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.

LeanKG Graph Visualization LeanKG Obsidian

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

Live Demo

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

leankg web --port 9000

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

Docker (Recommended for Teams)

One command — mount your codebase and start the MCP HTTP server on port 9699 (auto-indexes on first start):

docker run -d --name leankg -p 9699:9699 -v /absolute/path/to/your/project:/workspace -v leankg-rocksdb:/data/leankg-rocksdb freepeak/leankg:latest

Use the current directory:

docker run -d --name leankg -p 9699:9699 -v "$PWD:/workspace" -v leankg-rocksdb:/data/leankg-rocksdb freepeak/leankg:latest

Verify:

curl http://localhost:9699/health

Stop / remove:

docker rm -f leankg

Requires Docker or OrbStack. Point your AI tool MCP config at http://localhost:9699/mcp.

Note: Published image tags (freepeak/leankg:latest, :0.18.0) currently target linux/arm64 (Apple Silicon / ARM hosts). On linux/amd64, build locally with compose below. The image builds with --features embeddings so CozoDB HNSW semantic search works out of the box.

Build from source (compose)

docker compose -f docker-compose.rocksdb.yml up --build

For multi-project mounts and local overrides, see AGENTS.md → RocksDB Docker Deployment.

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 (with confidence + severity)
leankg status                             # Check index status
leankg metrics                            # View token savings
leankg doctor                             # Diagnose index health
leankg smoke-test                         # Run self-test (also runs at MCP HTTP startup)
leankg web                                # Start Web UI at http://localhost:8080
leankg export --format mermaid            # Export graph as Mermaid, DOT, HTML, SVG, GraphML, Neo4j
leankg quality --min-lines 50             # Find oversized functions
leankg detect-clusters                    # Identify functional code communities
leankg trace --all                        # Show feature-to-code traceability
leankg annotate src/main.rs::main -d "Entry point"  # Annotate code elements

# Smart agent verbs (Graphify-style)
leankg path "FastAPI" "ModelField"        # Shortest path between two symbols
leankg explain "APIRouter"                # Definition, cluster, degree, neighbors
leankg gods --limit 20                    # Top god nodes / hub ranking
leankg report --out .leankg/GRAPH_REPORT.md  # Generate architecture brief

# LSP bridge (uses your configured language server)
leankg lsp-list                           # List supported languages
leankg lsp-install go                     # Print install command for gopls (or run it)
leankg lsp-resolve --language go src/foo.go 42 5   # textDocument/definition via gopls

# Graph quality + reasoning
leankg check-consistency --severity BROKEN   # Detect broken / stale / current links
leankg tunnels --limit 50                 # List cross-domain tunnels
leankg clones --min-similarity 0.8        # Same-file near-duplicate detection
leankg prs --triage --conflicts           # PR impact + community-conflict triage
leankg reflect "auth flow?" useful --nodes src/auth.rs,src/jwt.rs  # Record query outcome

# Semantic search (build with --features embeddings; Docker ships it OOTB)
leankg embed --init                       # One-time model download
leankg embed                              # Incremental embedding build
leankg semantic-context "auth token validation" --top-k 100

# Run shell commands with RTK compression
leankg run -- cargo test -- --compress

# REST API server with auth
leankg api-serve --port 8081 --auth
leankg api-key create --name my-key

# Process management
leankg proc status                        # Show running LeanKG/Vite processes
leankg proc kill                          # Kill all LeanKG/Vite processes

# 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

# Team knowledge: incidents, env, services
leankg incident add --title "DB timeout" --severity P1 --affected checkout --env production
leankg incident list --env staging
leankg env-conflicts --service checkout   # Find promote-time drift across envs
leankg team                                # Show team + on-call + environment map

# Multi-repo registry + auto-install
leankg register my-project                # Register a repository
leankg list                               # List all registered repos
leankg status-repo my-project             # Show repo health + freshness
leankg setup                              # Configure MCP for all repos + install Claude hooks
leankg update                             # Self-update to latest GitHub release

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

Semantic Search (Embeddings)

Optional feature: dense-vector retrieval + cross-encoder reranking + graph traversal. Off by default to keep the binary slim. Requires building with the embeddings Cargo feature.

Canonical store: CozoDB embedding_vectors + native HNSW (embedding_vectors:vec_idx, Cosine, f32, 384-dim). Do not migrate the graph DB to Redis/FalkorDB to speed cold embed — measured writer-only throughput is already ~100k+ vec/sec; cold wall time is dominated by ONNX inference.

Measured cold rates (M2 Pro, function,method):

Profile	Sustained e2e	~371k ETA
Legacy FP32 (pre-fast)	~170 vec/s	~36 min
Fast path (`LEANKG_EMBED_FAST=1`, INT8)	~480–500 vec/s	~12–15 min

See PRD v3.6.3 (FR-EMBED-R1..R4) and generated_docs/embed_bg_job_and_runtime_plan_2026-07-15.md.

Build & first-time setup

# 1. Build with the feature flag
cargo build --release --features embeddings

# 2. Pre-download models (~2.3 GB) — do this once per machine
./target/release/leankg embed --init

Models cache to ~/Library/Caches/leankg/models/ (macOS), ~/.cache/leankg/models/ (Linux), or %LOCALAPPDATA%\leankg\models (Windows). Fast path uses Xenova onnx/model_quantized.onnx (auto-downloaded if missing).

Build the embedding index

# Incremental (default): only changed/new nodes — day-2 path (seconds–minutes)
# Defaults: --workers 2 --batch-size 32; further capped by LEANKG_EMBED_MAX_MB
leankg embed --wait --workers 2 --batch-size 32

# Mega-graph cold build: functions/methods only (default filter when >50k nodes)
leankg embed --wait --types function,method

# Force re-embed every selected node
leankg embed --wait --full

# Progress / cancel
leankg embed --status
leankg embed --cancel

Incremental runs diff against embedding_state and skip rows whose content hash hasn't changed. Prefer incremental after the first cold pass; avoid --full unless the model or schema changed.

Fast path (recommended for mega-graph cold builds)

LEANKG_EMBED_FAST defaults on. It selects INT8 (Xenova quantized BGE-small), caps sequence length, and runs data-parallel workers (intra_threads=1). Raise LEANKG_EMBED_MAX_MB on large hosts so batch size is not clamped to 16:

export LEANKG_EMBED_FAST=1
export LEANKG_EMBED_MODEL=bge-q          # Xenova INT8 (not Qdrant model_optimized)
export LEANKG_EMBED_MAX_SEQ=128
export LEANKG_EMBED_MAX_MB=6144          # allow batch≥64–128 (macOS default 2048 clamps hard)
export LEANKG_EMBED_MAX_BLOB_CHARS=500   # shorter blobs → faster tokenize/infer

./target/release/leankg embed --wait \
  --project /path/to/project \
  --types function,method \
  --workers 8 \
  --batch-size 128

Healthy logs should show kind=bge-int8, max_seq=128, and ...Xenova.../onnx/model_quantized.onnx — not Qdrant model_optimized.onnx (that file fails on current ORT with a SkipLayerNorm missing-input error).

Set LEANKG_EMBED_FAST=0 and/or LEANKG_EMBED_MODEL=bge for the legacy FP32 path.

MCP / Docker: do not block boot on cold embed

Cold embed on a mega-graph can take tens of minutes. Keep MCP healthy immediately and let vectors catch up in the background:

export LEANKG_EMBED_ON_BOOT=0              # entrypoint must not wait on embed
export LEANKG_EMBED_BACKGROUND=1           # in-process embed inside mcp-http
export LEANKG_EMBED_MAX_MB=2048            # soft RSS budget (macOS default)
export LEANKG_EMBED_BACKGROUND_WORKERS=1
export LEANKG_EMBED_BACKGROUND_BATCH=32

While the index is building, keyword/graph MCP tools work; semantic tools degrade until HNSW is ready (leankg embed --status to poll).

Query

# CLI one-shot (retrieve → rerank → traverse)
leankg semantic-context "embedding inference for semantic search"
leankg semantic-context "auth token validation" --env production --top-k 100
leankg semantic-context "..." --no-traverse       # skip Stage 4 graph enrichment
leankg semantic-context "..." --debug             # diagnostics: counts, latency

Via MCP, the kg_semantic_context tool exposes the same pipeline to AI tools.

Memory tuning

Embed auto-caps workers, batch size, upsert chunk, and the in-flight vector queue from LEANKG_EMBED_MAX_MB (default 2048 on macOS, 3072 elsewhere) so a cold run cannot balloon into swap and freeze the host. Inference also pauses briefly when RSS crosses 90% of that soft cap.

Knob	Effect
`LEANKG_EMBED_MAX_MB=2048`	Soft RSS budget (set `0` to disable caps — not recommended)
`LEANKG_EMBED_FAST=1`	INT8 + seq cap + data-parallel workers (default on)
`LEANKG_EMBED_MODEL=bge-q`	Xenova quantized ONNX; `bge` = FP32
`LEANKG_EMBED_MAX_SEQ=128`	Token truncate for cold speed (fast path default)
`LEANKG_EMBED_MAX_BLOB_CHARS`	Cap text blob length before tokenize (fast path ~500)
`--workers` / `--batch-size`	Requested values; clamped by the memory plan
`LEANKG_EMBED_UPSERT_CHUNK`	Writer flush size (also capped under a low budget)

`--batch-size`	Approx peak RSS (10-core Mac)	When to use
128 (fast path)	needs `LEANKG_EMBED_MAX_MB` ≥ ~6g	Mega-graph cold on workstation
32 (CLI default)	~1–2 GB with 1–2 workers	Laptop / default
16	lower	Tight `LEANKG_EMBED_MAX_MB`
8	~730 MB	Memory-pressured host
4	~400 MB	1-vCPU container

For Docker cold embeds, prefer mem_limit ≥ 6g or set LEANKG_EMBED_MAX_MB below the container limit so backpressure engages before the OOM killer.

Internals & design rationale

See src/embeddings/EMBEDDINGS.md for the module architecture, file map, data model, the embed/retrieve pipelines, operational gotchas, and the rationale for storing vectors natively in CozoDB's HNSW index.

Design philosophy for the retrieve→rerank→traverse flow is in docs/design/hybrid-retrieval-reranking.md.

Runtime measurements and rejected storage levers (WAL-off, Redis side-store): generated_docs/embed_bg_job_and_runtime_plan_2026-07-15.md.

Configuration (Environment Variables)

Variable	Default	Purpose
`LEANKG_MMAP_SIZE`	`67108864` (64 MiB)	SQLite mmap window. Lower = less RSS, more page faults.
`LEANKG_DB_ENGINE`	`sqlite`	`rocksdb` enables the RocksDB storage backend (recommended for teams).
`LEANKG_ROCKSDB_ROOT`	`~/.leankg-rocksdb`	Centralized RocksDB project store.
`LEANKG_AUTO_INDEX`	`1`	Enable index-if-needed on container startup.
`LEANKG_EMBED_ON_BOOT`	`1` (image-dependent)	Set `0` so Docker entrypoint does not block MCP on cold embed.
`LEANKG_EMBED_BACKGROUND`	unset	Set `1` to spawn in-process background embed inside `mcp-http` (shared DB).
`LEANKG_EMBED_FAST`	`1` (on)	INT8 + seq cap + data-parallel workers. Set `0` for legacy FP32 profile.
`LEANKG_EMBED_MODEL`	`bge` / fast→`bge-q`	`bge` = Xenova FP32; `bge-q`/`int8` = Xenova quantized; avoid Qdrant `model_optimized`.
`LEANKG_EMBED_MAX_SEQ`	`512` / fast→`128`	Max tokens per blob for DirectEmbedder.
`LEANKG_EMBED_MAX_BLOB_CHARS`	unset / fast→`500`	Cap blob text length before tokenize.
`LEANKG_EMBED_MAX_MB`	`2048` (macOS) / `3072`	Soft RSS budget for embed: caps workers/batch/channel; pauses infer at 90%. `0` disables.
`LEANKG_EMBED_BACKGROUND_WORKERS`	`1`	Worker count for in-process background embed (further capped by `LEANKG_EMBED_MAX_MB`).
`LEANKG_EMBED_BACKGROUND_BATCH`	`32`	Batch size for in-process background embed (further capped by `LEANKG_EMBED_MAX_MB`).
`LEANKG_EMBED_UPSERT_CHUNK`	`5000`	Rows per Cozo `import_relations` flush during embed (capped under a low RSS budget).
`LEANKG_VACUUM_INTERVAL_HOURS`	`1`	Hourly tick that calls `GraphEngine.vacuum()`. Set `0` to disable. No-op on RocksDB (background compaction handles it).
`LEANKG_WATCHER_DEBOUNCE_MS`	`2000`	File-watcher debounce window.
`LEANKG_WATCHER_BURST_LIMIT`	`256`	Soft cap on pending file changes per debounce window.
`LEANKG_WATCHER_MAX_DB_SIZE`	`524288000` (500 MiB)	Trigger VACUUM once the on-disk DB exceeds this size.
`LEANKG_CACHE_MAX_TOKENS`	`500000`	SessionCache upper bound. Lower this on memory-constrained hosts.
`LEANKG_API_PORT`	`9699`	Port for the auto-spawned REST API child process.

See INSTRUCTION.md for the full memory-tuning playbook.

Claude Code Setup

LeanKG auto-triggers in Claude Code sessions via lifecycle hooks that route search intents to LeanKG tools instead of native tools.

# Install LeanKG with Claude Code hooks and plugin
leankg setup

# Then restart Claude Code or run:
/reload-plugins

What leankg setup installs:

.claude-plugin/ - Plugin manifest for Claude Code validation
hooks/ - Full lifecycle hooks: Setup, SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop
Adds leankg@local to enabledPlugins in ~/.claude/settings.json

Hook lifecycle:

Setup - Version gating on startup
SessionStart - Injects tool selection hierarchy into every session
UserPromptSubmit - Initializes session context with LeanKG patterns
PreToolUse - Nudges toward LeanKG when you use Grep/Read/Bash for code analysis
PostToolUse - Logs LeanKG MCP tool usage for analytics
Stop - Captures session summary for future context retrieval

How LeanKG Helps

graph LR
    subgraph "Without LeanKG"
        A1[AI Tool] -->|Full codebase context| B1[15,000-45,000 tokens]
        B1 --> A1
    end

    subgraph "With LeanKG"
        A2[AI Tool] -->|Targeted subgraph| C[LeanKG Graph]
        C -->|Context reduction| A2
    end

Without LeanKG: AI processes full context from files found via grep/search. With LeanKG: AI queries knowledge graph for targeted context. Token reduction varies by task complexity (see benchmark results).

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 Optimized -- Targeted subgraph retrieval vs full file scanning
Impact Radius -- Compute blast radius before making changes with confidence + severity
Pre-Commit Risk Analysis -- detect_changes classifies risk as critical/high/medium/low
Dependency Graph -- Build call graphs with IMPORTS, CALLS, TESTED_BY, EMITS, LISTENS_ON, HTTP_CALLS, TUNNEL, EXPLAINS edges
MCP Server -- Expose graph via MCP protocol for AI tool integration (85 tools)
Orchestration -- Smart context routing with persistent + hot-path caching via natural language intent
Community Detection -- Auto-detect functional clusters (Leiden) with per-cluster SKILL.md
Smart Agent Verbs -- shortest_path, explain_node, god_nodes, get_graph_report for connection questions
LSP Bridge -- Generic JSON-RPC client for gopls / typescript-language-server / pyright / dart-language-server / rust-analyzer with per-(language, workspace) cache; resolve_with_lsp MCP + leankg lsp-resolve / lsp-install / lsp-list
Semantic Search (HNSW) -- CozoDB native ::hnsw over dense embeddings (--features embeddings); Docker ships it OOTB
Ontology & Knowledge -- Concept (concepts.yaml) + procedural workflow (workflows.yaml) layer with kg_* MCP tools
Agent Personas -- agent_focus, agent_diary_read/write for specialist reviewer/architect/ops contexts
Temporal Graph -- valid_from / valid_to on relationships with temporal_query + timeline
Rationale Extraction -- # WHY: / # NOTE: / # HACK: / # FIXME: / # XXX: comments become first-class rationale nodes linked via explains edges
Consistency Checker -- check_consistency flags BROKEN / STALE / CURRENT links against current code
Cross-Domain Tunnels -- Auto-link clusters sharing the same domain concept across projects
PR Impact Dashboard -- leankg prs + triage_prs use cluster overlap to spot merge-order risk
Work-Memory Reflect Loop -- report_query_outcome + leankg reflect aggregate into .leankg/reflections/LESSONS.md
Portable Graph Snapshot -- export_graph_snapshot for merge-friendly team-committed graph artifacts
Wake-up Protocol -- wake_up MCP loads ~170 tokens of L0+L1 identity at session start
Multi-Language -- Index Go, TypeScript, JavaScript, Python, Rust, Java, Kotlin, Dart, Android XML, Terraform, CI/CD YAML with tree-sitter
Android -- Extract XML layouts, resources, manifest relationships, Room, Hilt, Navigation, and WorkManager
Service Topology -- Microservice call graph with env namespacing (local / staging / production / upcoming)
Incidents & Env Conflicts -- Link outage postmortems to services; find_env_conflicts surfaces promote-time drift
Annotation Search -- Search code by @Entity, @HiltViewModel, and other annotations
Graph Export -- Export as JSON, DOT, Mermaid, HTML, SVG, GraphML, Neo4j, or portable snapshot
REST API -- Axum HTTP API with Argon2 API keys + team multi-project RocksDB deploy
RTK Compression -- 8 read modes + response compressor + --compress shell wrapper
TOON Responses -- MCP responses use Token-Oriented Object Notation (~40% token reduction)
CI/CD Auto-Update -- GitHub Actions workflow reindexes + commits portable snapshot on release

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

Supported AI Tools

Tool	Auto-Setup	Session Hook	Plugin	Full Lifecycle Hooks
Cursor	Yes	session-start	-	-
Claude Code	Yes	session-start	Yes	Setup, SessionStart, UserPromptSubmit, PreToolUse, PostToolUse, Stop
OpenCode	Yes	-	Yes	-
Docker	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.

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.

Update

# Check current version
leankg version

# Update LeanKG binary (kills processes, removes old binary, installs hooks)
leankg update

# Or 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

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
src/embeddings/EMBEDDINGS.md	Embeddings module internals (vector index, pipelines, native HNSW rationale)

Troubleshooting

High RAM Usage on macOS

LeanKG uses memory-mapped I/O and in-memory caching which can consume significant RAM on macOS. Primary causes:

Cause	Location	Fix
SQLite mmap_size=256MB	`src/db/schema.rs:20`	Set `LEANKG_MMAP_SIZE=134217728` (128MB)
Deprecated `all_elements()`	`src/graph/query.rs:537`	Use `get_elements_paginated()` instead
Deprecated `all_relationships()`	`src/graph/query.rs:992`	Use `get_relationships_paginated()`
SessionCache 500K tokens	`src/compress/session_cache.rs:11`	Set `LEANKG_CACHE_MAX_TOKENS=100000`
Multiple GraphEngine cached	`src/mcp/server.rs:48-49`	Cache eviction with TTL
Multiple cache layers	Various	Enable `memory_only` mode for PersistentCache
Unbounded DB file growth on long-lived servers	`src/graph/query.rs:100`	`LEANKG_VACUUM_INTERVAL_HOURS=1` (default) reclaims free pages hourly. No-op on RocksDB.

Quick fix - add to your shell profile:

export LEANKG_MMAP_SIZE=134217728   # 128MB instead of 256MB
export LEANKG_CACHE_MAX_TOKENS=100000  # 100K instead of 500K

See INSTRUCTION.md for detailed memory tuning and MCP server setup.

Embeddings feature (`--features embeddings`)

The optional embeddings pipeline (dense-vector retrieval + reranker) has its own failure modes. See src/embeddings/EMBEDDINGS.md for full operational notes. Common issues:

Symptom	Cause	Fix
Cold embed still ~30–40+ min	Fast path off, or `LEANKG_EMBED_MAX_MB` clamps batch to 16	Enable `LEANKG_EMBED_FAST=1` + `bge-q`; raise `LEANKG_EMBED_MAX_MB` (e.g. 6144); `--workers 8 --batch-size 128`
ORT `SkipLayerNorm` / missing `LayerNorm.weight`	Loading Qdrant `model_optimized.onnx`	Rebuild binary; use Xenova `model_quantized.onnx` via `LEANKG_EMBED_MODEL=bge-q`
MCP / Docker stuck unhealthy for hours	Entrypoint waiting on sync embed	Set `LEANKG_EMBED_ON_BOOT=0`; use `LEANKG_EMBED_BACKGROUND=1`
`embed` peaks at 10+ GB RSS	ORT arenas × many workers × large batch/channel	Set `LEANKG_EMBED_MAX_MB=2048` (default on macOS); use `--workers 1 --batch-size 8`
`semantic-context` returns 0 seeds, `Env-filtered: N` in `--debug`	elements' `env` doesn't match the requested env (default `local`)	pass `--env <value>`, or re-index with the right env
`parser::pest` from `embed`	ran against an old build that uses `:delete` (CozoDB 0.2.2 only supports `:rm`)	rebuild from current `main`
`semantic-context` says `Reranker: fallback`	bge-reranker-v2-m3 failed to init (corrupt cache, OOM)	`leankg embed --init` to re-download; lower `--batch-size`
Searches miss elements that state says are `fresh`	DB was modified out-of-band (manual SQLite edit) without re-running `embed`	`leankg embed --full`
Considering Redis/FalkorDB to “fix” cold embed	Writer is not the limiter (~100k+ vec/sec empty-DB)	Do not migrate; see plan doc / PRD v3.6.3 Won't Do

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 proc kill        # Kill all leankg and vite processes
leankg proc status      # Show running leankg/vite processes

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

Performance Benchmarks

Load Test Results (100K nodes)

Operation	Throughput
Insert elements	~57,618 elements/sec
Insert relationships	~67,067 relationships/sec
Retrieve all elements	~418,718 elements/sec
Cache speedup (cold to warm)	345-461x

Run load tests:

cargo test --release load_test -- --nocapture

Unified A/B Benchmark (All Tools, Simple to Complex)

Measures latency, input/output token usage, and token efficiency across 19 test cases spanning all LeanKG tools (search, find, context, dependencies, impact radius, call graphs, ontology) at 3 complexity levels, with automated Markdown export.

# Run the unified benchmark (rebuild first if source changed)
cargo build --release
target/release/leankg benchmark-unified --project .

Metric	With LeanKG	Without (grep)	Winner
Input Token Savings	30.0%	--	LeanKG
Token Efficiency (tokens/result)	2.09	6.39	LeanKG (3x)
Latency (simple queries)	20.4ms	20.2ms	~Equal
Latency (complex queries)	8.9s	34.9ms	Manual (impact radius is heavy)

See benchmark/results/unified-benchmark-1782980096.md for the full report (JSON + Markdown).

A/B Benchmark Results (Legacy)

See tests/benchmark/results/clean-benchmark-2026-04-21.md for earlier A/B testing results comparing LeanKG vs baseline code search.

leankg 0.18.1

LeanKG

Live Demo

Installation

One-Line Install (Recommended)

Install via Cargo or Build from Source

Docker (Recommended for Teams)

Build from source (compose)

Quick Start

Semantic Search (Embeddings)

Build & first-time setup

Build the embedding index

Fast path (recommended for mega-graph cold builds)

MCP / Docker: do not block boot on cold embed

Query

Memory tuning

Internals & design rationale

Configuration (Environment Variables)

Claude Code Setup

How LeanKG Helps

Highlights

Supported AI Tools

Context Metrics

Update

Documentation

Troubleshooting

High RAM Usage on macOS

Embeddings feature (`--features embeddings`)

Database Lock Error

Process Management

Performance Benchmarks

Load Test Results (100K nodes)

Unified A/B Benchmark (All Tools, Simple to Complex)

A/B Benchmark Results (Legacy)

Requirements

License

Star History

leankg 0.18.1

LeanKG

Live Demo

Installation

One-Line Install (Recommended)

Install via Cargo or Build from Source

Docker (Recommended for Teams)

Build from source (compose)

Quick Start

Semantic Search (Embeddings)

Build & first-time setup

Build the embedding index

Fast path (recommended for mega-graph cold builds)

MCP / Docker: do not block boot on cold embed

Query

Memory tuning

Internals & design rationale

Configuration (Environment Variables)

Claude Code Setup

How LeanKG Helps

Highlights

Supported AI Tools

Context Metrics

Update

Documentation

Troubleshooting

High RAM Usage on macOS

Embeddings feature (--features embeddings)

Database Lock Error

Process Management

Performance Benchmarks

Load Test Results (100K nodes)

Unified A/B Benchmark (All Tools, Simple to Complex)

A/B Benchmark Results (Legacy)

Requirements

License

Star History

Embeddings feature (`--features embeddings`)