knot

knot is a high-performance codebase indexer that extracts structural and semantic information from source code, enabling AI agents to understand, analyze, and navigate large code repositories. Currently supports Java, Kotlin (v0.7.4+), TypeScript, JavaScript/Node.js, HTML, and CSS/SCSS with full cross-language linking, with planned support for Rust and C/C++.

The indexer automatically builds:

• Vector Search Database (Qdrant) — semantic understanding via embeddings

• Graph Database (Neo4j) — architectural relationships via call graphs

This dual-database approach powers both:

• MCP (Model Context Protocol) Server — Exposes three tools to any LLM client (Claude, Gemini, ChatGPT, Cursor, etc.)

• CLI Tool (v0.8.0+) — Standalone knot command for terminal and scripting environments

✨ Key Features

🔍 Code Intelligence Tools

• search_hybrid_context: Semantic + structural search. Find code by meaning, class name, method signature, docstrings, or comments. Returns full context including dependencies.

• find_callers: Reverse dependency lookup. Identify dead code, perform impact analysis, or understand the full call chain of any function/method.

• explore_file: File anatomy inspection. Quickly see all classes, interfaces, methods, and functions in a file with signatures and documentation.

🏗️ Multi-Language Support

• Java: Full AST extraction with package awareness

• Kotlin (v0.7.4+): Complete support for Kotlin codebases with classes, interfaces, objects, companion objects, functions, methods, and properties. Fully compatible with tree-sitter-kotlin-ng grammar.

• TypeScript/TSX/CTS: Complete support for modern JavaScript/TypeScript codebases, including CommonJS TypeScript files

• JavaScript/Node.js (v0.7.4+): Vanilla JS, Node.js, and module systems (.js, .mjs, .cjs, .jsx)

• Hybrid Web Ecosystem (v0.6.5): Cross-language linking between JavaScript, HTML, and CSS for full-stack SPA analysis

• HTML (v0.6.3+): Custom elements (Web Components, Angular), id and class attribute indexing for cross-language CSS search

• JSX/TSX Attributes (v0.6.3+): Extracts id and className from React components for unified HTML/CSS discovery

• CSS/SCSS (v0.6.4+): Stylesheet indexing with class/ID selector extraction and variable tracking (CSS/SCSS variables, mixins, functions)

• Rust (Planned v0.8.x): Struct, trait, and macro analysis

• C/C++ (Planned v0.9.x): Pointer relationships and macro analysis

📚 Rich Comment Extraction

• Captures docstrings (JavaDoc, JSDoc) preceding declarations

• Extracts inline comments within method/function bodies

• Respects nesting boundaries (class comments don't capture method comments)

• Intelligently aggregates comment blocks

📊 Dual-Database Architecture

• Qdrant: Vector search for semantic code understanding

• Neo4j: Graph relationships for structural navigation

🚀 High Performance

• Parallel Streaming Pipeline: Overlaps CPU-bound embedding with I/O-bound ingestion via MPSC channels (v0.5.0+)

• Incremental Indexing: Uses SHA-256 hashes to skip unchanged files

• Real-time Watch Mode: Automatically re-indexes changed files in seconds via --watch

• CPU Parallelism: AST extraction via Rayon

• Scalable: Configurable batch processing and constant memory footprint (~2GB) regardless of repository size

Related MCP server: MCP-RAG

🛠️ Installation

Prerequisites

Option A: Pre-compiled Binaries (macOS & Modern Linux)

Go to the Releases page and download the native executable for your platform.

Install via Shell Script (macOS & Linux):

curl --proto '=https' --tlsv1.2 -LsSf https://github.com/raultov/knot/releases/latest/download/knot-installer.sh | sh && curl -sO https://raw.githubusercontent.com/raultov/knot/master/.knot-agent.md

This one-liner installs the knot binary and downloads the .knot-agent.md skill file to your current directory for use with AI agents and LLM-based code analysis tools.

Linux Requirements:

• Full install (knot-indexer + CLI + MCP): glibc 2.38+

  • Ubuntu 24.04 LTS or later

  • Debian 13 (Trixie) or later

  • Fedora 39+ / RHEL 10+

  • Arch Linux (rolling release)

• Lightweight clients-only (knot CLI + MCP server, no indexing): glibc 2.35+ (even older systems like Debian 12 Bookworm work fine)

For older Linux distributions or Windows, see the Lightweight Clients section below or use Docker (Option B).

Option B: Docker (Universal Compatibility)

Docker images provide universal compatibility for any Linux distribution and Windows.

Full Install (All Binaries: knot-indexer, knot CLI, knot-mcp)

Build the image:

docker build -t knot:latest . --network=host

Run the indexer:

# Use --network host to connect to databases running on your host machine
docker run --rm \
  -v /path/to/your/repo:/workspace \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot-indexer

Run the CLI tool:

docker run --rm \
  -v /path/to/your/repo:/workspace \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot search "user login flow"

Run the MCP server:

docker run --rm \
  -e KNOT_REPO_PATH=/workspace \
  -e KNOT_NEO4J_PASSWORD=your-password \
  --network host \
  knot:latest \
  knot-mcp

Note: Uses Debian Trixie (glibc 2.38+) and includes ONNX Runtime for full functionality.

Lightweight Clients (Only knot CLI + knot-mcp, No Indexer)

For older systems (Debian 12 Bookworm, Ubuntu 22.04) or production deployments that only need to query existing indexes without indexing new code:

Build the lightweight image:

docker build -t knot:clients -f Dockerfile.clients . --network=host

Image size: ~100MB (vs ~160MB for full install)

Run the CLI tool (query existing index):

docker run --rm \
  --network host \
  knot:clients \
  knot callers "MyClass"

Run the MCP server:

docker run --rm \
  --network host \
  knot:clients \
  knot-mcp

Available tools in lightweight mode:

• ✅ knot search (structural only, no semantic search)

• ✅ knot callers (reverse dependency lookup)

• ✅ knot explore (file structure inspection)

• ❌ Semantic search requires the full install

Note: Uses Debian Bookworm (glibc 2.35+) and excludes ONNX Runtime, making it compatible with older Linux distributions.

Option C: Install via Cargo

cargo install --git https://github.com/raultov/knot

Option D: Build from Source

Full Install (All Binaries):

1. Start infrastructure with Docker:

docker compose up -d

2. Clone and build:

git clone https://github.com/raultov/knot
cd knot
cargo build --release

3. Configure:

cp .env.example .env
$EDITOR .env  # Set KNOT_REPO_PATH and Neo4j credentials

4. Index a codebase:

./target/release/knot-indexer

5. Query via CLI:

./target/release/knot search "your query"

Option E: Lightweight Clients (No Indexing)

For older Linux distributions (e.g. Debian 12 Bookworm, Ubuntu 22.04) or production deployments where you only need the CLI and MCP server (not the indexer), compile without the embedding dependencies:

Build lightweight clients:

cargo build --release --no-default-features --features only-clients

This produces only knot and knot-mcp binaries (~8-10 MB each), excluding the 30+ MB of ONNX Runtime dependencies.

Available tools in lightweight mode:

• ✅ find_callers: Reverse dependency lookup (graph search)

• ✅ explore_file: File structure inspection

• ❌ search_hybrid_context: Semantic search (requires embeddings, not available in this mode)

Use case: Query an existing Qdrant + Neo4j index that was built elsewhere, without needing the indexer on your machine.

Docker alternative (for lightweight mode):

docker build -t knot:clients-only -f Dockerfile -f - . << 'EOF'
FROM rust:1.90-slim-bookworm AS builder
WORKDIR /build
COPY . .
RUN cargo build --release --no-default-features --features only-clients
FROM debian:bookworm-slim
COPY --from=builder /build/target/release/knot* /usr/local/bin/
CMD ["knot-mcp"]
EOF

6. Start the MCP server:

./target/release/knot-mcp

📖 Usage

📥 Download Agent-Skills Documentation

Get comprehensive guides for using knot CLI:

# Install all agent-skills documentation in one command:
bash scripts/install-agent-skills.sh

# Or download and install directly:
curl -fsSL https://raw.githubusercontent.com/user/knot/master/scripts/install-agent-skills.sh | bash

# Specify custom location:
bash scripts/install-agent-skills.sh ~/.config/knot/docs

This downloads:

• search.md — Semantic code discovery guide with examples

• callers.md — Reverse dependency lookup with critical usage rules

• explore.md — File anatomy inspection guide

• workflows.md — Common patterns and best practices

For quick reference without downloading, see .knot-agent.md.

Using the CLI (v0.8.0+)

The knot CLI provides the same capabilities as the MCP server via command-line commands, making it ideal for:

• Terminal-only environments

• Bash scripting and automation

• CI/CD pipelines

• Direct integration with other tools

Three main commands:

knot search — Semantic Code Search

knot search "user authentication" --max-results 10 --repo my-app

Find code entities by meaning, class names, docstrings, or comments.

knot callers — Reverse Dependency Lookup

knot callers "LoginService" --repo my-app

Find all code that references a specific entity (dead code detection, impact analysis, call chains).

knot explore — File Structure Inspection

knot explore "src/services/auth.ts" --repo my-app

List all classes, methods, functions in a file with signatures and documentation.

For detailed CLI usage guide, see .knot-agent.md — a machine-readable skill that teaches LLMs how to use knot CLI for autonomous code analysis.

Indexing a Codebase

Incremental Indexing (Default, v0.4.3+)

# First run: indexes all files
knot-indexer --repo-path /path/to/your/repo --neo4j-password secret

# Subsequent runs: only re-indexes changed files (fast!)
knot-indexer --repo-path /path/to/your/repo --neo4j-password secret

# NEW: Real-time Watch mode (v0.5.2+)
knot-indexer --watch --repo-path /path/to/your/repo --neo4j-password secret

How it works:

• Tracks file content via SHA-256 hashes in .knot/index_state.json

• Automatically detects: modified, added, and deleted files

• Only re-parses and re-embeds changed files

• Preserves graph relationships to unchanged files

• Processes entities in memory-efficient 512-entity chunks

Performance:

• Initial index (3800 files): ~60 minutes on standard hardware

• Incremental update (3 files changed): ~5-10 seconds

• Memory usage: Constant ~2GB regardless of repository size

Full Re-Index (Clean Mode)

# Force complete re-index (deletes all existing data)
knot-indexer --clean --repo-path /path/to/your/repo --neo4j-password secret

Use --clean when:

• You want to rebuild the entire index from scratch

• You've changed Tree-sitter queries or embedding models

• Troubleshooting indexing issues

Running E2E Integration Tests

To ensure indexer stability, run the E2E integration test suite:

# Run all language E2E tests (Java, TS, JS, HTML, CSS, Kotlin)
./tests/run_e2e.sh

# Run only Kotlin E2E tests
./tests/run_kotlin_e2e.sh

See tests/KOTLIN_E2E_TESTS.md for detailed coverage and troubleshooting.

Using the MCP Server

The MCP server exposes three tools to any compatible AI client:

Tool 1: search_hybrid_context

Find code by meaning or keywords

Query: "How is user authentication implemented?"
Result: All auth-related code, signatures, docstrings, and dependencies

Capabilities:

• Semantic search by functionality

• Class/method/function name lookup

• Docstring and inline comment search

• Architectural pattern discovery

• Full dependency context

Tool 2: find_callers

Find who calls a specific function

Query: "Find callers of getCurrentTimeInSeconds"
Result: All code that invokes this function + file locations

Advanced: Search by Signature (NEW in v0.7.4)

# Find by full signature (Java)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"registerUser(String"}}}' | knot-mcp

# Find by parameter type (Kotlin)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"findById(Int"}}}' | knot-mcp

# Find by type annotation (TypeScript)
echo '{"method":"tools/call","params":{"name":"find_callers","arguments":{"entity_name":"(EventData"}}}' | knot-mcp

Use Cases:

• Dead Code Detection: Zero callers = unused code

• Impact Analysis: "What breaks if I modify this?"

• Refactoring Safety: Find all references before removing

Tool 3: explore_file

Understand file structure

Query: "What's in BrowserService.ts?"
Result: All classes, methods, and functions with signatures and docs

Use Cases:

• Quick file navigation

• Module structure overview

• Finding all methods in a class without reading line-by-line

🔗 MCP Client Configuration

Supported Clients

knot works with any MCP-compatible AI client:

• ✅ Claude Desktop (Anthropic)

• ✅ Gemini CLI (Google)

• ✅ ChatGPT CLI / GPT (OpenAI)

• ✅ Cursor (AI IDE)

• ✅ Any standard MCP client

Configuration Examples

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "knot": {
      "command": "/absolute/path/to/knot/target/release/knot-mcp",
      "env": {
        "KNOT_REPO_PATH": "/path/to/indexed/repo",
        "KNOT_QDRANT_URL": "http://localhost:6334",
        "KNOT_NEO4J_URI": "bolt://localhost:7687",
        "KNOT_NEO4J_USER": "neo4j",
        "KNOT_NEO4J_PASSWORD": "your-password"
      }
    }
  }
}

Gemini CLI

{
  "mcpServers": {
    "knot": {
      "command": "/absolute/path/to/knot/target/release/knot-mcp",
      "env": {
        "KNOT_REPO_PATH": "/path/to/indexed/repo",
        "KNOT_QDRANT_URL": "http://localhost:6334",
        "KNOT_NEO4J_URI": "bolt://localhost:7687",
        "KNOT_NEO4J_USER": "neo4j",
        "KNOT_NEO4J_PASSWORD": "your-password"
      }
    }
  }
}

ChatGPT / GPT CLI

Similar JSON configuration in your client's MCP configuration file.

⚙️ Configuration Reference

All options can be set via environment variables (.env) or CLI flags. Environment variables take precedence.

🎨 Custom Tree-sitter Queries

The built-in extraction queries (queries/java.scm, queries/typescript.scm) can be overridden without recompiling:

KNOT_CUSTOM_QUERIES_PATH=/path/to/my/queries ./target/release/knot-indexer

Place java.scm and/or typescript.scm in your custom directory. Missing files fall back to built-in defaults.

🔄 Workflow Example

Step 1: Index a Java project

./target/release/knot-indexer --repo-path /home/user/my-java-app --neo4j-password secret

Step 2: Query via CLI (Instant search)

./target/release/knot search "authentication logic"
./target/release/knot callers "UserService.login"

Step 3: Start MCP server (For AI Agents)

./target/release/knot-mcp

Step 4: Use with Claude Desktop

• Claude will list the three tools in its Tools menu

• Ask: "Search for all authentication logic"

• Ask: "Find who calls the login method"

• Ask: "Explore the structure of UserService.java"

🤖 Auto-Configuring AI Agents

knot includes a universal .prompt file in its root directory that automatically configures modern AI coding agents (Cursor, Cline, opencode, Claude, etc.) to use the knot-mcp tools correctly.

The directive explicitly instructs AI agents to prioritize:

• search_hybrid_context — for semantic code discovery (instead of grep)

• find_callers — for reverse dependency analysis (instead of finding references manually)

• explore_file — for file structure inspection (instead of reading line-by-line)

This ensures that when you ask an AI agent to analyze, refactor, or understand your code, it leverages the full power of the vector and graph databases rather than falling back to context-blind regex searches. The .prompt file is universal and tool-agnostic, working with any LLM client that reads codebase directives.

🤝 Contributing

Contributions are welcome! Please ensure:

• All code passes cargo clippy

• Code is formatted with cargo fmt

• Changes are compatible with Rust 2024 edition

📜 License

This project is licensed under the MIT License. See LICENSE for details.

🚀 Roadmap

Current Release (v0.8.4 — Agent-Skills Documentation Installer & Lightweight Clients) ✅

• ✅ Downloadable Agent-Skills: Automated installer (scripts/install-agent-skills.sh) for agent-skills documentation via curl

• ✅ Lightweight Clients Mode: --features only-clients compiles CLI + MCP without embedding dependencies (glibc 2.35+)

• ✅ Feature Flags: Optional indexer feature for systems that only need query clients

• ✅ Docker Multi-Stage: New Dockerfile.clients for minimal client-only images (103MB vs 163MB)

Previous Release (v0.8.3 — Dry-Run Mode for Deployment Platforms) ✅

• ✅ Dry-Run Mode: MCP server can run in offline mode for quality checks on deployment platforms.

• ✅ Platform-Agnostic: Removed all platform-specific references; compatible with any deployment platform.

• ✅ Enhanced Reliability: Graceful handling of missing database connections for validation scenarios.

Earlier Release (v0.8.2 — Quality & Doc Refactor) ✅

• ✅ MCP Quality: Enhanced tool descriptions for better agent discovery and usage safety.

• ✅ Token-Efficient Docs: Modularized agent skill guide into docs/agent-skills/ for on-demand loading.

• ✅ Rust Phase 1: Infrastructure prepared for Rust 2024 integration.

Earlier Release (v0.8.1 — CLI UX & Docker Integration) ✅

• ✅ Silenced CLI Logs: Default log level set to error for knot CLI (cleaner Markdown output).

• ✅ 100% E2E Dual-Testing: All 35 integration tests simultaneously verify both MCP and CLI.

• ✅ Docker CLI Support: Official Docker image now includes the knot binary.

• ✅ Agent Guidance: Enhanced .knot-agent.md with signature-based search warnings.

Phase 6 (v0.8.0 — CLI Interface & Unified Core) ✅

• ✅ CLI Tool: Standalone knot command with search, callers, and explore subcommands.

• ✅ Unified Architecture: Shared core logic (src/cli_tools/) used by both CLI and MCP.

• ✅ LLM Skill File: .knot-agent.md teaches AI agents how to use CLI for autonomous analysis.

Upcoming (v0.8.x+)

Phase 7: Rust Support

• Support .rs files

• Struct, trait, and impl tracking

• Macro invocation analysis

Upcoming (v0.9.x+)

Phase 8: C/C++ Support

• Support .c, .cpp, .h, .hpp files

• Pointer and memory relationship tracking

• Header inclusion graph analysis

Long-Term Vision

• Python support

• Go support

• C# support

• IDE plugins (VS Code, IntelliJ, Vim)

• Web UI for graph visualization

• Language Server Protocol (LSP) integration

• Automated Code Review tool (MCP-based)

💬 Questions?

For issues, feature requests, or discussions, please open a GitHub issue.