Kodegraf

Kodegraf

A knowledge graph that makes AI coding assistants actually understand your codebase.

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Your AI assistant doesn't understand your codebase. It reads files, guesses at function names, invents import paths, and hallucinates enum values. Then you fix its mistakes. Then it makes them again next session.

Kodegraf gives it a memory. It parses your code into a queryable graph — every function signature, every import path, every type definition — and serves that knowledge to your AI through MCP. Your assistant stops guessing. It looks things up.

Built in Rust. Parses 37,000 files in 46 seconds. Searches in 23ms. Zero runtime dependencies.

Install

cargo install kodegraf-cli kodegraf-mcp

Set Up (once per project)

cd your-project
kodegraf install

This single command handles everything: parses your codebase, builds the graph, registers the MCP server, and configures your editor. Open Claude Code (or Cursor, Windsurf, Zed) and start working.

What Happens Next

When your AI assistant needs to find a function, it queries the graph instead of grepping thousands of files. When it writes code, Kodegraf validates the imports, function calls, and enum values against what actually exists. When you switch branches, the graph updates automatically.

You: "How is authentication implemented?"

Without Kodegraf:                         With Kodegraf:
  Grep("auth") → 200+ matches              kodegraf_find("auth") → 3 results
  Read 8 files → 25K tokens                 Exact signatures, file:line
  Guesses import path → wrong               Verified import path → correct
  3 fix cycles                              Works first try

17 Tools

Tool	What it does
kodegraf_find	Find functions, classes, types by name with signatures
kodegraf_search	Full-text search across all symbols (FTS5 + porter stemmer)
kodegraf_deps	What does this file import?
kodegraf_dependents	What imports this file?
kodegraf_impact	Blast radius — what breaks if you change this?
kodegraf_check	Validate code against the graph (DiffGuard)
kodegraf_exports	List a file's exported symbols
kodegraf_enum_values	Get valid enum values (never guess)
kodegraf_context	~100 token summary with next-step suggestions
kodegraf_query	Trace callers, callees, imports, tests for any symbol
kodegraf_changes	Risk-scored git diff with test gap detection
kodegraf_flows	Execution flows sorted by criticality
kodegraf_affected_flows	Which user-facing paths are impacted by changes?
kodegraf_large	Find oversized functions for decomposition
kodegraf_dead_code	Find unreferenced code
kodegraf_build	Build or update the graph
kodegraf_insights	Quality metrics and failure patterns

DiffGuard

Every time your AI writes code, Kodegraf checks it against the graph:

• Does the import path resolve to a real file?
• Does the function being called actually exist?
• Does the function signature match (parameter count, types)?
• Is the enum value valid?

Errors are caught before they reach your PR. Failures are recorded, patterns are detected, and prevention rules are generated automatically. The system gets smarter every session.

Performance

Tested on a production monorepo with 37,000 source files:

Metric	Value
Full build	46 seconds
Incremental update	191ms
Search latency (avg)	23ms
Search latency (P99)	200ms
Symbols indexed	390,000
Dependency edges	2.5 million

Parallel parsing with 8 threads. SQLite with WAL mode, FTS5 full-text search, 64MB cache. All queries use prepared statements.

Languages

TypeScript, JavaScript, Python, Go, Rust, Java, Ruby, C, C++, C#, Scala.

How It Works

flowchart LR
    A[Codebase] --> B[Build] --> C[Graph] --> D[MCP Server] --> E[AI Assistant] --> F[DiffGuard]

    style A fill:#e8e8e8,color:#000,stroke:#999
    style B fill:#4dabf7,color:#fff,stroke:#339af0
    style C fill:#51cf66,color:#fff,stroke:#40c057
    style D fill:#845ef7,color:#fff,stroke:#7048e8
    style E fill:#ff922b,color:#fff,stroke:#f76707
    style F fill:#ffd43b,color:#000,stroke:#fab005

Codebase → parsed with tree-sitter → Graph (SQLite, 390K nodes) → served via MCP (17 tools, 23ms) → used by AI Assistant → validated by DiffGuard (12 checks) → graph auto-updates on every edit

How It Integrates

kodegraf install sets up five layers automatically:

1. MCP Server — registers with your AI tool via .mcp.json
2. Instructions — prepends CLAUDE.md with "use Kodegraf tools first"
3. Hooks — auto-updates graph after every file edit (191ms, silent)
4. Skills — workflow templates for explore, review, debug, refactor
5. Auto-build — graph rebuilt on session start if empty or stale

Works with Claude Code, Cursor, Windsurf, Zed, and Continue.

CLI

kodegraf install            # Set up everything (one command)
kodegraf build              # Full graph build
kodegraf update             # Incremental update
kodegraf update --fast      # Hook mode (silent, <200ms)
kodegraf find <query>       # Find a symbol
kodegraf deps <file>        # File dependencies
kodegraf dependents <file>  # Reverse dependencies
kodegraf impact             # Blast radius of current changes
kodegraf check <file>       # Validate a file (DiffGuard)
kodegraf eval               # Run benchmarks
kodegraf status             # Graph statistics

Architecture

Four Rust crates. Two binaries. One SQLite database.

Crate	What it does
kodegraf-core	Graph store, tree-sitter parser, FTS5 search, flow detection, signals
kodegraf-checks	DiffGuard — 12 validation rules with fuzzy matching
kodegraf-cli	CLI binary with all commands
kodegraf-mcp	MCP server (rmcp SDK, 17 tools, stdio transport)

Development

cargo build --workspace        # Build
cargo test --workspace         # 112 tests
cargo clippy --workspace       # Lint

Contributing

See CONTRIBUTING.md.

Security

See SECURITY.md. Kodegraf is local-only — no network calls, no telemetry, no cloud.

License

MIT