Codemesh

The Problem

AI coding agents waste 40-80% of their tokens on discovery — grepping through files, reading irrelevant code, and rebuilding context they've already seen in previous sessions.

On a 600-file codebase, a typical exploration task involves 10+ file reads before the agent even knows what's relevant.

Before:  Agent → Grep → 50 matches → Read 10 files → Understand → Work
After:   Agent → codemesh_explore → 3 relevant files → codemesh_trace → full path → Work

Codemesh is an MCP server that gives agents a persistent, queryable knowledge graph. The graph gets smarter over time: agents write back what they learn, so the next session starts informed.

Benchmarks

Benchmarked on 6 real-world codebases (Alamofire, Excalidraw, VS Code, Swift Compiler, pydantic-validators, pydantic-basemodel) with Claude Sonnet 4.6, compared alongside baseline and graph-based approaches for context.

Full methodology, per-repo breakdowns, and pairwise comparisons: docs/benchmark-results.md | Early pydantic evals

Cost

Time

Tool calls (agent turns)

Quality (1–10, LLM-as-judge)

Cost savings: Codemesh MCP vs Baseline

[!NOTE] Codemesh MCP achieves the lowest cost and fastest time of any mode tested — 71% cheaper and 72% faster than baseline on average across 6 repos, using 82% fewer tool calls (8 vs 45). Quality is comparable to baseline (7.9 vs 7.0); Codegraph edges Codemesh on quality (8.4) but at roughly double the cost ($0.45 vs $0.22). Every repo shows cost and time savings — including the comprehension-heavy queries (Excalidraw, pydantic-basemodel) that regressed in prior builds of codemesh.

Quick Start

1. Install

npm install -g @pyalwin/codemesh

Or run directly without installing:

npx -y @pyalwin/codemesh --help

git clone https://github.com/pyalwin/codemesh.git
cd codemesh
npm install && npm run build
npm link

Verify the install: codemesh --version should print the package version.

2. Index your project

cd /your/project
codemesh index --with-embeddings

Indexed 656 files
  Symbols found:  16733
  Edges created:  33266
  Duration:       10009ms
  PageRank:       13843 nodes scored
  Embeddings:     13187 symbols embedded

3. Choose your mode

Codemesh offers two ways to integrate with AI agents:

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/path/to/your/project"
      }
    }
  }
}

export CODEMESH_PROJECT_ROOT=/path/to/your/project

# Primary — one-call question answering:
codemesh explore answer "How does request handling work?"

# Follow-up commands:
codemesh explore search "request flow"
codemesh explore context Source/Core/Session.swift Source/Core/Request.swift
codemesh explore trace Session.request --depth 5
codemesh explore semantic "network request handling"  # requires --with-embeddings

Which mode should I use?

4. Use it

The agent now has 6 new tools. Query the graph before reading code:

You: "Find how pydantic handles validation"

Agent calls: codemesh_answer({ question: "How does pydantic handle validation?" })
       gets: 9 relevant files ranked by PageRank, call chains, 
             git hotspots, co-change relationships, 5 suggested reads

Agent calls: Read("pydantic/functional_validators.py", lines 1-50)
       reads: only the specific lines suggested by the answer tool

Agent calls: codemesh_enrich({ path: "pydantic/functional_validators.py",
               summary: "Primary V2 validator API..." })
       saves: summary for next session

Client Integrations

Codemesh speaks the Model Context Protocol, so any MCP-compatible client can use it. Paste one of the snippets below, restart the client, and the six codemesh_* tools show up in the agent's toolbox.

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
      }
    }
  }
}

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
      }
    }
  }
}

{
  "mcpServers": {
    "codemesh": {
      "command": "npx",
      "args": ["-y", "@pyalwin/codemesh"],
      "env": {
        "CODEMESH_PROJECT_ROOT": "${workspaceFolder}"
      }
    }
  }
}

{
  "experimental": {
    "modelContextProtocolServers": [
      {
        "transport": {
          "type": "stdio",
          "command": "npx",
          "args": ["-y", "@pyalwin/codemesh"],
          "env": {
            "CODEMESH_PROJECT_ROOT": "/absolute/path/to/your/project"
          }
        }
      }
    ]
  }
}

Agent Write-Back: the graph that gets smarter

Every other code-intelligence tool indexes your repo once and hands the agent a read-only view. Codemesh lets the agent teach the graph as it works — summaries, workflows, and cross-concept links persist across sessions and survive re-indexing.

// Session 1 — agent reads unfamiliar code, then writes back what it learned.
codemesh_enrich({
  path: "pydantic/functional_validators.py",
  summary: "Primary V2 validator API. `@field_validator` wraps "
         + "`_decorators.FieldValidatorDecoratorInfo`; `mode='before'|'after'` "
         + "toggles pre/post-coercion execution. Extends BaseValidator.",
  concepts: ["validation", "decorators", "v2-api"]
})

// Session 1 — agent traces a multi-file flow, records the path.
codemesh_workflow({
  name: "pydantic field validation",
  description: "Request → BaseModel.__init__ → SchemaValidator → field_validator",
  files: [
    "pydantic/main.py",
    "pydantic/_internal/_model_construction.py",
    "pydantic/functional_validators.py"
  ]
})

// Session 2 (days later) — same question, different agent instance.
codemesh_answer({ question: "How does pydantic validate fields?" })
// → returns the enriched summary AND the 3-file workflow from Session 1
//   before the agent reads a single line. Zero rediscovery cost.

The graph now knows things no static analyzer could infer: why a file matters, which files move together, what a maintainer called a concept. Re-indexing rebuilds the structural layer (files, symbols, imports, calls) but preserves every enrichment — entries only go stale when their referenced files change.

See codemesh_enrich and codemesh_workflow under MCP Tools.

How It Works

                      ┌──────────────────────────────────┐
                      │         Knowledge Graph           │
                      │                                   │
                      │  ┌──────────┐ ┌───────────────┐  │
                      │  │Structural│ │   Semantic     │  │
                      │  │  (auto)  │ │   (agents)    │  │
                      │  │          │ │               │  │
                      │  │ files    │ │ summaries     │  │
                      │  │ symbols  │ │ workflows     │  │
                      │  │ imports  │ │ concepts      │  │
                      │  │ calls    │ │ enrichments   │  │
                      │  └──────────┘ └───────────────┘  │
                      │                                   │
                      │  ┌──────────┐ ┌───────────────┐  │
                      │  │   Git    │ │   Search      │  │
                      │  │  Intel   │ │               │  │
                      │  │          │ │ FTS5 (exact)  │  │
                      │  │ hotspots │ │ Trigram (fuzzy)│  │
                      │  │ co-change│ │ LanceDB (sem) │  │
                      │  │ churn    │ │ PageRank      │  │
                      │  └──────────┘ └───────────────┘  │
                      │                                   │
                      │        SQLite + LanceDB           │
                      └────────────┬──────────────────────┘
                                   │
                      ┌────────────┴──────────────────────┐
                      │    MCP Server / CLI (7 tools)      │
                      │                                    │
                      │  answer · explore · trace          │
                      │  enrich · workflow · status         │
                      └────────────────────────────────────┘

Structural layer (automatic) — Tree-sitter parses your code into files, symbols (functions, classes, methods), and relationships (imports, calls, extends). Rebuilt on each index.

Semantic layer (agent-built) — As agents work with your code, they write back summaries and workflow paths. These survive re-indexing and accumulate across sessions. Invalidated when referenced files change.

MCP Tools

CLI

codemesh index                          # structural + git intel + pagerank
codemesh index --with-embeddings        # + semantic vectors (~80MB model, zero API cost)
codemesh status                         # graph statistics
codemesh rebuild                        # purge and re-index

codemesh explore answer "question"      # one-call context assembly (PRIMARY)
codemesh explore search "query"         # FTS5 + trigram + semantic search
codemesh explore context file1 file2    # multi-target context
codemesh explore trace symbol --depth 5 # follow call chains
codemesh explore semantic "query"       # vector similarity (needs embeddings)
codemesh explore impact file            # reverse dependencies

Optional: Hooks & Skills

# Install the skill so Claude Code loads the workflow automatically
cp /path/to/codemesh/skills/codemesh.md /your/project/.claude/skills/

{
  "hooks": {
    "pre_tool_use": [{
      "matcher": "Read",
      "command": "/path/to/codemesh/hooks/pre-read.sh"
    }],
    "post_tool_use": [{
      "matcher": "Read",
      "command": "/path/to/codemesh/hooks/post-read.sh"
    }]
  }
}

Supported Languages

Any language with a tree-sitter grammar can be added.

Graph Data Model

Nodes

Edges

Architecture

codemesh/
├── src/
│   ├── index.ts              # MCP server entry (stdio transport)
│   ├── server.ts             # Tool registration (zod schemas)
│   ├── graph/
│   │   ├── types.ts          # Node/edge type definitions
│   │   ├── storage.ts        # StorageBackend interface (swappable)
│   │   └── sqlite.ts         # SQLite + FTS5 implementation
│   ├── indexer/
│   │   ├── indexer.ts        # File walking, hashing, incremental indexing
│   │   ├── parser.ts         # Tree-sitter AST extraction
│   │   └── languages.ts      # Language registry (ext → grammar)
│   ├── tools/                # 6 MCP tool handlers
│   └── cli.ts                # CLI entry point
├── skills/codemesh.md        # Agent education skill
├── hooks/                    # Pre/post read hooks
└── eval/                     # Eval framework (5 tasks, 3 models)

Storage is backend-agnostic. The StorageBackend interface abstracts all persistence. v1 uses SQLite with FTS5 for zero-dependency local operation. The interface supports swapping to Memgraph, Neo4j, or other graph databases.

Eval Framework

Reproducible evaluation harness with LLM-as-judge scoring:

# Setup
npm install -g @pyalwin/codemesh
git clone --depth 1 https://github.com/Alamofire/Alamofire.git /tmp/alamofire
# ... clone other repos ...

# Index
CODEMESH_PROJECT_ROOT=/tmp/alamofire codemesh index

# Run benchmarks
python3 eval/head_to_head.py --model sonnet alamofire excalidraw vscode swift-compiler

See docs/benchmark-results.md for full methodology and results. Early pydantic evals are archived in docs/experiments/.

vs. Existing Tools

Development

bun install          # Install dependencies
bun run build        # Compile TypeScript
bun run test         # Run 102 tests
bun run dev          # Watch mode
bun run lint         # Type check

Contributing

Contributions welcome. Areas for improvement:

• More languages — Add tree-sitter grammars and language-specific extractors
• AST-diff invalidation — Function-level instead of file-level staleness detection
• Graph backends — Memgraph/Neo4j adapters for StorageBackend
• Semantic search — Embedding columns alongside FTS5
• Agent adoption — Better patterns for agents to prefer graph tools naturally

License

MIT