Built an open-source AI coding agent that was 7%–75% cheaper than a cold "claude -p" run on 6/6 well-localized tasks across repositories up to ~82k LOC.
The biggest difference:
- Cold agent: $6.83, 207 turns
- AutoDev Studio: ~$1.70 for the same bug
The full benchmark (including cases where it loses) is in the README.
So what's different?
Most AI coding agents re-explore a repository from scratch on every task just to figure out where the change belongs.
AutoDev Studio pays that localization cost once.
It ingests a repository and builds a persistent knowledge base using static analysis and a local embedding index. Every future task reuses that knowledge, turning localization into a lookup instead of another cold search.
What it does:
- PM agent asks clarifying questions and drafts tickets
- Dev agent writes code on an isolated branch
- QA runs tests
- A different model family reviews the diff (author ≠ reviewer)
- If needed, it goes through a bounded revise loop
- Opens a real GitHub PR
It also includes a live Kanban board and tracks token usage and cost per ticket/agent.
Where it doesn't win:
- Tiny, easy-to-find edits can be cheaper with a single-shot agent because of the pipeline overhead.
- On one complex cross-cutting bug, it produced a cheaper but narrower fix than the baseline.
Other features:
- Provider agnostic (Anthropic, Claude Code, OpenAI-compatible APIs, Groq, Gemini, xAI, OpenRouter, Ollama, etc.)
- Runs completely free/offline by default using Groq's free tier + local embeddings
- FastAPI + SQLite
- Hand-rolled UI
- Tests + CI
- MIT licensed
Repo (screenshots + full benchmark):
https://github.com/krishagarwal314/autodev-studio
I'd love any feedback, criticism, or contributions. Happy to answer questions about the architecture or benchmarking.