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smart-explore

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

开发与工程研究与检索

中风险

  • 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：1 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/smart-explore" 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/smart-explore" 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/smart-explore" 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/smart-explore" 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/smart-explore" 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: smart-explore
description: Token-optimized structural code search using tree-sitter AST parsing. Use instead of reading full files when you need to understand code structure, find functions, or explore a codebase efficiently.

Smart Explore

Structural code exploration using AST parsing. This skill overrides your default exploration behavior. While this skill is active, use smart_search/smart_outline/smart_unfold as your primary tools instead of Read, Grep, and Glob.

Core principle: Index first, fetch on demand. Give yourself a map of the code before loading implementation details. The question before every file read should be: "do I need to see all of this, or can I get a structural overview first?" The answer is almost always: get the map.

Your Next Tool Call

This skill only loads instructions. You must call the MCP tools yourself. Your next action should be one of:

smart_search(query="<topic>", path="./src")    -- discover files + symbols across a directory
smart_outline(file_path="<file>")              -- structural skeleton of one file
smart_unfold(file_path="<file>", symbol_name="<name>")  -- full source of one symbol

Do NOT run Grep, Glob, Read, or find to discover files first. smart_search walks directories, parses all code files, and returns ranked symbols in one call. It replaces the Glob → Grep → Read discovery cycle.

3-Layer Workflow

Step 1: Search -- Discover Files and Symbols

smart_search(query="shutdown", path="./src", max_results=15)

Returns: Ranked symbols with signatures, line numbers, match reasons, plus folded file views (~2-6k tokens)

-- Matching Symbols --
  function performGracefulShutdown (services/infrastructure/GracefulShutdown.ts:56)
  function httpShutdown (services/infrastructure/HealthMonitor.ts:92)
  method WorkerService.shutdown (services/worker-service.ts:846)

-- Folded File Views --
  services/infrastructure/GracefulShutdown.ts (7 symbols)
  services/worker-service.ts (12 symbols)

This is your discovery tool. It finds relevant files AND shows their structure. No Glob/find pre-scan needed.

Parameters:

  • query (string, required) -- What to search for (function name, concept, class name)
  • path (string) -- Root directory to search (defaults to cwd)
  • max_results (number) -- Max matching symbols, default 20, max 50
  • file_pattern (string, optional) -- Filter to specific files/paths

Step 2: Outline -- Get File Structure

smart_outline(file_path="services/worker-service.ts")

Returns: Complete structural skeleton -- all functions, classes, methods, properties, imports (~1-2k tokens per file)

Skip this step when Step 1's folded file views already provide enough structure. Most useful for files not covered by the search results.

Parameters:

  • file_path (string, required) -- Path to the file

Step 3: Unfold -- See Implementation

Review symbols from Steps 1-2. Pick the ones you need. Unfold only those:

smart_unfold(file_path="services/worker-service.ts", symbol_name="shutdown")

Returns: Full source code of the specified symbol including JSDoc, decorators, and complete implementation (~400-2,100 tokens depending on symbol size). AST node boundaries guarantee completeness regardless of symbol size — unlike Read + agent summarization, which may truncate long methods.

Parameters:

  • file_path (string, required) -- Path to the file (as returned by search/outline)
  • symbol_name (string, required) -- Name of the function/class/method to expand

When to Use Standard Tools Instead

Use these only when smart_* tools are the wrong fit:

  • Grep: Exact string/regex search ("find all TODO comments", "where is ensureWorkerStarted defined?")
  • Read: Small files under ~100 lines, non-code files (JSON, markdown, config)
  • Glob: File path patterns ("find all test files")
  • Explore agent: When you need synthesized understanding across 6+ files, architecture narratives, or answers to open-ended questions like "how does this entire system work end-to-end?" Smart-explore is a scalpel — it answers "where is this?" and "show me that." It doesn't synthesize cross-file data flows, design decisions, or edge cases across an entire feature.

For code files over ~100 lines, prefer smart_outline + smart_unfold over Read.

Workflow Examples

Discover how a feature works (cross-cutting):

1. smart_search(query="shutdown", path="./src")
   -> 14 symbols across 7 files, full picture in one call
2. smart_unfold(file_path="services/infrastructure/GracefulShutdown.ts", symbol_name="performGracefulShutdown")
   -> See the core implementation

Navigate a large file:

1. smart_outline(file_path="services/worker-service.ts")
   -> 1,466 tokens: 12 functions, WorkerService class with 24 members
2. smart_unfold(file_path="services/worker-service.ts", symbol_name="startSessionProcessor")
   -> 1,610 tokens: the specific method you need
Total: ~3,076 tokens vs ~12,000 to Read the full file

Write documentation about code (hybrid workflow):

1. smart_search(query="feature name", path="./src")    -- discover all relevant files and symbols
2. smart_outline on key files                           -- understand structure
3. smart_unfold on important functions                  -- get implementation details
4. Read on small config/markdown/plan files             -- get non-code context

Use smart_* tools for code exploration, Read for non-code files. Mix freely.

Exploration then precision:

1. smart_search(query="session", path="./src", max_results=10)
   -> 10 ranked symbols: SessionMetadata, SessionQueueProcessor, SessionSummary...
2. Pick the relevant one, unfold it

Token Economics

ApproachTokensUse Case
smart_outline~1,000-2,000"What's in this file?"
smart_unfold~400-2,100"Show me this function"
smart_search~2,000-6,000"Find all X across the codebase"
search + unfold~3,000-8,000End-to-end: find and read (the primary workflow)
Read (full file)~12,000+When you truly need everything
Explore agent~39,000-59,000Cross-file synthesis with narrative

4-8x savings on file understanding (outline + unfold vs Read). 11-18x savings on codebase exploration vs Explore agent. The narrower the query, the wider the gap — a 27-line function costs 55x less to read via unfold than via an Explore agent, because the agent still reads the entire file.

Language Support

Smart-explore uses tree-sitter AST parsing for structural analysis. Unsupported file types fall back to text-based search.

Bundled Languages

LanguageExtensions
JavaScript.js, .mjs, .cjs
TypeScript.ts
TSX / JSX.tsx, .jsx
Python.py, .pyw
Go.go
Rust.rs
Ruby.rb
Java.java
C.c, .h
C++.cpp, .cc, .cxx, .hpp, .hh

Files with unrecognized extensions are parsed as plain text — smart_search still works (grep-style), but smart_outline and smart_unfold will not extract structured symbols.

Custom Grammars (.claude-mem.json)

You can register additional tree-sitter grammars for file types not in the bundled list. Create or update .claude-mem.json in your project root:

{
  "grammars": {
    "solidity": {
      "package": "tree-sitter-solidity",
      "extensions": [".sol"],
      "query": "solidity-query.scm"
    }
  }
}

Each key is a language name. package is the npm package of the tree-sitter grammar and extensions lists the file extensions it covers; the package must be installed in the project's node_modules (npm install tree-sitter-solidity). query (optional) is a path, relative to the config file, to a tree-sitter query whose captures (@func, @cls, @method, @iface, @enm, @struct_def, @imp) extract symbols. Without query, a minimal generic pattern is used — it only matches grammars that define function_declaration/class_declaration node types, and query compilation fails silently (0 symbols) for grammars that lack them, so a custom query is effectively required for most languages. Once registered, smart_outline and smart_unfold parse those extensions structurally instead of falling back to plain text.

Markdown Special Support

Markdown files (.md, .mdx) receive special handling beyond the generic plain-text fallback:

  • smart_outline — extracts headings (#, ##, ###) as the symbol tree. Use it to navigate long documents without reading the full file.
  • smart_search — searches within code fences as well as prose, so queries for function names inside ```ts ``` blocks work as expected.
  • smart_unfold — expands heading sections rather than function bodies; each section up to the next same-level heading is returned as a chunk.
  • Frontmatter — YAML frontmatter (lines between leading --- delimiters) is included in smart_outline output under a synthetic frontmatter symbol so metadata like title: and description: is visible without reading the whole file.

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