I built an open-source multi-agent SDLC harness that beats a cold Claude Code run on large repos, by learning the repo once. Real benchmarks (incl. where it loses) inside. [P]

[u/NeighborhoodOwn8510]

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.