vera-ai 0.12.12

Bootstrap installer and wrapper for the Vera CLI

vera-ai

Code search for AI agents. Vera indexes your codebase using tree-sitter parsing and hybrid search (BM25 + vector similarity + cross-encoder reranking), then returns ranked code snippets as structured JSON.

This package downloads and wraps the native Vera binary for your platform. On musl-based Linux (Alpine, NixOS), the correct static binary is selected automatically. Set VERA_TARGET to override target detection (e.g., VERA_TARGET=x86_64-unknown-linux-musl uvx vera-ai install).

Current benchmark snapshot: on Vera's local 21-task, 4-repo release benchmark, v0.7.0 reaches 0.78 Recall@5, 0.83 Recall@10, 0.91 MRR@10, and 0.84 nDCG@10 with the local Jina CUDA ONNX stack. Full details live in the main repo docs.

Install

pip install vera-ai

Quick Start

vera-ai setup --api
vera-ai index .
vera-ai search "authentication logic"

vera-ai setup only configures the backend. Run vera-ai agent install to set up skill files for your agents. The interactive flow can also update AGENTS.md / CLAUDE.md style project instructions for you.

Common Tasks

Task	Command
Use API mode (recommended)	vera-ai setup --api
Use the interactive setup wizard	vera-ai setup
Use a local NVIDIA backend	vera-ai setup --onnx-jina-cuda
Search semantically	vera-ai search "authentication middleware"
Keep the index up to date	vera-ai update .
Watch for file changes	vera-ai watch .
Diagnose setup issues	vera-ai doctor
Run the deeper ONNX probe	vera-ai doctor --probe
Repair missing local assets	vera-ai repair
Inspect binary upgrades	vera-ai upgrade
Install agent skills	vera-ai agent install

For the full backend matrix, model options, Docker setup, and troubleshooting, see the main README and Installation Guide.

What you get

• 61+ languages via tree-sitter AST parsing
• Hybrid search: BM25 keyword + vector similarity, fused with Reciprocal Rank Fusion
• Cross-encoder reranking for precision
• Markdown codeblock output by default with file paths, line ranges, and optional symbol info (use --json for compact JSON, --raw for verbose output, --timing for step durations)

For full documentation, including custom local ONNX embedding models and manual install steps, see the GitHub repo.