Claude reads entire files to find what it needs. Lumen gives it a map.

Lumen is a 100% local semantic code search engine for AI coding agents. No API keys, no cloud, no external database, just open-source embedding models (Ollama or LM Studio), SQLite, and your CPU. A single static binary and your own local embedding server.

The payoff is measurable and reproducible: across 8 benchmark runs on 8 languages and real GitHub bug-fix tasks, Lumen cuts cost in every single language — up to 39%. Output tokens drop by up to 66%, sessions complete up to 53% faster, and patch quality is maintained in every task. All verified with a transparent, open-source benchmark framework that you can run yourself.

	With Lumen	Baseline (no Lumen)
Cost (avg, bug-fix)	$0.29 (-26%)	$0.40
Time (avg, bug-fix)	125s (-28%)	174s
Output tokens (avg)	5,247 (-37%)	8,323
JavaScript (marked)	$0.32, 119s (-33%, -53%)	$0.48, 255s
Rust (toml)	$0.38, 204s (-39%, -34%)	$0.61, 310s
PHP (monolog)	$0.14, 34s (-27%, -34%)	$0.19, 52s
TypeScript (commander)	$0.14, 56s (-27%, -33%)	$0.19, 84s
Patch quality	Maintained in all 8 tasks	—

Table of contents

• Demo
• Quick start
• What you get
• How it works
• Benchmarks
• Supported languages
• Configuration
  • Supported embedding models
• Controlling what gets indexed
• Database location
• CLI Reference
• Troubleshooting
• Development

Demo

Claude Code asking about the Prometheus codebase. Lumen's semantic_search finds the relevant code without reading entire files.

Quick start

Prerequisites:

1. Ollama installed and running, then pull the default embedding model:

   ollama pull ordis/jina-embeddings-v2-base-code

2. Claude Code installed

Install:

/plugin marketplace add ory/claude-plugins
/plugin install lumen@ory

That's it. On first session start, Lumen:

1. Downloads the binary automatically from the latest GitHub release
2. Indexes your project in the background using Merkle tree change detection
3. Registers a semantic_search MCP tool that Claude uses automatically

Two skills are also available: /lumen:doctor (health check) and /lumen:reindex (forced re-indexing).

What you get

• Semantic vector search — Claude finds relevant functions, types, and modules by meaning, not keyword matching
• Auto-indexing — indexes on session start, only re-processes changed files via Merkle tree diffing
• Incremental updates — re-indexes only what changed; large codebases re-index in seconds after the first run
• 11 language families — Go, Python, TypeScript, JavaScript, Rust, Ruby, Java, PHP, C/C++, C#
• Zero cloud — embeddings stay on your machine; no data leaves your network
• Ollama and LM Studio — works with either local embedding backend

How it works

Lumen sits between your codebase and Claude as an MCP server. When a session starts, it walks your project and builds a Merkle tree over file hashes: only changed files get re-chunked and re-embedded. Each file is split into semantic chunks (functions, types, methods) using Go's native AST or tree-sitter grammars for other languages. Chunks are embedded and stored in SQLite + sqlite-vec using cosine-distance KNN for retrieval.

Files → semantic chunks → vector embeddings → SQLite/sqlite-vec → KNN search

When Claude needs to understand code, it calls semantic_search instead of reading entire files. The index is stored outside your repo (~/.local/share/lumen/<hash>/index.db), keyed by project path and model name — different models never share an index.

Benchmarks

Lumen is evaluated using bench-swe: a SWE-bench-style harness that runs Claude on real GitHub bug-fix tasks and measures cost, time, output tokens, and patch quality — with and without Lumen. All results are reproducible: raw JSONL streams, patch diffs, and judge ratings are committed to this repository.

Key results — 8 runs across 8 languages, hard difficulty, real GitHub issues (ordis/jina-embeddings-v2-base-code, Ollama):

Language	Cost Reduction	Time Reduction	Output Token Reduction	Quality
Rust	-39%	-34%	-31% (18K → 12K)	Poor (both)
JavaScript	-33%	-53%	-66% (14K → 5K)	Perfect (both)
TypeScript	-27%	-33%	-64% (5K → 1.8K)	Good (both)
PHP	-27%	-34%	-59% (1.9K → 0.8K)	Good (both)
Ruby	-24%	-11%	-9% (6.1K → 5.6K)	Good (both)
Python	-20%	-29%	-36% (1.7K → 1.1K)	Perfect (both)
Go	-12%	-9%	-10% (11K → 10K)	Good (both)
C++	-8%	-3%	+42% (feature task)	Good (both)

Cost was reduced in every language tested. Quality was maintained in every task — zero regressions. JavaScript and TypeScript show the most dramatic efficiency gains: same quality fixes in half the time with two-thirds fewer tokens. Even on tasks too hard for either approach (Rust), Lumen cuts the cost of failure by 39%.

See docs/BENCHMARKS.md for all 8 per-language deep dives, judge rationales, and reproduce instructions.

Supported languages

Supports 11 language families with semantic chunking (8 benchmarked):

Language	Parser	Extensions	Benchmark status
Go	Native AST	.go	Benchmarked: -12% cost, Good quality
Python	tree-sitter	.py	Benchmarked: Perfect quality, -36% tokens
TypeScript / TSX	tree-sitter	.ts, .tsx	Benchmarked: -64% tokens, -33% time
JavaScript / JSX	tree-sitter	.js, .jsx, .mjs	Benchmarked: -66% tokens, -53% time
Rust	tree-sitter	.rs	Benchmarked: -39% cost, -34% time
Ruby	tree-sitter	.rb	Benchmarked: -24% cost, -11% time
PHP	tree-sitter	.php	Benchmarked: -59% tokens, -34% time
C / C++	tree-sitter	.c, .h, .cpp, .cc, .cxx, .hpp	Benchmarked: -8% cost (C++ feature task)
Java	tree-sitter	.java	Supported
C#	tree-sitter	.cs	Supported

Go uses the native Go AST parser for the most precise chunks. All other languages use tree-sitter grammars. See docs/BENCHMARKS.md for all 8 per-language benchmark deep dives.

Configuration

All configuration is via environment variables:

Variable	Default	Description
LUMEN_EMBED_MODEL	see note ¹	Embedding model (must be in registry)
LUMEN_BACKEND	ollama	Embedding backend (ollama or lmstudio)
OLLAMA_HOST	http://localhost:11434	Ollama server URL
LM_STUDIO_HOST	http://localhost:1234	LM Studio server URL
LUMEN_MAX_CHUNK_TOKENS	512	Max tokens per chunk before splitting

¹ ordis/jina-embeddings-v2-base-code (Ollama), nomic-ai/nomic-embed-code-GGUF (LM Studio)

Supported embedding models

Dimensions and context length are configured automatically per model:

Model	Backend	Dims	Context	Recommended
ordis/jina-embeddings-v2-base-code	Ollama	768	8192	Best default — lowest cost, no over-retrieval
qwen3-embedding:8b	Ollama	4096	40960	Best quality — strongest dominance (7/9 wins), very slow indexing
nomic-ai/nomic-embed-code-GGUF	LM Studio	3584	8192	Usable — good quality, but TypeScript over-retrieval raises costs
qwen3-embedding:4b	Ollama	2560	40960	Not recommended — highest costs, severe TypeScript over-retrieval
nomic-embed-text	Ollama	768	8192	Untested
qwen3-embedding:0.6b	Ollama	1024	32768	Untested
all-minilm	Ollama	384	512	Untested

Switching models creates a separate index automatically. The model name is part of the database path hash, so different models never collide.

Controlling what gets indexed

Lumen filters files through six layers: built-in directory and lock file skips → .gitignore → .lumenignore → .gitattributes (linguist-generated) → supported file extension. Only files that pass all layers are indexed.

.lumenignore uses .gitignore syntax. Place it in your project root (or any subdirectory) to exclude files that aren't in .gitignore but are noise for code search — generated protobuf files, test snapshots, vendored data, etc.

Built-in skips (always excluded)

Directories: .git, node_modules, vendor, dist, .cache, .venv, venv, __pycache__, target, .gradle, _build, deps, .idea, .vscode, .next, .nuxt, .build, .output, bower_components, .bundle, .tox, .eggs, testdata, .hg, .svn

Lock files: package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lock, bun.lockb, go.sum, composer.lock, poetry.lock, Pipfile.lock, Gemfile.lock, Cargo.lock, pubspec.lock, mix.lock, flake.lock, packages.lock.json

Database location

Index databases are stored outside your project:

~/.local/share/lumen/<hash>/index.db

Where <hash> is derived from the absolute project path, embedding model name, and binary version. Different models or Lumen versions automatically get separate indexes. No files are added to your repo, no .gitignore modifications needed.

You can safely delete the entire lumen directory to clear all indexes, or use lumen purge to do it automatically.

CLI Reference

Download the binary from the GitHub releases page or let the plugin install it automatically.

lumen help

Troubleshooting

Ollama not running / "connection refused"

Start Ollama and verify the model is pulled:

ollama serve
ollama pull ordis/jina-embeddings-v2-base-code

Run /lumen:doctor inside Claude Code to confirm connectivity.

Stale index after large refactor

Run /lumen:reindex inside Claude Code to force a full re-index, or:

lumen purge && lumen index .

Switching embedding models

Set LUMEN_EMBED_MODEL to a model from the supported table above. Each model gets its own database; the old index is not deleted automatically.

Slow first indexing

The first run embeds every file. Subsequent runs only process changed files (typically a few seconds). For large projects (100k+ lines), first indexing can take several minutes — this is a one-time cost.

Development

git clone https://github.com/ory/lumen.git
cd lumen

# Build locally (CGO required for sqlite-vec)
make build-local

# Run tests
make test

# Run linter
make lint

# Load as a Claude Code plugin from source
make plugin-dev

See CLAUDE.md for architecture details, design decisions, and contribution guidelines.