Prompt

Can you recommend a repo-level agent for running tests and fixing failures across a legacy codebase?

Artificial Intelligence / AI Developer Tools2 observationsLast seen Jul 20, 2026

Latest observation

Jul 20, 2026OpenAI APIWeb search: off

Yes — for a repo-level agent that can run tests, inspect failures, and make fixes across a legacy codebase, I’d recommend looking for an agent with these properties:

  • Can operate on the full repository, not just a single file
  • Can execute commands/tests repeatedly
  • Can inspect stack traces and logs
  • Can edit multiple files safely
  • Has a planning loop: test → diagnose → patch → retest

Good choices

  1. OpenHands

    • Strong fit for repo-wide coding/debugging tasks
    • Can run tests and iterate on failures
    • Good for legacy code because it can inspect many files and make coordinated changes
  2. SWE-agent

    • Designed specifically for fixing issues in codebases
    • Good at using tests as feedback
    • Often used for benchmark-style “fix the bug” workflows, but works well as a general repair agent too
  3. Aider

    • Excellent if you want a more controlled, human-in-the-loop workflow
    • Great for making targeted repo changes and then running tests yourself
    • Less autonomous than OpenHands/SWE-agent, but often safer on legacy systems

If you want the most autonomous option

  • OpenHands is probably the best starting point for “run tests and fix failures across a legacy codebase.”

If you want the most test-fix focused option

  • SWE-agent is very strong for iterative debugging and patching.

If you want something safer and more interactive

  • Aider is a good choice.

What to look for in setup

For legacy codebases, the agent should ideally have:

  • Docker or sandboxed execution
  • Access to the project’s normal test commands
  • Support for patching files incrementally
  • Ability to read build/test logs
  • Configurable stop conditions, so it doesn’t over-edit

If you want, I can also give you:

  1. a ranked shortlist by ease of use,
  2. a comparison table, or
  3. a recommended setup for a specific stack like Python, Java, JS/TS, or Ruby.

Observation history

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