LeanKG

Lightweight Knowledge Graph for AI-Assisted Development

LeanKG is a local-first knowledge graph that gives AI coding tools accurate codebase context. It indexes your code, builds dependency graphs, and exposes an MCP server so tools like Cursor, OpenCode, and Claude Code can query the knowledge graph directly. No cloud services, no external databases.

Visualize your knowledge graph with force-directed layout, WebGL rendering, and community clustering.

See docs/web-ui.md for more features.

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Live Demo

Try LeanKG without installing: https://leankg.onrender.com

leankg web --port 9000

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Installation

One-Line Install (Recommended)

curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/install.sh | bash -s -- <target>

Supported targets:

Target	AI Tool	Auto-Installed
opencode	OpenCode AI	Binary + MCP + Plugin + Skill + AGENTS.md
cursor	Cursor AI	Binary + MCP + Skill + AGENTS.md + Session Hook
claude	Claude Code	Binary + MCP + Plugin + Skill + CLAUDE.md + Session Hook
gemini	Gemini CLI	Binary + MCP + Skill + GEMINI.md
kilo	Kilo Code	Binary + MCP + Skill + AGENTS.md
antigravity	Google Antigravity	Binary + MCP + Skill + GEMINI.md

Examples:

curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/install.sh | bash -s -- cursor
curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/install.sh | bash -s -- claude

Install via Cargo or Build from Source

cargo install leankg && leankg --version

git clone https://github.com/FreePeak/LeanKG.git && cd LeanKG && cargo build --release

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Quick Start

leankg init                              # Initialize LeanKG in your project
leankg index ./src                        # Index your codebase
leankg watch ./src                        # Auto-index on file changes
leankg impact src/main.rs --depth 3       # Calculate blast radius
leankg status                             # Check index status
leankg metrics                            # View token savings
leankg web                                # Start Web UI at http://localhost:8080

# Obsidian vault sync
leankg obsidian init                      # Initialize Obsidian vault structure
leankg obsidian push                      # Push LeanKG data to Obsidian notes
leankg obsidian pull                      # Pull annotation edits from Obsidian
leankg obsidian watch                     # Watch vault for changes and auto-pull
leankg obsidian status                    # Show vault status

# Microservice call graph (via Web UI)
leankg web                                # Start Web UI at http://localhost:8080
                                          # Then visit http://localhost:8080/services

See docs/cli-reference.md for all commands.

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How LeanKG Helps

graph LR
    subgraph "Without LeanKG"
        A1[AI Tool] -->|Scans entire codebase| B1[10,000+ tokens]
        B1 --> A1
    end

    subgraph "With LeanKG"
        A2[AI Tool] -->|13-42 tokens| C[LeanKG Graph]
        C -->|Targeted subgraph| A2
    end

Without LeanKG: AI scans entire codebase (~10,000+ tokens). With LeanKG: AI queries knowledge graph for targeted context (13-42 tokens). 98% token saving for impact analysis.

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Highlights

• Auto-Init -- Install script configures MCP, rules, skills, and hooks automatically
• Auto-Trigger -- Session hooks inject LeanKG context into every AI tool session
• Token Concise -- 13-42 tokens per query vs 10,000+ for full codebase scan
• Token Saving -- Up to 98% token reduction for impact analysis
• Impact Radius -- Compute blast radius before making changes
• Dependency Graph -- Build call graphs with IMPORTS, CALLS, TESTED_BY edges
• MCP Server -- Expose graph via MCP protocol for AI tool integration
• Multi-Language -- Index Go, TypeScript, Python, Rust, Java, Kotlin with tree-sitter
• Android -- Extract XML layouts, resources, and manifest relationships

See docs/architecture.md for system design and data model details.

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Supported AI Tools

Tool	Auto-Setup	Session Hook	Plugin
Cursor	Yes	session-start	-
Claude Code	Yes	session-start	Yes
OpenCode	Yes	-	Yes
Kilo Code	Yes	-	-
Gemini CLI	Yes	-	-
Google Antigravity	Yes	-	-
Codex	Yes	-	-

Note: Cursor requires per-project installation. The AI features work on a per-workspace basis, so LeanKG should be installed in each project directory where you want AI context injection.

See docs/agentic-instructions.md for detailed setup and auto-trigger behavior.

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Context Metrics

Track token savings to understand LeanKG's efficiency.

leankg metrics --json              # View with JSON output
leankg metrics --since 7d           # Filter by time
leankg metrics --tool search_code   # Filter by tool

See docs/metrics.md for schema and examples.

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Update

# Check current version
leankg version

# Update LeanKG binary via install script
curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/install.sh | bash -s -- update

# Obsidian vault sync
leankg obsidian init                      # Initialize Obsidian vault
leankg obsidian push                      # Push LeanKG data to Obsidian notes
leankg obsidian pull                      # Pull annotation edits from Obsidian

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Documentation

Doc	Description
docs/cli-reference.md	All CLI commands
docs/mcp-tools.md	MCP tools reference
docs/agentic-instructions.md	AI tool setup & auto-trigger
docs/architecture.md	System design, data model
docs/web-ui.md	Web UI features
docs/metrics.md	Metrics schema & examples
docs/benchmark.md	Performance benchmarks
docs/roadmap.md	Feature planning
docs/tech-stack.md	Tech stack & structure
docs/android-extraction.md	Android XML & resource extraction

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Troubleshooting

Database Lock Error

If you see database is locked (code 5), another LeanKG process is holding the database:

# Kill all leankg and vite processes
leankg-kill

# Or manually
pkill -9 -f "leankg"
pkill -9 -f "vite"

Process Management

leankg-kill              # Kill all leankg and vite processes (after adding to ~/.zshrc)
leankg-status            # Show running leankg/vite processes

Important: Always kill the web server before indexing to avoid database lock conflicts.

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Performance Benchmarks

Load Test Results (100K nodes)

Test	Throughput
Insert elements	~173,000 elements/sec
Insert relationships	~179,000 relationships/sec
Retrieve all elements	~662,000 elements/sec

Run load tests:

cargo test --release load_test -- --nocapture

See docs/analysis/load-testing-1m-nodes-2026-04-17.md for detailed performance analysis.

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Requirements

• Rust 1.70+
• macOS or Linux

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License

MIT

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