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codeagent-memory

by HanochZhuversion 0.1.6i★ –iRuns on your computer
Registry nameio.github.HanochZhu/codeagent-memory

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

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

Run this in your terminal

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

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

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

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account HanochZhu, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7226 servers in this category
  • Published to the registry more recently than 83% of the other 7226 servers in this category
  • Basic checks equal to or better than 100% of the other 7226 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is codeagent-memory safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.6) reached the registry 7 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago
Install

Codebase Memory

by DeusData

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

Runs on your computer★ 48,148MITUpdated 20 days ago
Install

Exa

by exa-labs

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

Runs in the cloud★ 5,213MITUpdated 44 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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

Only the beginning is shown; read the rest in the repo

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