Vera

Vera

[](https://github.com/lemon07r/Vera/blob/master/LICENSE) [](https://www.rust-lang.org) [](https://github.com/lemon07r/Vera/releases) [](docs/supported-languages.md)

[Install Guide](docs/installation.md) · [Features](docs/features.md) · [Query Guide](docs/query-guide.md) · [Benchmarks](docs/benchmarks.md) · [How It Works](docs/how-it-works.md) · [Models](docs/models.md) · [Supported Languages](docs/supported-languages.md)

Vector Enhanced Reranking Agent

Code search that combines BM25 keyword matching, vector similarity, and cross-encoder reranking. Supports 65 languages (61 with tree-sitter parsing), runs locally, returns structured results with file paths, line ranges, symbol metadata, and relevance scores.

Quick Start

1. Install

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

2. Set up models (pick one)

vera setup                       # Interactive wizard (auto-detects your hardware)
vera setup --api                 # API mode: works everywhere, no GPU needed (recommended)
vera setup --onnx-jina-coreml    # Apple Silicon (M1/M2/M3/M4)
vera setup --onnx-jina-cuda      # NVIDIA GPU
vera setup --onnx-jina-rocm      # AMD GPU (ROCm, Linux)
vera setup --onnx-jina-openvino  # Intel GPU (OpenVINO, Linux)
vera setup --onnx-jina-directml  # DirectX 12 GPU (Windows)

3. Index and search

vera index .
vera search "authentication logic"

What Sets Vera Apart

| | | |---|---| | Cross-encoder reranking | Most tools stop at retrieval. Vera scores query-candidate pairs jointly, lifting MRR@10 from 0.28 to 0.60. | | Single binary, 65 languages | One static binary with 61 tree-sitter grammars compiled in. No Python, no language servers, no per-language toolchains. | | Built-in code intelligence | Call graph analysis, reference finding, dead code detection, and project overview, all from the same index. | | Token-efficient for agents | Returns symbol-bounded chunks, not entire files. 75-95% fewer tokens on typical queries. |

Vera started after weeks of working on Pampax, a project I forked because it and other similar tools were missing what I wanted. I kept running into deep-rooted bugs, less-than-ideal design decisions, and thought I could build something better from the ground up. Every design choice comes from careful research, learning from other projects, benchmarking and evaluation. Take a look at the full [feature list](docs/features.md) to see everything Vera can do.

Installation

Use the quick start above if you just want to get going. This section helps you pick the right backend.

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

Pick Your Backend

Vera itself is always local: the index lives in .vera/ per project, config and models in $XDG_DATA_HOME/vera (or ~/.vera for existing installs). The backend choice only affects where embeddings and reranking run.

| You have | Run this | What happens | |----------|----------|-------------| | Not sure | vera setup | Interactive wizard auto-detects your hardware | | Any hardware | vera setup --api | Models run remotely via any OpenAI-compatible API. No GPU needed. Recommended. | | Apple Silicon (M1/M2/M3/M4) | vera setup --onnx-jina-coreml | Downloads local models, uses CoreML GPU acceleration | | NVIDIA GPU | vera setup --onnx-jina-cuda | Downloads local models, uses CUDA. Fastest local option | | AMD GPU (Linux) | vera setup --onnx-jina-rocm | Downloads local models, uses ROCm | | Intel GPU (Linux) | vera setup --onnx-jina-openvino | Downloads local models, uses OpenVINO | | DirectX 12 GPU (Windows) | vera setup --onnx-jina-directml | Downloads local models, uses DirectML |

API mode works with any OpenAI-compatible endpoint and needs no local compute. Local mode downloads two curated ONNX models and auto-detects your GPU; a GPU is recommended since CPU-only indexing is slow. After the first index, vera update . only re-embeds changed files, so incremental updates are fast on any backend. Full details: [docs/models.md](docs/models.md).

For step-by-step instructions, API provider options, Docker, building from source, and troubleshooting, see the full [Installation Guide](docs/installation.md).

MCP server

vera mcp   # or: bunx @vera-ai/cli mcp / uvx vera-ai mcp

Exposes search_code, get_stats, get_overview, and regex_search tools. search_code auto-indexes and starts a file watcher on first use if no index exists. The MCP surface stays intentionally small; use the CLI skill path when you need the full command set.

Usage

Core Workflow

vera index .
vera search "authentication logic"
vera update .

Search Patterns

vera search "error handling" --lang rust
vera search "routes" --path "src/**/*.ts"
vera search "handler" --type function --limit 5
vera search "OAuth token refresh" "JWT expiry handling" "auth middleware"
vera search "config" --intent "find where database connection strings are loaded"
vera search "config loading" --deep
vera search "auth" --compact

Common Tasks

| Task | Command | |------|---------| | Regex or exact text | vera grep "fn\s+main" | | Find callers | vera references foo | | Find callees | vera references foo --callees | | Find dead code | vera dead-code | | Get a project overview | vera overview | | Keep the index fresh | vera watch . | | Check your setup | vera doctor | | Repair missing local assets | vera repair | | Install agent skills | vera agent install |

See the [query guide](docs/query-guide.md) for search tips, the [feature list](docs/features.md) for the full command surface, and vera --help for CLI details.

Output

Defaults to markdown codeblocks (the most token-efficient format for AI agents):

````

```src/auth/login.rs:42-68 function:authenticate pub fn authenticate(credentials: &Credentials) -> Result { ... }

````

Use `--json` for compact JSON. `--raw` and `--timing` work with `vera search` and `vera grep`, and you can place them before or after the subcommand (for example, `vera --timing search "auth"` or `vera grep "TODO" --raw`).

### Excluding Files

Vera respects `.gitignore` by default. Create a `.veraignore` file (gitignore syntax) for more control, or use `--exclude` flags. Details: [docs/features.md](docs/features.md#flexible-exclusions).

## Benchmarks

21-task benchmark across `ripgrep`, `flask`, `fastify`, and `turborepo`:

| Metric | ripgrep | cocoindex | ColGREP (149M) | Vera |
|--------|---------|-----------|----------------|------|
| Recall@5 | 0.28 | 0.37 | 0.67 | **0.78** |
| MRR@10 | 0.26 | 0.35 | 0.62 | **0.91** |
| nDCG@10 | 0.29 | 0.52 | 0.56 | **0.84** |

Full methodology and version history: [docs/benchmarks.md](docs/benchmarks.md).

## Configure Your AI Agent

`vera agent install` installs the Vera skill for supported coding agents and can add a short usage snippet to your project's `AGENTS.md`, `CLAUDE.md`, `COPILOT.md`, or editor rules file.

```bash
vera agent install
vera agent install --client all

If you use the skills CLI, you can install Vera there too:

npx skills add lemon07r/Vera

If you skipped the prompt and want to add the instructions manually, use the snippet in the [Installation Guide](docs/installation.md#set-up-agent-skills).

Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: lemon07r
• Source: lemon07r/Vera
• License: MIT

Install and usage instructions live in the source repository linked above.