Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Local code graph and solution memory for coding agents via CLI and MCP.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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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:0.1.6Paste 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.
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.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account HanochZhu, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed –, and the latest version (0.1.6) reached the registry 7 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
In the same category: Context7, Codebase Memory, Exa
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
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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.
English · 中文 · 日本語 · 한국어 · العربية · 🌐 Website
Code graph + memory · surgical reads · 100% local · MCP + CLI
Kernel written in Rust
Guides: Interactive Showcase (Website) · Main Agent vs Subagent · MCP Configs by IDE · 中文说明
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:
| Plane | What it stores | How the agent uses it | |
|---|---|---|---|
| Code Graph | Tree-sitter symbols (functions, types, methods) as a virtual filesystem + 1-hop callers/callees | ls, find, read src/file.rs/symbol, `ref symbol --dir in\ | out` — surgical symbol reads instead of whole-file dumps |
| Memory Tree | Project facts, architecture decisions, debugging write-ups, and user habits | recall (Vector + BM25 fused with RRF) and add (with branching --parent and revision --supersedes) |
One binary, two interfaces: The main agent calls
camover MCP (cam mcp); subagents (which rarely inherit MCP tools) call the exact same CLI (cam --json …). Both share.cam/cam.dbon disk, 100% local, zero API cost.
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
| Environment | What to do |
|---|---|
| Cursor / Windsurf / Claude Code / Codex | Paste 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.
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 "$PWD:/work" -w /work ghcr.io/hanochzhu/codeagent-memory:0.1.6
Or build locally: docker build -t cam https://github.com/HanochZhu/codeagent_memory.git
cd your-project
cam index # parse repo into .cam/cam.db
cam ls src/ # list files or symbols in a file cam read src/main.rs/main # read a single symbol body cam ref main --dir in # 1-hop callers (in) or callees (out)
cam recall "how does hybrid recall fuse BM25 and vectors" cam add --summary "..." < notes.md
cam auto-initializes .cam/ on first use. Run cam watch in the background if you want live incremental graph updates as files change.
Add cam mcp to your host's MCP configuration (e.g. .cursor/mcp.json or .mcp.json):
{
"mcpServers": { "cam": { "command": "cam", "args": ["mcp"] } } }
See docs/mcp.md for host-specific config paths and docs/agents.md for main-agent / subagent prompt rules.
The first recall or add downloads the default embedding model (potion-multilingual-128M to ~/.cam/models/) and falls back to a lexical hash embedder if offline.
| Feature | Details |
|---|---|
| SQLite Code Graph | Tree-sitter parses functions, types, and call edges into .cam/cam.db for instant symbol-level navigation |
| Virtual Paths | src/main.rs addresses a file outline; src/main.rs/main addresses the symbol body directly |
| Live Incremental Sync | cam sync hashes file contents; cam watch keeps the graph updated on save |
| Hybrid Recall (RRF) | Combines local dense embeddings (model2vec) + identifier-aware BM25 via Reciprocal Rank Fusion ($k=60$) |
| Revision Chains & Tree | --parent builds branching topic hierarchies; --supersedes links linear revisions and folds older entries automatically |
| Ebbinghaus Retention | Retention $R = e^{-t / S}$ 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 & Private | Zero external API keys; runs entirely on local SQLite and on-disk embeddings |
| 9 Languages | Rust, Python, TypeScript, JavaScript, Go, Java, C, C++, C# |
tree-sitter walks the workspace and stores symbol definitions and call edges in .cam/cam.db.ls → find → read → ref). Reading a file returns its symbol outline; reading file/symbol returns only that symbol's source slice.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.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.
Every coding task follows the same four-step loop:
cam_recall / cam recall in English before grepping. Reuse a hit if it is latest and not needs_update.not_indexed, run cam_index / cam index once for the project.find or ls, read symbol bodies with read / , and trace callers/callees with ref --dir in|out.cam_add / cam add as atomic facts (one fact per memory, English summary; body supports non-Latin characters), linking related facts with links / --link.| Role | Interface | Typical 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.
| Command | Description | |
|---|---|---|
cam index | Parse the project with tree-sitter into .cam/cam.db (creates .cam/ if needed) | |
cam sync | Incrementally 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 |
| Command | Description |
|---|
| 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