjs cleanup-hooks.

Recovery (stuck install / hook errors)

If you see ERR_MODULE_NOT_FOUND on PreToolUse:Read/Edit/Skill hooks, or claude-mem-lite commands crash with import errors, you're likely hit by a partial auto-update — the updater copied new scripts but missed a sibling lib/* file, breaking the hook chain (and the next auto-update that would have healed it).

v2.84.0+ ships a repair subcommand that re-syncs from the latest GitHub release:

claude-mem-lite repair

If repair itself fails (the bin is older than v2.84.0, or the bin is also broken), run this one-liner — it pulls a fresh tarball into a temp dir and runs that tarball's install.mjs, bypassing every file on your disk:

T=$(mktemp -d) && curl -sL https://api.github.com/repos/sdsrss/claude-mem-lite/tarball | tar xz -C "$T" --strip-components=1 && node "$T/install.mjs" install

After it finishes, ~/.claude-mem-lite/ is back in sync with the latest release and claude-mem-lite repair is available for next time.

Uninstall

```bash

Mixed-install residue (read this if you've used multiple install methods)

/plugin uninstall only removes the plugin manifest — it does not touch ~/.claude/settings.json. If you've ever run claude-mem-lite install (npx or git-clone path), hook entries pointing at ~/.claude-mem-lite/hook.mjs were written into your user-global settings, and they keep firing after /plugin uninstall. If ~/.claude-mem-lite/hook.mjs still exists they double-fire alongside the plugin; if you also ran rm -rf ~/.claude-mem-lite/ they error every session.

The safe sequence is: run claude-mem-lite uninstall first (which cleans the settings.json hooks plus the global MCP registration), then /plugin uninstall claude-mem-lite, then optionally rm -rf ~/.claude-mem-lite/.

If you already uninstalled in the wrong order, claude-mem-lite doctor flags orphan hooks under Orphan hooks: with the exact cleanup command.

Usage

Environment Variables

VariableDescriptionDefault
CLAUDE_MEM_DIRCustom data directory. All databases, runtime files, and managed resources are stored here.~/.claude-mem-lite/
CLAUDE_MEM_MODELLLM model for background calls (episode extraction, session summaries). Accepts haiku or sonnet.haiku
ANTHROPIC_API_KEYAnthropic API key. When set, all background LLM calls go directly to the Anthropic Messages API (with prompt caching). Highest priority._(unset → CLI)_
OPENROUTER_API_KEYOpenRouter API key (OpenAI-compatible). Used for background LLM calls when ANTHROPIC_API_KEY is **not** set. If neither key is set, calls fall back to the claude -p CLI._(unset)_
OPENROUTER_MODELOverrides the OpenRouter model slug for **all** background calls (e.g. openai/gpt-4o-mini, qwen/qwen-2.5-72b-instruct). When unset, the CLAUDE_MEM_MODEL tier maps to anthropic/claude-haiku-4.5 (haiku) or anthropic/claude-sonnet-4.5 (sonnet)._(tier default)_
CLAUDE_MEM_DEBUGEnable debug logging (1 to enable)._(disabled)_
MEM_QUIET_HOOKSLow-noise hooks. 1 drops the File Lessons / Key Context sections from SessionStart injection, the lesson suffix from [mem] Related memories, and the WHEN TO USE / Decision rules blocks from MCP server instructions. IDs and the Recent table still surface so mem_get(ids=[…]) remains reachable. Intended for users running the invited-memory adopt path or who otherwise want minimal auto-injection. **Since v2.82.0 this env no longer gates auto-adopt — use MEM_NO_AUTO_ADOPT=1 for that.**_(disabled)_
MEM_NO_AUTO_ADOPTGlobal opt-out for auto-adopt (v2.82.0+). 1 prevents the first-SessionStart auto-write of the invited-memory sentinel across **all** projects. For per-project opt-out use claude-mem-lite adopt --disable instead (writes a durable <memdir>/.mem-no-auto-adopt sentinel that survives marker deletion)._(disabled)_
MEM_NO_ADOPT_HINTSilences the one-line "Invited-memory 未启用:claude-mem-lite adopt…" hint that SessionStart appends when the current project hasn't been adopted. Since v2.82.1 auto-adopt fires on first SessionStart for any install path, so this hint typically surfaces only when you've explicitly opted out (MEM_NO_AUTO_ADOPT=1 or claude-mem-lite adopt --disable)._(disabled)_

How is claude-mem-lite different from mem0 or MCP's reference memory server?

mem0 and the MCP memory server are general-purpose LLM memory frameworks designed for any client. claude-mem-lite is purpose-built for Claude Code's hook lifecycle: it captures episodes (batched tool calls), uses domain-specific synonym expansion for code terms (K8s, DB, 数据库, ...), and surfaces past observations proactively before file edits via the PreToolUse:Edit hook.

What about privacy? Does it call external APIs?

Only the Haiku summarization step calls Anthropic's API (or the local claude -p CLI if no API key is set). All search, storage, and retrieval is local SQLite — no telemetry, no third-party services.

Efficient Search Workflow

1. mem_search(query="auth bug")     -> compact ID index
2. mem_timeline(anchor=12345)       -> surrounding context
3. mem_get(ids=[12345, 12346])      -> full details

Hook Pipeline

SessionStart
  -> Generate session ID (or save handoff snapshot on /clear)
  -> Mark stale sessions (>24h active) as abandoned
  -> Clean orphaned/stale lock files
  -> Query recent observations (24h)
  -> Inject context into CLAUDE.md + stdout

PostToolUse (every tool execution)
  -> Bash pre-filter skips noise in ~5ms (Read paths tracked to reads file)
  -> Detect Bash significance (errors, tests, builds, git, deploys)
  -> Accumulate into episode buffer
  -> Proactive file history: show past observations for edited files
  -> Flush when: buffer full (10 entries) | 5min gap | context change
  -> Collect Read file paths into episode on flush
  -> Spawn LLM episode worker for significant episodes
  -> Error-triggered recall: search memory for related past fixes

UserPromptSubmit (two parallel paths)
  -> [user-prompt-search.js] Auto-search memory via FTS5 + active file context
  -> [user-prompt-search.js] Inject relevant past observations with recency/importance weighting
  -> [user-prompt-search.js] Write injected IDs to temp file for dedup
  -> [user-prompt-search.js] L1 skill auto-load: match managed skill names in prompt
     -> Load content with portable ~ path + Read() guidance
     -> source="managed-skill|managed-agent", path="~/.claude-mem-lite/managed/..."
  -> [hook.mjs handleUserPrompt] Capture user prompt text to user_prompts table
  -> [hook.mjs handleUserPrompt] Increment session prompt counter
  -> [hook.mjs handleUserPrompt] Handoff: detect continuation intent → inject previous session context
  -> [hook.mjs handleUserPrompt] Semantic memory injection (hook-memory.mjs), deduped via temp file

Stop
  -> Flush final episode buffer
  -> Save handoff snapshot (on /exit)
  -> Mark session completed
  -> Spawn LLM summary worker (poll-based wait)

Plugin:

/plugin uninstall claude-mem-lite

Architecture comparison

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

Quality comparison

DimensionWinnerWhy
**Classification accuracy**TieBoth 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**originalSees full diffs, but rarely useful for memory search
**Search recall**TieUsers search semantic concepts ("auth bug"), not code lines
**Hook latency****lite**Async background workers; original blocks 2-5s per hook

Comparison: memory systems for AI coding agents

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

**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)
**Target client**Claude Code onlyAny LLM app via SDKAny MCP clientClaude Code only
**Capture model**Auto via hooksManual memory.add()Manual tool calls (create_entities, add_observations)Auto via hooks
**Code-aware retrieval**FTS5 + 100+ synonym pairs (incl. CJK↔EN)General-purposeGeneric graph nodesCode-aware
**Search**Hybrid: FTS5 BM25 + TF-IDF cosine via RRFHybrid: semantic + BM25 + entity linkingKnowledge-graph traversalFTS5 + Chroma vector
**Storage**Single local SQLitePluggable; Qdrant or configurable vector storeSingle JSONL file (knowledge graph)SQLite + Chroma
**LLM dependency**Haiku per episode (5–10 ops batched)LLM per add/search opNone (graph CRUD only)Sonnet per tool call
**Setup**One command (/plugin install or npx)SDK integration + vector store configMCP install (per-client)Bun + Python + Chroma

When to pick which: pick mem0 if you need a memory layer for a non-Claude-Code app (your own agent, multiple LLM providers). Pick the MCP reference memory server if you specifically want a knowledge-graph data model and don't mind invoking memory tools by hand. Pick claude-mem-lite if you want zero-touch automatic capture purpose-built for Claude Code's hook lifecycle, with code-domain retrieval and no external services.

FAQ

中文常见问题

Claude Code 怎么跨会话记住内容? 默认不能。claude-mem-lite 通过 MCP 协议和钩子自动把决策、bug 修复、文件历史持久化到本地 SQLite,下次会话开始时再注入。

和 mem0、官方 MCP memory server 有什么区别? 那两个是通用 LLM 记忆框架;claude-mem-lite 是为 Claude Code 钩子生命周期定制的:批量 episode 处理、代码领域同义词扩展(K8s/DB/数据库等)、文件编辑前主动召回相关历史。

支持中文吗? 完整支持。FTS5 + 中英文同义词扩展(100+ 对,含 CJK ↔ EN 跨语言映射),中文记忆也可用英文关键词召回,反之亦然。

🇨🇳 中文文档镜像 AI 翻译 2026-05-29
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

claude-mem-lite 是一个专为 Claude Code 设计的轻量化记忆增强插件。它通过集成 MCP 协议,能够自动记录并持久化开发过程中的关键决策、Bug 修复记录及文件变更历史,为开发者提供跨会话的上下文感知能力,让 Claude 能够“记住”之前的开发细节。

⚡ 功能介绍

本项目具备自动捕获功能,通过挂钩 Claude Code 的生命周期(如 PostToolUse、SessionStart 等)实现无感记录。��心采用混合搜索技术,结合 FTS5 BM25 关键词匹配与 TF-IDF 向量语义相似度,并通过 Reciprocal Rank Fusion (RRF) 算法进行重排序,确保在处理代码术语时既能精准匹配,又能实现语义层面的召回。此外,还支持 Timeline 浏览功能,方便回溯开发轨迹。

📋 环境依赖

运行本项目需要 Node.js >= 18 环境,并且必须已安装并配置好 Claude Code CLI(确保 `claude` 命令可用)。后端存储依赖 SQLite3,系统将在安装时通过 `better-sqlite3` 进行本地编译。目前平台支持 Linux 或 macOS 系统。

🛠 安装步骤(Docker/pip/源码)

推荐使用 Claude Code 内置的插件管理命令进行安装。通过执行 `/plugin install claude-mem-lite` 即可完成安装与 MCP server 的注册。安装过程会自动处理依赖并编译原生模块。若需卸载,可使用 `/plugin uninstall claude-mem-lite`。请注意,安装后会注册一个名为 `mem-lite` 的 MCP server,包含 20 个工具供调用。

🚀 使用教程

安装完成后,插件将作为 MCP server 运行。你可以通过特定的工具指令进行交互,例如使用 `mem_search` 进行语义搜索,或使用 `mem_timeline` 查看特定时间点的上下文。对于高级用户,可以通过查询 ID 索引来获取完整的开发细节,实现高效的记忆检索与���下文注入。

⚙️ 配置说明(含 MCP / env)

用户可以通过环境变量对插件进行自定义配置。`CLAUDE_MEM_DIR` 用于指定自定义的数据存储目录,所有的数据库文件和运行时资源都将保存在此处(默认路径为 `~/.claude-mem-lite/`)。`CLAUDE_MEM_MODEL` 则用于指定执行后台任务(如 episode 提取和会话摘要)时使用的 LLM 模型,支持 `haiku` 或 `sonnet`。

🔌 API 说明

与通用的 `mem0` 或 MCP `memory` server 不同,claude-mem-lite 是为 Claude Code 的钩子生命周期深度定制的。它采用特殊的 episode 批量处理机制,并针对代码领域进行了同义词扩展(如 K8s、DB 等)。在隐私方面,除了摘要步骤会调用 Anthropic 的 API 外,所有的搜索、存储与检索操作均在本地 SQLite 中完成,确保数据安全且无第三方遥测。

🔄 工作流/模块

插件采用插件市场模式(Plugin Marketplace)进行管理,确保版本更新的安全。其核心工作流包含 Hook Pipeline:在 SessionStart 时生成会话 ID 并查询近期观察结果以注入上下文;在 PostToolUse 时记录工具执行细节。搜索工作流则通过 `mem_search` 建立索引,配合 `mem_timeline` 实现上下文的精准定位与召回。

❓ FAQ 摘要

针对常见问题:Claude Code 默认无法跨会话记忆,但本项目通过 MCP 协议将决策和历史持久化到本地 SQLite,并在新会话开始时自动注入上下文。相比通用框架,它更擅长处理代码领域的语义扩展(如中英文术语转换)。本项目完整支持中文,通过 FTS5 与中英文同义词扩展,能够精准处理 CJK 与英文的混合检索需求。

🎯 aiskill88 AI 点评 A 级 2026-05-22

创新的轻量级记忆解决方案,FTS5搜索能力强劲,架构简洁高效。Star数量适中但技术方案先进,非常适合Claude生态集成应用。

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
部署方案
  • Docker:claude-mem-lite 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
claude-mem-lite 中文教程claude-mem-lite 安装报错怎么办claude-mem-lite MCP 配置claude-mem-lite Docker 部署claude-mem-lite Agent 工作流claude-mem-lite 与同类工具对比claude-mem-lite 最佳实践claude-mem-lite 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • 依赖 Claude 客户端,非 Claude 用户无法使用
  • MCP 协议仍在持续演进,接口可能变更
  • 需要一定的配置步骤
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

支持文本记忆、对话历史、知识片段等多种类型,通过FTS5索引优化查询性能。
💡 AI Skill Hub 点评

AI Skill Hub 点评:claude-mem-lite MCP工具 的核心功能完整,质量良好。对于Claude Desktop / Claude Code 用户来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 claude-mem-lite MCP工具
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 claude-mem-lite
原始描述 开源MCP工具:Lightweight persistent memory system for Claude Code — FTS5 search, episode batc。⭐43 · JavaScript
Topics ClaudeMCP工具记忆系统FTS5搜索持久化
GitHub https://github.com/sdsrss/claude-mem-lite
License MIT
语言 JavaScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/sdsrss/claude-mem-lite

收录时间:2026-05-18 · 更新时间:2026-05-19 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

📺 订阅 AI Skill Hub Daily Telegram 频道
每天 8 条精选 AI Skill、MCP、Agent 与自动化工具推送
加入频道 →