LeanKG

Contents

• Get Started
• Why LeanKG?
• Measured Results
• Key Features
• Screenshots
• How It Works
• MCP & Agents
• Language Support
• CLI Quick Reference
• Documentation
• Troubleshooting
• Contributing
• License
• Star History

Get Started

1. Install the CLI

One command — binary, MCP wiring, and agent docs for your tool of choice:

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

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

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

curl -fsSL https://raw.githubusercontent.com/FreePeak/LeanKG/main/scripts/docker-up.sh | bash
curl http://localhost:9699/health

Point your MCP client at http://localhost:9699/mcp. Multi-project RocksDB mounts: AGENTS.md.

Published Hub tags currently target linux/arm64. On linux/amd64, build with docker compose -f docker-compose.rocksdb.yml up --build.

2. Wire up your agent(s)

Installing the binary alone does not connect your agent. Run setup (or use an install target above) so MCP is registered:

leankg setup

This configures Cursor, Claude Code, OpenCode, Gemini, and other supported clients with LeanKG’s MCP server, skills, and hooks where available.

3. Index each project

cd your-project
leankg init
leankg index ./src
leankg status

Optional: enable watch mode so the graph stays fresh while you and your agent edit code:

leankg mcp-stdio --watch

4. Ask better questions

leankg impact src/main.rs --depth 3
leankg path "Handler" "Repository"
leankg explain "APIRouter"
leankg graph-query "what connects auth to the database?"
leankg web    # UI at http://localhost:8080

Upgrade anytime:

leankg update

Why LeanKG?

When an AI agent needs to understand code, it usually discovers structure the slow way: grep, glob, and Read — one file at a time — rebuilding call paths and dependencies by hand. That is a pile of tool calls and round-trips before the real work starts.

LeanKG hands the agent the exact subgraph it needs. It indexes symbols, edges, tests, docs, and (optionally) embeddings into a local knowledge graph, then exposes them over MCP. Instead of crawling the tree, the agent asks one question and gets back callers, dependents, blast radius, and targeted source — surgical context, not a file-by-file search.

graph LR
    A[AI Agent] -->|intent| B[LeanKG MCP]
    B --> C[Graph + Embeddings]
    C -->|targeted context| A

On cost: LeanKG’s win on every codebase is precision and speed — fewer tool calls, faster answers. Token savings are real and scale-dependent: modest on small repos, material on large monorepos multiplied by team-wide agent usage.

Company ROI vs grep and Graphify

For engineering managers choosing a team-wide stack: LeanKG vs Graphify — Company ROI Brief (token/tool-call floors, multi-repo Docker TCO, mega-graph safety, ops/traceability). The primary adoption lever is always-on graph-first install (curl …/install.sh | bash -s -- cursor or claude) so agents query the graph before grep.

Measured Results

Vector-engine A/B gate (100 tasks, synthetic agent workload vs grep/cat-style baseline) — see docs/benchmarks/vector_engine_gate_results.json:

Unified agent A/B (19 cases vs grep baseline): ~30% input token savings, ~3× tokens/result efficiency.

Load test (~100K nodes):

cargo build --release
target/release/leankg benchmark-unified --project .
cargo bench --bench vector_engine_ab

Full methodology: docs/benchmark.md

Key Features

• MCP-native — 85+ tools for search, impact, call graphs, ontology, architecture, and team knowledge
• Procedural ontology auto-update — edit ontology/workflows.yaml while serving; watcher re-syncs so kg_trace_workflow returns corrected steps without restart
• Impact radius — blast radius before you change code, with confidence and severity
• Dependency graph — imports, calls, tested_by, http_calls, service_calls, tunnels, and more
• Semantic search — CozoDB HNSW over dense embeddings (--features embeddings; included in Docker)
• Community detection — Leiden clusters with per-cluster skill context
• Local-first storage — SQLite by default; RocksDB for multi-project / team deploy
• Token-aware payloads — targeted subgraphs + TOON responses (~40% smaller MCP payloads)
• Team knowledge — incidents, env conflicts, service topology, Obsidian vault sync
• Graph export — Mermaid, DOT, HTML, SVG, GraphML, Neo4j, portable snapshots
• Web UI (v2) — explorer shell adapted from GitNexus gitnexus-web (Force / Tree / Circles, filters, search, code panel); data plane is LeanKG /api/* (ui-v2/ + leankg serve)

Architecture: docs/architecture.md · MCP catalog: docs/mcp-tools.md · UI v2: ui-v2/README.md

Screenshots