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pathfinder

Persistent memory compression system built for Claude Code .

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项目 README

来源文件:README.md

抓取于 2026年7月28日


Claude-Mem
Vercel OSS Program

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Persistent memory compression system built for Claude Code.

License Version Node Mentioned in Awesome Claude Code

thedotmack/claude-mem | Trendshift


Claude-Mem Preview Star History Chart

Quick Start • How It Works • Search Tools • Documentation • Configuration • Troubleshooting • License

Claude-Mem seamlessly preserves context across sessions by automatically capturing tool usage observations, generating semantic summaries, and making them available to future sessions. This enables Claude to maintain continuity of knowledge about projects even after sessions end or reconnect.


Quick Start

Install with a single command:

npx claude-mem install

Or install for OpenCode:

npx claude-mem install --ide opencode

Or install for Antigravity CLI (setup guide):

npx claude-mem install --ide antigravity

Or install from the plugin marketplace inside Claude Code:

/plugin marketplace add thedotmack/claude-mem

/plugin install claude-mem

Restart Claude Code. Context from previous sessions will automatically appear in new sessions.

Note: Claude-Mem is also published on npm, but npm install -g claude-mem installs the SDK/library only — it does not register the plugin hooks or set up the worker service. Always install via npx claude-mem install or the /plugin commands above.

🦞 OpenClaw Gateway

Install claude-mem as a persistent memory plugin on OpenClaw gateways with a single command:

curl -fsSL https://install.cmem.ai/openclaw.sh | bash

The installer handles dependencies, plugin setup, AI provider configuration, worker startup, and optional real-time observation feeds to Telegram, Discord, Slack, and more. See the OpenClaw Integration Guide for details.

Key Features:

  • 🧠 Persistent Memory - Context survives across sessions
  • 📊 Progressive Disclosure - Layered memory retrieval with token cost visibility
  • 🔍 Skill-Based Search - Query your project history with mem-search skill
  • 🖥️ Web Viewer UI - Real-time memory stream at the worker URL printed on startup
  • 💻 Claude Desktop Skill - Search memory from Claude Desktop conversations
  • 🔒 Privacy Control - Use <private> tags to exclude sensitive content from storage
  • ⚙️ Context Configuration - Fine-grained control over what context gets injected
  • 🤖 Automatic Operation - No manual intervention required
  • 🔗 Citations - Reference past observations with IDs through the worker API or view all in the web viewer

Documentation

📚 View Full Documentation - Browse on official website

Getting Started

  • Installation Guide - Quick start & advanced installation
  • Usage Guide - How Claude-Mem works automatically
  • Search Tools - Query your project history with natural language
  • Cloud Sync - Back up your memories to cmem.ai — no daemon, the worker syncs on write

Best Practices

Architecture

Configuration & Development


How It Works

Core Components:

  1. 5 Lifecycle Hooks - SessionStart, UserPromptSubmit, PostToolUse, Stop, SessionEnd (6 hook scripts)
  2. Smart Install - Cached dependency checker (pre-hook script, not a lifecycle hook)
  3. Worker Service - Local HTTP API with web viewer UI and search endpoints, managed by Bun
  4. SQLite Database - Stores sessions, observations, summaries
  5. mem-search Skill - Natural language queries with progressive disclosure
  6. Chroma Vector Database - Hybrid semantic + keyword search for intelligent context retrieval

See Architecture Overview for details.


MCP Search Tools

Claude-Mem provides intelligent memory search through 4 MCP tools following a token-efficient 3-layer workflow pattern:

The 3-Layer Workflow:

  1. search - Get compact index with IDs (~50-100 tokens/result)
  2. timeline - Get chronological context around interesting results
  3. get_observations - Fetch full details ONLY for filtered IDs (~500-1,000 tokens/result)

How It Works:

  • Claude uses MCP tools to search your memory
  • Start with search to get an index of results
  • Use timeline to see what was happening around specific observations
  • Use get_observations to fetch full details for relevant IDs
  • ~10x token savings by filtering before fetching details

Available MCP Tools:

  1. search - Search memory index with full-text queries, filters by type/date/project
  2. timeline - Get chronological context around a specific observation or query
  3. get_observations - Fetch full observation details by IDs (always batch multiple IDs)

Example Usage:

// Step 1: Search for index
search(query="authentication bug", type="bugfix", limit=10)

// Step 2: Review index, identify relevant IDs (e.g., #123, #456)

// Step 3: Fetch full details
get_observations(ids=[123, 456])

See Search Tools Guide for detailed examples.


Release Branches

Stable releases ship from main and are published to npm. core-dev and community-edge are source-run branches for early reliability fixes and community integrations. See Release Branches for the branch flow and non-stable run instructions.


System Requirements

  • Node.js: 20.0.0 or higher
  • Claude Code: Latest version with plugin support
  • Bun: JavaScript runtime and process manager (auto-installed if missing)
  • uv: Python package manager for vector search (auto-installed if missing)
  • SQLite 3: For persistent storage (bundled)

Windows Setup Notes

If you see an error like:

npm : The term 'npm' is not recognized as the name of a cmdlet

Make sure Node.js and npm are installed and added to your PATH. Download the latest Node.js installer from https://nodejs.org and restart your terminal after installation.


Configuration

Settings are managed in ~/.claude-mem/settings.json (auto-created with defaults on first run). Configure AI model, worker port, data directory, log level, and context injection settings.

See the Configuration Guide for all available settings and examples.

Mode & Language Configuration

Claude-Mem supports multiple workflow modes and languages via the CLAUDE_MEM_MODE setting.

This option controls both:

  • The workflow behavior (e.g. code, chill, investigation)
  • The language used in generated observations

How to Configure

Edit your settings file at ~/.claude-mem/settings.json:

{
  "CLAUDE_MEM_MODE": "code--zh"
}

Modes are defined in plugin/modes/. To see all available modes locally:

ls ~/.claude/plugins/marketplaces/thedotmack/plugin/modes/

Available Modes

ModeDescription
codeDefault English mode
code--zhSimplified Chinese mode
code--jaJapanese mode

Language-specific modes follow the pattern code--[lang] where [lang] is the ISO 639-1 language code (e.g., zh for Chinese, ja for Japanese, es for Spanish).

Note: code--zh (Simplified Chinese) is already built-in — no additional installation or plugin update is required.

After Changing Mode

Restart Claude Code to apply the new mode configuration.

Development

See the Development Guide for build instructions, testing, and contribution workflow.


Troubleshooting

If experiencing issues, describe the problem to Claude and the troubleshoot skill will automatically diagnose and provide fixes.

See the Troubleshooting Guide for common issues and solutions.


Bug Reports

Create comprehensive bug reports with the automated generator:

cd ~/.claude/plugins/marketplaces/thedotmack
npm run bug-report

Contributing

Contributions are welcome! Please:

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes with tests
  4. Update documentation
  5. Submit a Pull Request

Claude-Mem ships from three branches: main (stable), core-dev, and community-edge. Only main is published to npm; the others are run from source. See Release Branches for the strategy and local run instructions.

See Development Guide for contribution workflow.


License

Claude-Mem is licensed under the Apache License 2.0.

We chose Apache-2.0 because durable agentic memory should be easy to embed in developer tools, local agents, MCP servers, enterprise systems, robotics stacks, and production agent harnesses.

See the LICENSE file for full details. See docs/license.md and docs/ip-boundary.md for licensing scope and the open/commercial boundary.

Note on Ragtime: The ragtime/ directory is licensed under the Apache License 2.0. See ragtime/LICENSE for details.


Support


Built with Claude Agent SDK | Works with Claude Code | Made with TypeScript


What About CMEM?

CMEM is a token created by a 3rd party but officially embraced by the creator of Claude-Mem (Alex Newman, @thedotmack). The token acts as a community catalyst for growth and a vehicle for bringing CMEM to the developers and knowledge workers that need it most.

Official BASE CA: 0x76b1967eec0ccaeb001bbbb2b40dc4badba31ba3

其他

低风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:0 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/thedotmack/claude-mem.git
  3. 将 "plugin/skills/pathfinder" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/thedotmack/claude-mem.git
  3. 将 "plugin/skills/pathfinder" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/thedotmack/claude-mem.git
  3. 将 "plugin/skills/pathfinder" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/thedotmack/claude-mem.git
  3. 将 "plugin/skills/pathfinder" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/thedotmack/claude-mem.git
  3. 将 "plugin/skills/pathfinder" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: pathfinder
description: Map a codebase into feature-grouped flowcharts, identify duplicated concerns across features, and propose a unified architecture. Use when asked to "find the ideal path," unify duplicated systems, or audit architecture before a refactor. Emits a proposed unified flowchart plus per-system /make-plan prompts.

Pathfinder

You are an ORCHESTRATOR. Map the codebase into feature-grouped flowcharts, identify duplicated concerns, propose the simplest unified architecture, and hand off per-system plans to /make-plan.

You do not write implementation code. You produce diagrams, a duplication report, a proposed unified flowchart, and handoff prompts.

Delegation Model

Use subagents for discovery and extraction (file reading, flow tracing, grep, diagramming). Keep synthesis (deciding feature boundaries, picking unification strategies, final flowchart) with the orchestrator. Reject subagent reports that lack source citations and redeploy.

Subagent Reporting Contract (MANDATORY)

Each subagent response must include:

  1. Sources consulted — exact file paths and line ranges read
  2. Concrete findings — exact function names, call sites, data flow
  3. Mermaid diagram(s) with nodes labeled by file:line
  4. Confidence note + known gaps

Output Artifacts

All artifacts go in PATHFINDER-<YYYY-MM-DD>/ at repo root:

  • 00-features.md — feature inventory with boundaries
  • 01-flowcharts/<feature>.md — one Mermaid flowchart per feature
  • 02-duplication-report.md — cross-cutting duplicated concerns with evidence
  • 03-unified-proposal.md — proposed unified architecture + Mermaid
  • 04-handoff-prompts.md — copy-pasteable /make-plan prompts per unified system

Phases

Phase 0: Feature Discovery (ALWAYS FIRST)

Deploy ONE "Feature Discovery" subagent to:

  1. Walk the source tree (not built artifacts) and read top-level README / CLAUDE.md
  2. Propose feature boundaries based on directory structure, import graph, and naming
  3. Return a flat list of features with: name, entry points (file:line), core files, brief purpose

Orchestrator reviews the proposal, adjusts boundaries if needed, writes 00-features.md. Do NOT fan out until feature boundaries are approved.

Phase 1: Per-Feature Flowcharts (FAN OUT)

Deploy ONE "Flowchart" subagent per feature in parallel. Each receives only its feature's scope. Each must:

  1. Trace the feature's primary happy path from entry point to terminal state
  2. Identify side effects (DB writes, HTTP calls, file I/O, process spawns)
  3. Note error and fallback branches but do not let them dominate the diagram
  4. Produce a Mermaid flowchart TD with every node labeled Name<br/>file:line
  5. List external dependencies (other features it calls into) at the bottom

Orchestrator writes each flowchart to 01-flowcharts/<feature>.md. Reject any diagram missing file:line labels.

Phase 2: Duplication Hunt

Deploy TWO subagents in parallel:

"Within-Feature Duplication" subagent:

  • For each feature, find repeated code/logic patterns inside the feature only
  • Report only duplications worth consolidating (ignore trivial repetition)

"Cross-Feature Duplication" subagent:

  • Compare flowcharts across features for concerns that appear in multiple places
  • Examples of what to look for: multiple capture paths, parallel queue implementations, duplicated storage/migration code, repeated agent scaffolding, parallel parsing layers
  • For each duplication, report: (a) the concern, (b) every location with file:line, (c) why they diverged, (d) whether the divergence is legitimate specialization or accidental

Orchestrator synthesizes both into 02-duplication-report.md. Every duplication claim must cite ≥2 file:line locations.

Phase 3: Unified Proposal (ORCHESTRATOR)

The orchestrator writes 03-unified-proposal.md itself — do not delegate synthesis.

For each duplicated concern from Phase 2 that is NOT legitimate specialization:

  1. Propose the simplest unified design (one path, one store, one handler — whatever applies)
  2. Name the consolidated component and its single entry point
  3. Show what each old call site becomes
  4. Call out any loss of capability and whether it's acceptable

End the document with ONE combined Mermaid flowchart showing the proposed unified system. Nodes still labeled with target file:line (new or existing) where knowable.

Anti-patterns to reject in your own proposal:

  • Adding a new abstraction layer "for flexibility"
  • Keeping both old paths behind a feature flag
  • Introducing a registry/factory when a switch statement suffices
  • Preserving divergent behavior "just in case"

Phase 4: Per-System Handoff Prompts

For each unified system in the proposal, write a ready-to-run /make-plan prompt to 04-handoff-prompts.md. Each prompt must:

  1. State the target unified component and its single entry point
  2. List the exact call sites to rewrite (from Phase 2 evidence)
  3. Cite the relevant flowchart file from 01-flowcharts/
  4. Include anti-pattern guards specific to this system

Format each as a fenced code block the user can copy directly into /make-plan.

Key Principles

  • Evidence over intuition — every diagram node and duplication claim cites file:line
  • Current state before ideal state — Phases 0–2 describe what IS; Phase 3 describes what SHOULD BE
  • Simplest unification wins — prefer deletion over abstraction; prefer one path over configurable paths
  • Specialization is not duplication — two components serving different trust models or data sources are legitimate even if their code looks similar
  • Handoff, don't implement — Pathfinder ends at plan prompts; /make-plan and /do take it from there

Failure Modes to Prevent

  • Drawing flowcharts from memory instead of source — redeploy subagent with grep evidence requirement
  • Proposing unification of legitimately specialized components — re-examine trust/data-source divergence
  • Handoff prompts that lack concrete call sites — rewrite with Phase 2 evidence
  • Skipping Phase 0 boundary review — fanning out on bad feature boundaries wastes all of Phase 1

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