原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

codeagent-memory

作者:HanochZhu版本 0.1.6i★ –i在你的电脑上运行
注册名称io.github.HanochZhu/codeagent-memory

安装

概览

作者提供的说明(英文原文)

Local code graph and solution memory for coding agents via CLI and MCP.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "codeagent-memory": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hanochzhu/codeagent-memory:0.1.6"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add codeagent-memory -- docker run -i --rm ghcr.io/hanochzhu/codeagent-memory:0.1.6

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "codeagent-memory": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hanochzhu/codeagent-memory:0.1.6"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "codeagent-memory": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/hanochzhu/codeagent-memory:0.1.6"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 HanochZhu 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 7226 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7226 个服务器的 83% 更新
  • 基础检查结果等于或优于此分类中其他 7226 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

codeagent-memory 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.6)于7天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT3天前更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT44天前更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

cam

English · 中文 · 日本語 · 한국어 · العربية · 🌐 Website

A local multi-turn conversational memory system purpose-built for coding agents

Code graph + memory · surgical reads · 100% local · MCP + CLI

Kernel written in Rust

Contents

  • Why cam?
  • Get Started
  • Key Features
  • How It Works
  • Agent Workflow
  • CLI Reference
  • Memory Rules
  • Configuration
  • Benchmarks
  • Supported Agents
  • Supported Languages
  • Supported Platforms
  • License

Guides: Interactive Showcase (Website) · Main Agent vs Subagent · MCP Configs by IDE · 中文说明


Why cam?

When an AI coding agent explores a repository, it usually rediscovers structure the slow way — running grep, glob, and full-file Read one file at a time — and forgets everything in the next session.

`cam` gives the agent two local retrieval planes in a single binary:

PlaneWhat it storesHow the agent uses it
Code GraphTree-sitter symbols (functions, types, methods) as a virtual filesystem + 1-hop callers/calleesls, find, read src/file.rs/symbol, `ref symbol --dir in\out` — surgical symbol reads instead of whole-file dumps
Memory TreeProject facts, architecture decisions, debugging write-ups, and user habitsrecall (Vector + BM25 fused with RRF) and add (with branching --parent and revision --supersedes)

One binary, two interfaces: The main agent calls cam over MCP (cam mcp); subagents (which rarely inherit MCP tools) call the exact same CLI (cam --json …). Both share .cam/cam.db on disk, 100% local, zero API cost.


Get Started

Option 1: One-Sentence Agent Install (Recommended)

Paste this prompt directly into your coding agent (Cursor, Claude Code, Codex, Windsurf, GitHub Copilot, Cline, etc.) and let it install, index, and wire cam for you:

Install cam from https://github.com/HanochZhu/codeagent_memory: require a working Rust toolchain (rustup), run `cargo install --git https://github.com/HanochZhu/codeagent_memory --locked`, ensure ~/.cargo/bin (or %USERPROFILE%\.cargo\bin on Windows) is on PATH, then verify with `cam --help`. In this repo run `cam index`. For the main agent, register MCP stdio: command `cam`, args `["mcp"]` (Cursor: .cursor/mcp.json; Claude Code: .mcp.json). Subagents call `cam --json` in the shell. Do not add extra docs.

How each IDE / agent handles this prompt

EnvironmentWhat to do
Cursor / Windsurf / Claude Code / CodexPaste into Agent / Chat; the agent runs the terminal install, builds .cam/cam.db, and writes the MCP config
VS Code (Copilot / Continue / Cline)Paste into Agent / Chat mode with terminal access enabled
JetBrains (IntelliJ / RustRover / GoLand)Paste into AI Assistant, or run cargo install in the built-in terminal

Note: cargo install places cam in ~/.cargo/bin (Windows: %USERPROFILE%\.cargo\bin). Restart your terminal or IDE if cam is not immediately found on PATH.


Option 2: Manual Setup
1. Install the CLI

Requires a Rust toolchain:

cargo install --git https://github.com/HanochZhu/codeagent_memory --locked

cam --help

Alternative: Build from local clone or run via Docker

From a local clone:

git clone https://github.com/HanochZhu/codeagent_memory.git

cd codeagent_memory cargo install --path . --locked

Docker (no Rust toolchain required):

docker pull ghcr.io/hanochzhu/codeagent-memory:0.1.6

docker run -i --rm -v "$PWD:/work" -w /work ghcr.io/hanochzhu/codeagent-memory:0.1.6

Or build locally: docker build -t cam https://github.com/HanochZhu/codeagent_memory.git

2. Index & query your project
cd your-project

cam index # parse repo into .cam/cam.db

Query the code graph

cam ls src/ # list files or symbols in a file cam read src/main.rs/main # read a single symbol body cam ref main --dir in # 1-hop callers (in) or callees (out)

Query & write to the memory tree

cam recall "how does hybrid recall fuse BM25 and vectors" cam add --summary "..." < notes.md

cam auto-initializes .cam/ on first use. Run cam watch in the background if you want live incremental graph updates as files change.

3. Connect MCP for the main agent

Add cam mcp to your host's MCP configuration (e.g. .cursor/mcp.json or .mcp.json):

{

"mcpServers": { "cam": { "command": "cam", "args": ["mcp"] } } }

See docs/mcp.md for host-specific config paths and docs/agents.md for main-agent / subagent prompt rules.

The first recall or add downloads the default embedding model (potion-multilingual-128M to ~/.cam/models/) and falls back to a lexical hash embedder if offline.


Key Features

FeatureDetails
SQLite Code GraphTree-sitter parses functions, types, and call edges into .cam/cam.db for instant symbol-level navigation
Virtual Pathssrc/main.rs addresses a file outline; src/main.rs/main addresses the symbol body directly
Live Incremental Synccam sync hashes file contents; cam watch keeps the graph updated on save
Hybrid Recall (RRF)Combines local dense embeddings (model2vec) + identifier-aware BM25 via Reciprocal Rank Fusion ($k=60$)
Revision Chains & Tree--parent builds branching topic hierarchies; --supersedes links linear revisions and folds older entries automatically
Ebbinghaus RetentionRetention $R = e^{-t / S}$ gently weights ranking ($\le 10\%$) and flags stale / needs_update memories without deleting history
Dual Interface (MCP + CLI)Main agent uses cam_* MCP tools; subagents run cam --json … in the shell
100% Local & PrivateZero external API keys; runs entirely on local SQLite and on-disk embeddings
9 LanguagesRust, Python, TypeScript, JavaScript, Go, Java, C, C++, C#

How It Works

  1. Extract — tree-sitter walks the workspace and stores symbol definitions and call edges in .cam/cam.db.
  2. Surgical Read — Agents navigate via virtual paths (ls → find → read → ref). Reading a file returns its symbol outline; reading file/symbol returns only that symbol's source slice.
  3. Hybrid Recall — recall takes an English query, embeds it locally, runs identifier-aware BM25 (splitting snake_case, kebab-case, camelCase + English stemming), prunes low-confidence tails, and fuses both lists with RRF ($k=60$) scaled by $0.9 + 0.1 \times R$. Superseded revisions are automatically expanded to their newest replacement.
  4. Write-Back — When an agent solves a non-trivial task, it extracts atomic facts so each add stores only one fact (English summary; body supports non-Latin characters). Related facts are connected via --link / links into a knowledge graph (inspected via mem show; recall does not traverse graph links yet). --parent attaches a memory to a structural topic; --supersedes replaces an outdated memory while preserving lineage.

Architecture deep dive (Chinese): DESIGN.md.


Agent Workflow

Every coding task follows the same four-step loop:

  1. Recall first — Query cam_recall / cam recall in English before grepping. Reuse a hit if it is latest and not needs_update.
  2. Ensure indexed — If graph tools return not_indexed, run cam_index / cam index once for the project.
  3. Walk the graph — On a memory miss, locate symbols with find or ls, read symbol bodies with read / , and trace callers/callees with ref --dir in|out.
  4. Write back — Persist newly discovered architecture facts or fixes with cam_add / cam add as atomic facts (one fact per memory, English summary; body supports non-Latin characters), linking related facts with links / --link.
RoleInterfaceTypical Usage
Main Agent (Chat / IDE session)MCP (cam mcp)Calls cam_recall, cam_find, cam_ls, cam_read, cam_ref, cam_add, cam_mem_tree, cam_mem_show directly
Subagent (Task / explore / CLI)CLI (cam --json)Runs cam --json --project in the shell (subagents rarely inherit MCP tools)

Full agent rules & copy-paste system prompts: docs/agents.md.


CLI Reference

Code Graph Commands
CommandDescription
cam indexParse the project with tree-sitter into .cam/cam.db (creates .cam/ if needed)
cam syncIncrementally update the graph for modified files using content hashes
cam watch [--debounce-ms N]Watch source files and auto-sync (--json streams one JSON report per sync)
cam ls [virt_path]List directories, files, or symbols inside a file
cam read [--full]Read a file's symbol outline, a specific symbol body (path/symbol), or the --full file
cam find [--kind K] [--file S] [--scope D] [--limit N]Case-insensitive substring search over symbol names (exact matches first, then prefixes)
`cam ref --dir in\out [--file S] [--kind K] [--scope D]`1-hop callers (in / --callers) or callees (out / --callees). Returns ranked candidates on ambiguous names
Memory Commands
CommandDescription

| cam recall " " [--limit N] [--no-expand] [--include-superseded] [--snippets] | Hybrid RRF recall (English query). --no-expand skips newest-revision expansion; --include-superseded keeps old revis

只显示了开头部分;其余内容请在仓库中阅读

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