Vera

Name: Vera
Author: LLM Mart

No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.

Docs · Install Guide · Features · Query Guide · Benchmarks · How It Works · Models · Supported Languages

Local, symbol-aware code search for developers and AI agents.

Hybrid BM25 + vector search with optional reranking, 65 languages, one static binary. Indexes stay on your machine; results come back as symbol-bounded chunks with file paths, line ranges, and scores.

_{Vector Enhanced Reranking Agent}

vera search demo

Quick Start

1. Install

bunx @vera-ai/cli install   # or: npx -y @vera-ai/cli install / uvx vera-ai install

2. Set up and index

Zero-setup local (CPU, no key, no GPU):

vera setup --potion-code --index .

Best measured search quality (one OpenRouter key, Qwen preset):

vera setup --api --index .

GPU and other backends

vera setup                                  # Interactive wizard, indexes this project by default
vera setup --onnx-jina-coreml --index .     # Apple Silicon (M1/M2/M3/M4)
vera setup --onnx-jina-cuda --index .       # NVIDIA GPU
vera setup --onnx-jina-rocm --index .       # AMD GPU (ROCm, Linux)
vera setup --onnx-jina-openvino --index .   # Intel GPU (OpenVINO, Linux)
vera setup --onnx-jina-directml --index .   # DirectX 12 GPU (Windows)

The wizard also offers presets for OpenAI, Jina, and Voyage. The Qwen preset uses qwen/qwen3-embedding-8b + qwen/qwen3-reranker-8b via https://openrouter.ai/api/v1 with a single shared key and the generic reranker protocol.

3. Search

vera search "authentication logic"

If the current project has no index, interactive search offers to create one. JSON and non-interactive searches still return the missing-index error.

4. Keep .vera/ out of git

echo '.vera/' >> .gitignore

The index can be large and is machine-local.

See What's New for release notes.

What Sets Vera Apart


Token-efficient for agents	Returns symbol-bounded chunks, not entire files. 75-95% fewer tokens on typical queries. In a blind-graded four-arm agent benchmark (GLM-5.3, high effort, 10 cross-file questions, one repository), the Qwen embedding+reranker pair consumed 48% less prompt context than a no-tool control at equal 10/10 answer quality, the only per-arm figure statistically significant at that sample size; the local Potion default consumed 27% less on the same lane, within run-to-run noise at N=10. Method and per-arm data: Benchmark history.
Single binary, 65 languages	One static binary with 61 tree-sitter grammars compiled in. No Python, no language servers, no per-language toolchains.
Fast at query time, tiny on disk	6.4 ms median query latency on the 1,251-task suite (local Potion Code defaults) with a 4.7 GB index for 63 repositories (6.8x smaller than Semble's 32 GB).
Updates, not just re-indexes	Incremental updates and watch mode keep the index current as files change. Persistent indexes survive restarts and are reused when identity checks pass.
Built-in code intelligence	Call graph analysis, reference finding, dead code detection, and project overview, all from the same index.
Holds up off the benchmark	Leads Semble on the independent contamination set (10 fresh repositories, locally generated ground truth) and on recall@5, while trailing by 0.008 nDCG on Semble's own 63-repo benchmark. Details in Benchmarks.

Quick Start

What Sets Vera Apart

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