Vera
Local code search combining BM25, vector similarity, and cross-encoder reranking. Parses 60+ languages with tree-sitter, runs entirely offline, and returns structured results with file paths, line ranges, and symbol metadata. Built in Rust.
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Vera
Install Guide · Features · Query Guide · Benchmarks · How It Works · Models · Supported Languages
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 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.
For step-by-step instructions, API provider options, Docker, building from source, and troubleshooting, see the full Installation Guide.
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 for search tips, the feature list 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<Token> { ... }
```
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.
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.
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.
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.
Contributing
See CONTRIBUTING.md.