Codegraph MCP

A self-use Rust MCP server that gives AI coding agents persistent code understanding and memory across sessions. Built as a personal tool to explore what happens when you give an LLM structured access to a codebase’s symbol graph, session state, and accumulated project knowledge.

What It Does

Codegraph runs as an MCP server (stdio transport) and exposes 26 tools that an AI agent can call:

• Code Graph — Parses source code with tree-sitter (Rust, TypeScript, JavaScript, Python, Go), extracts symbols and their relationships (calls, imports, inherits), and stores them as a directed graph. The agent can search symbols, traverse dependencies, and understand file structure without reading entire files.

• Session Memory — Tracks the agent’s current task, subtasks, decisions, and working context. Survives context window compaction so the agent can resume where it left off.

• Learning System — Records patterns (things that worked), failures (gotchas to avoid), and solution lineage (attempt chains with outcomes). A reflection engine converts outcomes into reusable knowledge. A suggestion system combines all three to recommend approaches for new tasks.

• Skill Distillation — Generates a SKILL.md from accumulated patterns, failures, and conventions — a machine-readable summary of project-specific knowledge.

• Cross-Language Inference — Detects REST/GraphQL calls in frontend code and matches them to backend route definitions.

• Bash Compression — Compresses verbose command output (git status, test results, directory listings) to reduce token usage.

Tech Stack

Architecture

src/
├── main.rs              # Entry point
├── config.rs            # Project root detection, config.toml
├── mcp/                 # MCP protocol layer
│   ├── protocol.rs      # JSON-RPC 2.0 + MCP types
│   ├── transport.rs     # Stdio transport
│   ├── server.rs        # Request dispatch, lazy init
│   └── tools.rs         # Tool registry (26 tools)
├── store/               # Persistence
│   ├── db.rs            # SQLite CRUD
│   ├── graph.rs         # In-memory petgraph
│   └── migrations.rs    # Schema versioning
├── code/                # Code analysis
│   ├── parser.rs        # tree-sitter symbol extraction
│   ├── indexer.rs       # Incremental indexing + cross-file resolution
│   ├── languages.rs     # Language configs + grammars
│   └── cross_language.rs
├── session/             # Session state machine
│   └── state.rs         # Task, decisions, context tracking
├── learning/            # Learning system
│   ├── patterns.rs      # Pattern storage + scoped queries
│   ├── failures.rs      # Failure records + severity
│   ├── confidence.rs    # Time decay + drift detection
│   ├── lineage.rs       # Solution attempt tracking
│   ├── reflection.rs    # Outcome → pattern/failure conversion
│   ├── niches.rs        # Behavioral clustering
│   └── sync.rs          # JSON export
├── skill/               # Skill distillation
│   ├── distill.rs       # Pattern → SKILL.md generation
│   ├── conventions.rs   # Convention clustering
│   └── render.rs        # Markdown rendering
└── compress/            # Token-saving output compression
    ├── bash.rs          # Command dispatch
    ├── git.rs           # Git output compression
    ├── test_output.rs   # Test result compression
    └── analytics.rs     # Savings tracking

Setup

1. Build

git clone https://github.com/subhankar-chowdhury/codegraph-mcp.git
cd codegraph-mcp
cargo build --release

The binary will be at target/release/codegraph.

2. Add to your MCP client

The server communicates over stdio (newline-delimited JSON-RPC 2.0) — your MCP client must connect via stdio transport, not HTTP/SSE. Add it to whichever MCP client you use:

Claude Code (~/.claude.json):

{
  "mcpServers": {
    "codegraph": {
      "command": "/absolute/path/to/codegraph-mcp/target/release/codegraph",
      "type": "stdio"
    }
  }
}

Cursor (.cursor/mcp.json in your project root):

{
  "mcpServers": {
    "codegraph": {
      "command": "/absolute/path/to/codegraph-mcp/target/release/codegraph",
      "type": "stdio"
    }
  }
}

Replace /absolute/path/to/codegraph-mcp with wherever you cloned the repo.

3. First run

When you start a session in any git repo, Codegraph will:

1. Auto-detect the project root (walks up looking for .git/)
2. Create a .codegraph/ directory with a default config.toml
3. Wait for you to index — run index_project(full: true) to build the code graph

After the initial index, subsequent sessions only need index_project() (incremental — skips unchanged files) or nothing at all if you haven’t changed code.

4. Configuration (optional)

Edit .codegraph/config.toml to customize:

[indexing]
exclude = ["node_modules", "target", ".git", "dist", "build", "__pycache__"]
max_file_size = 1048576  # 1 MiB

[learning]
decay_half_life = 90  # days

[cross_language]
enabled = true

Running tests

cargo test              # 87 tests
cargo bench             # criterion benchmarks

Benchmarks

Independently tested on a 111K-line Python codebase across 5 configurations: Vanilla (grep/read), Serena (LSP), Codegraph, Serena+Codegraph, and Codegraph with bad usage patterns. All numbers from actual measured runs.

Multi-Step Refactoring Task (The Real Test)

A realistic 10-step investigation task (read method, find callers, check tests, understand interfaces, produce refactoring plan). This is where per-query savings compound.

Same accuracy across all five configs. Pure cost difference. Compact mode is the default — the agent gets overviews first and requests source for specific symbols only when needed.

Per-Query Token Savings

Codegraph’s cost scales with answer size, not file size — the gap widens on larger codebases.

Learning System (10-Session Evolution Test)

10 sequential sessions with overlapping themes. Sessions 9-10 deliberately overlapped with earlier sessions to test knowledge compounding.

• Approach generation: Working. suggest_approach returned increasingly specific strategies across sessions. By sessions 9-10, it explicitly synthesized learnings from earlier sessions (“Leverage Session 1 + 2 + 4. Search for redis.RedisError catches.”).
• Pattern retrieval: Working. recall_patterns surfaces relevant patterns scoped by file paths and tags, with confidence scoring and time decay.
• Failure recall: Working. recall_failures always includes critical-severity failures and filters others by scope relevance.

See BENCHMARK.md for full methodology, all 5 configs, per-task breakdowns, and accuracy analysis.

What I Learned

This was a self-use project to explore the design space of “AI agent memory.” Some takeaways:

• Compact mode is everything. Codegraph with compact=true (the default) is 23% cheaper and 32% faster than vanilla grep/read. With include_source=true on whole files, it’s 64% worse. The value is in structured overviews, not dumping source through an extra protocol layer.
• The code graph’s value compounds over a session. On isolated single queries, fixed agent overhead buries the savings. On multi-step tasks with 18+ queries, per-query savings (26-281x) dominate.
• Session memory is genuinely helpful for long-running tasks that hit context limits, though its value shrinks as context windows grow.
• The learning system compounds. Approach generation noticeably improves over sessions as patterns accumulate. The “When X, do Y because Z” format produces actionable, reusable knowledge.
• MCP as a protocol works well for this kind of tool. Stdio transport is simple, the tool/resource model is clean, and lazy initialization (waiting for the client’s initialize handshake to resolve the project root) was the right call.

License

MIT