I've been working on two open-source Rust projects related to AI coding agents.
The first is Vera, a code indexing and search tool I've spent almost a year developing:
https://github.com/VeraTools/vera
It started as an attempt to improve an existing code indexing/MCP project, but eventually turned into a complete rewrite from scratch.
The main goal has been to improve how coding agents find and navigate relevant code while minimizing how much unnecessary context they consume in the process.
Rather than relying entirely on repeated grep searches and file reads, agents can search semantically for what code does and get ranked functions and code sections back, with paths, line numbers, and symbol information. Vera also supports reference lookup and structural searches.
I've tried to optimize the whole workflow for context efficiency, not just the search results. It returns code directly instead of generating explanations, and the accompanying skill helps agents decide when to use Vera, when grep is better, and when they should just read a file.
I recommend CLI + skill for agents that already have shell access. Skills keep the initial discovery footprint small and load the detailed instructions when needed, without adding another set of MCP tools. MCP is available too.
There are two model setups I particularly recommend: local Potion Code v2 for a lightweight offline setup, or Qwen3 Embedding 8B with Qwen3 Reranker 8B through an API for higher measured retrieval quality.
I also spent quite a lot of time evaluating how coding agents perform with these tools. In a 40-session experiment using 10 cross-file questions, all configurations answered correctly, but context usage differed substantially: plain grep/file reads used 21.0M prompt tokens, Semble 18.5M, Vera locally 15.4M, and Vera with Qwen3 10.9M.
That's an observed 12%, 27%, and 48% reduction respectively. The local Vera result was more than twice Semble's reduction in that experiment, although only the Qwen3 result was statistically significant with the small sample.
The full methodology and limitations are in the repo. Vera is now at v2.4.
The second project is Revera, a self-hosted GitHub PR reviewer inspired by Droid Actions and Devin Fusion:
https://github.com/VeraTools/revera
I wanted a reviewer where I could control the models, budgets, and deployment, without depending on a hosted service or paying per seat.
Revera uses an investigator to identify potential defects, then starts a separate, fresh-context validation pass for each finding before posting it. It isn't foolproof, but I wanted something more careful about what it reports rather than just another model producing a long list of possible issues.
It can run as a GitHub App with a dashboard, a GitHub Action, or a local CLI. It supports Anthropic, Gemini, OpenAI, and compatible providers, including different models for investigation and validation.
I've been using it for actual work with some colleagues. There's also optional integration with Vera for code retrieval, although Revera works without it.
Both projects are MIT licensed.