codeagent-memory MCPサーバー

cam

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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

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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:

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 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.

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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.

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.

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Option 2: Manual Setup

1. Install the CLI

Requires a Rust toolchain:

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

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.

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Key Features

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#

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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.

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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 <file>/<symbol>, and trace callers/callees with ref <symbol> --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.

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 <dir> <cmd> in the shell (subagents rarely inherit MCP tools)

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

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CLI Reference

Code Graph Commands

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 <virt_path> [--full]	Read a file's symbol outline, a specific symbol body (path/symbol), or the --full file
cam find <query> [--kind K] [--file S] [--scope D] [--limit N]	Case-insensitive substring search over symbol names (exact matches first, then prefixes)
cam ref <symbol> --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

Command	Description
cam recall "<query>" [--limit N] [--no-expand] [--include-superseded] [--snippets]	Hybrid RRF recall (English query). --no-expand skips newest-revision expansion; --include-superseded keeps old revisions; --snippets clips long bodies below rank 1
cam add --summary "..." [--parent ID] [--supersedes ID] [--link ID]... [--body TEXT | --file PATH]	Store one atomic fact as a memory (English summary required; body supports non-Latin characters; via --body, --file, or stdin). --link connects related facts in the knowledge graph; --parent for topic hierarchy; --supersedes to replace an older memory
cam mem tree	Render the memory hierarchy and revision chains
cam mem show <id>	Inspect a single memory entry in full

Server & Configuration Commands

Command	Description
cam mcp	Start the stdio MCP server for the main agent (see docs/mcp.md)
cam status	Show resolved project root, database path, node/edge/memory counts, and active config
cam config get	Print current ~/.cam/config.toml settings
cam config set stale_days <N>	Set age threshold (in days) for marking memories stale
cam config set embedding_model <model>	Set Hugging Face model2vec repo, local model directory, hash, or default

Global flags & conventions:

• --project <dir> (or CAM_PROJECT env var) overrides automatic upward .cam / .git root detection.
• --json emits compact JSON (--pretty for indented JSON). Errors on --json print {"error":{"code":…,"message":…}} to stdout and exit non-zero.
• Ambiguous symbols: When read or ref matches multiple definitions, cam returns status: "ambiguous" with candidate node IDs (e.g. src/main.rs:function:main:12) that can be passed directly back to read or ref.
• Ignore rules & multi-repo directories: Honors .gitignore and .camignore at any depth. A nested directory with its own .git (standalone clone, submodule, or linked worktree) is indexed into that directory's .cam/cam.db. The parent database keeps a catalog of those graphs and queries fan out into them, so opening the nested directory reuses the graph. cam ls lists them as kind: "project" (passable to --scope).

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Memory Rules

• Atomic facts & knowledge graph links: Store only one atomic fact per memory to avoid packing excessive information into a single entry. When multiple facts are related, link them with --link / links to form a knowledge graph (graph recall is not enabled yet).
• English queries and summaries: Recall queries and memory summaries must be written in English. Memory bodies support non-Latin characters.
• Retention & reinforcement: Ebbinghaus retention $R = e^{-t / S}$ adjusts recall score via $\text{relevance} \times (0.9 + 0.1 \times R)$. Entries with $R < 0.3$ are marked needs_update; entries older than stale_days (default 30) are marked stale. Every valid recall hit (needs_update = false) refreshes last-accessed time and multiplies stability $S$ by 1.7.
• Non-destructive lineage: Memories are never deleted. --parent forms branching trees; --supersedes forms linear revision chains where only the tail is marked latest and older revisions are folded out of default recall results.

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Configuration

Global settings live in ~/.cam/config.toml:

stale_days = 30
# embedding_model = "minishlab/potion-multilingual-128M" # default (cached under ~/.cam/models/)
# embedding_model = "minishlab/potion-base-8M"           # lighter Hugging Face model2vec repo
# embedding_model = "/path/to/local/model2vec"           # dir with tokenizer.json, config.json, model.safetensors
# embedding_model = "hash"                               # zero-download lexical hash embedder

Use CAM_EMBED_MODEL to override the model for a single process, or cam config set embedding_model default to reset. When the embedding model changes, the next recall automatically re-embeds existing memories before scoring.

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Benchmarks

cam evaluates its code graph and memory planes separately against the closest open-source systems on identical corpora:

Plane	Baseline / Analogue	Benchmark Corpus	Headline Takeaway
Memory	agentmemory hybrid + tokenized grep	coding-agent-life-v1	1.000 R@5 / 0.240 P@5 (matches hybrid ceiling, 1.000 newest R@5 on revision chains)
Long-term Memory	BM25 / dense retrievers	LongMemEval-S	0.909 R@5 / 0.980 Hit@5 / 0.840 MRR with potion-multilingual-128M
Code Graph	codegraph v0.9.6	LongMemCode (clap)	0.769 vs 0.768 on supported 1-hop tasks, P95 ≈ 5.2 ms, $0
Workflow Retrieval	Lexical / BM25 file retrieval	Agent Retrieval Bench V2 edit2ripple	0.297 MRR (best first-hit rank, P95 7.3 ms)
Multi-turn Tokens	Full context dump	DeepSeek multi-turn	57% fewer tokens on memory track, 98% fewer tokens on code track

General chat memory frameworks (Mem0, Zep, Letta) target conversational logs rather than these coding-agent corpora and are omitted from the tables below.

System Comparison

	cam	agentmemory	codegraph
What it stores	Code graph + memory tree	Session / chat memories	Code graph
Query primitives	recall / ls / find / read / ref	smart-search / remember	explore / callers / callees
Interface	MCP + CLI	MCP + REST + hooks	MCP + CLI
Ranking / Fusion	Vector + BM25 + RRF + retention	BM25 + embed + rerank	FTS5 + graph walk
Cost	100% local ($0)	Local server	Local

1. Memory — coding-agent-life-v1

15 sessions, 15 queries, $k=5$ (same scoring as agentmemory score.ts; P@5 ceiling is 0.240).

System	Hit Rate	R@5	P@5	Source
grep (tokenized substring)	15 / 15	0.967	0.227	this repo, 2026-09-23
cam min-max sum (legacy)	15 / 15	0.933	0.213	historical, hash embed
cam RRF	15 / 15	1.000	0.240	this repo, hash embed
agentmemory hybrid	15 / 15	1.000	0.240	published v0.9.26

Revision chains (--lineage): Re-ingesting every turn as a --supersedes revision tests whether top-$k$ returns the newest node of each gold chain (newest R@k) and whether any superseded node falsely claims latest (stale latest).

k	R@k	P@k	newest R@k	stale latest
5	1.000	0.240	1.000	0
10	1.000	0.120	1.000	0

2. Long-Term Memory — LongMemEval-S

100 non-abstention questions sampled with seed 42 from cleaned LongMemEval-S. Retrieval fuses embeddings + BM25 with RRF at $k=10$ without an LLM reader.

Embedder	R@1	R@5	R@10	Hit@1	Hit@5	Hit@10	MRR	Retrieval P50 / P95
hash (offline test)	0.382	0.803	0.922	0.580	0.910	0.970	0.718	24.1 / 35.5 ms
potion-multilingual-128M	0.474	0.909	0.952	0.740	0.980	0.990	0.840	1.12 / 1.34 s

3. Workflow Retrieval — Agent Retrieval Bench V2 (edit2ripple)

58 anchored-change queries across 44 frozen repository snapshots (no LLM; BCY = Budgeted Context Yield under the canonical 8K-token file-packing protocol).

System	Recall@5	Recall@10	Recall@20	MRR	BCY@8k
cam graph	0.300	0.335	0.378	0.297	0.300
Lexical	0.412	0.536	0.588	0.243	0.447
BM25	0.207	0.287	0.499	0.154	0.243

cam graph achieves the highest first-hit rank (MRR 0.297, query P95 7.3 ms), while lexical search provides broader multi-file coverage — motivating graph + lexical rank fusion.

4. Code Graph — LongMemCode (clap)

clap v4.6.1, 536 scenarios against SCIP gold (no LLM, P95 ≈ 5.2 ms, $/1k = 0).

Slice	n	cam	codegraph 0.9.6
Supported (1-hop)	478	0.769	0.768
lookup / file_symbols	293 / 55	0.808 / 0.915	0.808 / 0.915
callers / callees	67 / 18	0.397 / 0.811	0.355 / 0.867
implementors	40	0.119	0.094
Raw / Weighted (all 536)	536	0.709 / 0.704	0.704 / 0.700

5. Multi-Turn Token Savings — DeepSeek

Full context dump vs. per-turn cam retrieval (deepseek-flash, hash embedder, 2026-10-13):

Track	n	Full Acc.	cam Acc.	Full Tokens	cam Tokens	Token Saving
Memory (coding-agent-life)	15	1.00	0.87	22,796	9,904	57% (1,471 → 616 / turn)
Code (this repo indexed)	6	0.83	0.33	379,066	6,342	98% (63,076 → 1,000 / turn)

6. Memory writing — who decides what one memory is

The notes are a separate case set, cam-write-memory-eval. This repo keeps the runner. The first eval/write_memory/run.py downloads cases.json to eval/write_memory/data/ (override with CAM_EVAL_WRITE_MEMORY). That is the same split as LongMemEval and LoCoMo: the corpus stays outside, and the script here fetches it when you run the benchmark.

A model receives one note and calls cam_add. legacy asks for one full write-up. split asks for one fact, or one problem's solution path, and links pieces from the same solution with #0. A fact counts as exclusive when one stored memory contains every required phrase and no other gold fact. The notes are public behavior of this repository: recall fusion, the stale-binary restart notice, retention, and read versus delete. Labels and source files are in the case-set README.

Dataset paths are configured in eval/datasets.toml (coding-agent-life-v1 defaults to ../agentmemory or CAM_EVAL_CODING_LIFE).

python3 eval/coding_life/run.py --adapter grep
python3 eval/coding_life/run.py                                             # potion; errors if the model fails
python3 eval/coding_life/run.py --hash-embed                                # hash embedder
python3 eval/longmemeval/run.py --sample 100 --seed 42 --workers 8          # potion
python3 eval/longmemeval/run.py --sample 100 --seed 42 --workers 8 --hash-embed
python3 eval/locomo/run.py --workers 4                                      # potion, evidence recall

git clone --depth 1 https://github.com/eyuansu62/agent-retrieval-bench \
  eval/agent_retrieval/vendor/agent-retrieval-bench
python3 -m pip install -e eval/agent_retrieval/vendor/agent-retrieval-bench
arb download-benchmark --version v2_edit2ripple --local-dir eval/agent_retrieval/data --force
python3 eval/agent_retrieval/run.py --workers 8

python3 eval/llm_multiturn/run.py --track both   # potion; requires DEEPSEEK_API_KEY
python3 eval/write_memory/test_score.py          # downloads the case set; no model call
python3 eval/write_memory/run.py                 # downloads the case set; DEEPSEEK_API_KEY
python3 eval/longmemcode/run.py --corpus clap
python3 eval/charts/generate.py

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