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长手记忆
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长手记忆

基于 Python · 让 AI 助手直接操作你的系统与工具
英文名:longhand
⭐ 10 Stars 🍴 4 Forks 💻 Python 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
mcpai-memoryanthropicchromadbclaude-code
✦ AI Skill Hub 推荐

AI Skill Hub 推荐使用:长手记忆 是一款优质的MCP工具。AI 综合评分 7.5 分,在同类工具中表现稳健。如果你正在寻找可靠的MCP工具解决方案,这是一个值得深入了解的选择。

📚 深度解析

长手记忆 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 长手记忆,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。长手记忆 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 长手记忆 评为 AI 评分 7.5 分,属于同类工具中的优质选择。

📋 工具概览

长手记忆 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
⭐ 10
开发语言
Python
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
7.5 分
工具类型
MCP工具
Forks
4

📖 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」。

长手记忆 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/Wynelson94/longhand

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

# 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 长手记忆 执行以下任务...
Claude: [自动调用 长手记忆 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": {
    "____": {
      "command": "npx",
      "args": ["-y", "longhand"],
      "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 69/100 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。

Longhand

Longhand MCP server PyPI version Python License Tests Local SafeSkill 93/100

Persistent local memory for Claude Code. Every tool call, every file edit, every thinking block from every Claude Code session — stored verbatim on your machine. Searchable, replayable, and recallable by fuzzy natural-language questions. Zero API calls. Zero summaries. Zero decisions made by an AI about what's worth remembering.

Claude Code quietly rotates your session files after a few weeks. Longhand captures them into SQLite before they're gone. Once ingested, your history stays forever — even after the source JSONL files are deleted. Install early; the past you don't capture is unrecoverable.
If you have 20+ Claude Code sessions in ~/.claude/projects/, Longhand can search across every fix, decision, and conversation you've had in ~56ms — without a single API call.
Does it use a lot of tokens? No — every tool is capped by design. A full recall across 100+ sessions returns ~4K tokens. Reading one raw session JSONL costs 10–50× more. See Token budget.
pip install longhand
longhand setup        # ingest history + install hooks + configure MCP
longhand recall "that stripe webhook bug from last week"

Want to kick the tires first? Run longhand demo for a 60-second walkthrough on a fake 3-session sample corpus — your real ~/.claude and ~/.longhand are not touched. The demo seeds a sandboxed store with a Stripe-webhook bug + Supabase auth migration + downstream 401 fix, then runs cross-session recall and project-status so you can see what the output looks like before committing.

pip install longhand
longhand demo         # sandboxed; cleans up afterwards (pass --keep to explore)

Upgrading to 0.9.0? Live ingestion captures sessions in flight, plan history is preserved as first-class data, and an optional reconciler job keeps the index honest in the background:

  • New longhand ingest-live command runs from Claude Code's Stop hook to tail the active transcript between assistant turns. Sessions show up in recall while you're still working, not after they end.
  • New longhand plans list command and list_plans MCP tool surface every Write/Edit to ~/.claude/plans/*.md across your entire history. Plans are now extracted as their own entity alongside episodes.
  • New longhand schedule install-reconciler installs an optional launchd job that runs reconcile --fix periodically — catches anything the live and post-session hooks missed without you ever thinking about it.
  • The Stop hook coexists with the existing SessionEnd hook: live tails the transcript as it grows; SessionEnd does the full analysis pass when the session closes.

Upgrading to 0.8.1? Staleness signals now propagate everywhere they belong, and reconcile is an MCP tool — Claude can self-heal the index from inside a session:

  • search and list_sessions now wrap the response with stale: true + stale_reason when the project they're scoped to has on-disk transcripts not yet ingested. Pre-v0.8.1 these returned clean-looking empty results (same silent-failure shape recall_project_status was built to catch — just one layer up).
  • New reconcile MCP tool wraps longhand reconcile --fix. After a staleness banner fires, Claude calls reconcile directly instead of asking the user to run a CLI command.
  • list_sessions default limit raised from 20 to 50 — active days routinely cross 5+ projects across 5+ sessions; the old default truncated reviews silently.

Upgrading from 0.7.x or earlier? Cleaner recall narratives, plus a real bug-finding test layer underneath (from 0.8.0):

  • Pre-v0.8 _compose_fix_summary prepended a literal "Intent:" label to half of all extracted episodes (49% of the reference corpus). The label leaked into every recall narrative for those episodes. Migration v4 strips it from existing rows on first store open — no command needed.
  • Diff content in fix_summary now truncates at whitespace boundaries with a visible , instead of landing mid-token (phoneNum', family?:', strin'). Forward-only.
  • Narrative footer "Other matches" lines now include the session id so you can drill in.
  • New canary harness (tests/fixtures/corpus/) anchors regression tests to real shipped bugs. New recall validator (scripts/recall_diff.py) snapshots and diffs ranking results against your live corpus — catches regressions pytest can't see.
pip install --upgrade longhand
longhand recall "..."   # migration runs transparently on first open

If you're also coming from 0.5.x, run longhand reconcile --fix once to re-attribute multi-project sessions per the v0.6 inference improvements (cd-into-project sessions now attribute to the project where most work happened, not the first-event cwd). If you're on 0.5.8 or earlier, chain them: longhand reconcile --fix && longhand analyze --all. Both are idempotent.

Large history? (>1 GB of ~/.claude/projects) Expect the first-time backfill to take 10–30 minutes on an M-class Mac — most of that wall time is the embedding model running on all your cores (which is why you'll see triple-digit CPU%; that's ONNX doing its job, not a hang). To get a working store faster, use the fast-path:

longhand setup --skip-analysis   # SQLite only; works in ~1 min for multi-GB corpora
longhand analyze --all           # fill in episodes + vectors whenever, safe to background

Exact-text search, timelines, file history, and commit lookup all work after --skip-analysis. Semantic recall needs the analyze --all pass to complete. Typical throughput on an M-class Mac is ~1–2 sessions/sec for full analysis.

Status: v0.11.0 — stable, daily-driver tested, security-audited (zero critical findings), on PyPI, available as a Claude Code plugin. Validated against 246 real Claude Code sessions across 54 inferred projects. 316 unit tests passing.

Full docs: Longhand Wiki — getting started, CLI reference, MCP tools reference, architecture, and troubleshooting.

Longhand demo

---

What's in the archive?

longhand stats longhand sessions longhand projects

Install

pip install longhand
longhand setup

That's it. longhand setup backfills your existing Claude Code history, installs the hooks that keep it updated automatically, registers Longhand as an MCP server for Claude Code, and verifies everything works. About two minutes the first time, zero maintenance after that.

To upgrade later: pip install -U longhand.

Developer install (from source)

git clone https://github.com/Wynelson94/longhand.git
cd longhand
pip install -e .
longhand setup

<details> <summary>Or run the individual commands yourself</summary>

longhand ingest                       # ingest all your existing Claude Code history
longhand analyze --all                # run analysis (projects, outcomes, episodes, segments)
longhand hook install                 # wires both SessionEnd and Stop hooks
longhand ingest-live                  # live-tail the active transcript (Stop hook calls this)
longhand prompt-hook install          # (optional) auto-inject past context into new prompts
longhand mcp install                  # let Claude Code call Longhand as MCP tools
longhand schedule install-reconciler  # (optional) launchd job to run reconcile --fix periodically
longhand config                       # view/tune hook behavior (relevance threshold, injection size)
longhand doctor                       # verify everything is wired up
</details>

---

Quick Start

```bash

Recall Example

``` $ longhand recall "that stripe webhook I was fixing"

╭─ Project matches ───────────────────────────────────────╮ │ new-product (nextjs web app) · alias: 'stripe' · 1.52 │ ╰─────────────────────────────────────────────────────────╯

Found it: new-product · 2 weeks ago · session a4ba29d1

Configuration

longhand config # show current hook settings longhand config --set hook.min_relevance=3.0 # tune injection threshold longhand config --set hook.max_inject_chars=1000 # cap token usage

MCP Integration (Claude Desktop)

Run longhand mcp install to wire Longhand into Claude Desktop's config. After you restart Claude Desktop, it has nineteen tools:

Core (searchable archive): - search — semantic search with session, project, tool, file, and event_type filters (all combinable) - list_sessions — recent sessions with project/time filters - get_session_timeline — chronological view with offset/tail pagination and summary-only scan mode - get_latest_events — most recent events across all sessions, project- and tool-filterable - replay_file — reconstruct file state at a point in time - get_file_history — every edit to a file across all sessions - get_stats — storage statistics

Proactive memory: - recall — fuzzy natural-language recall (use this first) - recall_project_status — "where did we leave off on X?" — git-aware project summary with commits, issues, last outcome - search_in_context — find something in a session and get the surrounding conversation - match_project — find projects by partial name / category / description - find_episodes — structured search for problem→fix pairs - get_episode — full detail for one episode including diff + file state - list_projects — browse inferred projects (compact by default, verbose optional) - get_project_timeline — session-level timeline for one project - list_plans — every Write/Edit to ~/.claude/plans/*.md across your entire history

Git history: - get_session_commits — all git operations in a session (commits, pushes, checkouts, merges) - find_commits — search across all sessions by commit message, hash prefix, or branch name

Self-healing: - reconcile — wraps longhand reconcile --fix so Claude can re-attribute and re-ingest from inside a session after a staleness banner

All tools support max_chars output capping with pagination hints. No more 96k dumps crashing your context.

Once installed, you can ask Claude things like "what did we decide about the auth middleware in last week's session?" and it will actually search its own past work.

---

Longhand vs claude-mem

thedotmack/claude-mem is the most popular Claude Code memory tool on GitHub (55k+ stars). It's a good tool. It is also solving the memory problem in the opposite direction from Longhand, and the difference is worth understanding before you pick one.

claude-memLonghand
**What's stored**AI-generated summaries / "observations"Verbatim events from the raw JSONL
**Who decides what's kept**An LLM, at write timeNobody — everything is kept
**Compression**Semantic (lossy, by design)None (lossless)
**API calls per session**One or more (calls Claude to summarize)Zero
**Thinking blocks**Typically folded into summariesFirst-class, stored verbatim
**Deterministic replay**No — summaries can't reconstruct file stateYes — every diff kept and replayable
**Model portability**Tied to the summarizer's outputSame data works across any model, forever
**Runtime**TypeScript, Bun, HTTP worker on :37777Python, no server
**License**AGPL-3.0MIT

The philosophical split: claude-mem asks an AI what was important and keeps that. Longhand keeps the actual bytes and lets you decide later. If you trust a model's judgment about its own past, claude-mem's approach is cheaper at query time (pre-summarized) and easier on storage. If you've ever been burned by a summary that dropped the thing that turned out to matter, Longhand is the tool that never throws anything away.

Both can coexist on the same machine — they operate on the same JSONL files without interfering.

---

Comparison

LonghandSummary-based (Mem0, MemPalace, LangMem)
SourceRaw Claude Code JSONLAI-generated summaries
Tool calls capturedEvery one, verbatimWhatever the summarizer kept
File editsFull before/after diffsUsually not captured
Thinking blocksFirst-class eventsUsually discarded
File state replayDeterministicNot possible
Problem→fix extractionRules-based, at ingestDepends on summarizer
Fuzzy recallYes, with artifactsText search over summaries
What gets "decided"Nothing — store everythingThe AI decides what matters
Local-firstYesMost
CompletenessEvery event from the session fileWhatever the summarizer kept
LLM calls to functionZeroVaries

Summary memory and Longhand solve different problems. Summary memory is good for long-term personal assistants that need compressed context across many conversations. Longhand is good for developers who need forensic access to their past Claude Code work — the kind of access where you need the exact diff, not a paraphrase.

---

🎯 aiskill88 AI 点评 A 级 2026-06-09

长手记忆是一个有潜力的MCP工具

📚 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
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
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
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
longhand 中文教程longhand 安装报错怎么办longhand MCP 配置longhand 与同类工具对比longhand 最佳实践longhand 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
⭐ 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
⚠️ 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
  • Python 依赖冲突:建议用 venv / uv 隔离环境

👥 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

长手记忆是一种本地无损记忆工具,用于优化AI记忆
💡 AI Skill Hub 点评

总体来看,长手记忆 是一款质量良好的MCP工具,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 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 长手记忆
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 longhand
原始描述 开源MCP工具:Lossless local memory for Claude Code. The full, unabbreviated version. Every to。⭐10 · Python
Topics mcpai-memoryanthropicchromadbclaude-code
GitHub https://github.com/Wynelson94/longhand
License MIT
语言 Python
🔗 原始来源
🐙 GitHub 仓库  https://github.com/Wynelson94/longhand 🌐 官方网站  https://glama.ai/mcp/servers/Wynelson94/longhand

收录时间:2026-06-09 · 更新时间:2026-06-09 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。