NOW you can let AI agents make code changes without losing engineering control.
RunWield runs entirely on your machine. Use any model subscription or API key. No vendor lock-in.
curl -fsSL https://raw.githubusercontent.com/RunWield/runwield/main/install.sh | bashIntroducing RunWield
From chat and hope, to plan and prove.
Finally, a coding harness optimized for more than just getting an agent spitting out code as fast as possible.
Key features
Any model. Subscription or API key.
Use the models you want with the subscriptions or API keys you already have. Switch providers as your needs change.
All local. Yours to control.
RunWield runs on your machine. Your Plans, project context, memory, and Work Records live alongside your work.
Review the Plan before the code.
Shape the approach, add context, and choose how the agent should work before execution begins.
Validation built into delivery.
Run project checks, get an independent AI review, and inspect the diff. Findings feed directly into repair.
Every change informs the next.
Carry decisions and lessons forward through memory and Work Records, giving the next Plan a better starting point.
Make the harness fit your workflow.
Work from your terminal, browser, IDE, or chat. Extend RunWield with skills, MCP servers, and prompt templates.
Engineers who want more from agents than faster code generation.
- 01 Experienced developers working in real, existing codebases
- 02 Teams learning where agents should collaborate and where they can run
- 03 Anyone turning ambiguous product or engineering work into validated change
Build faster. Keep the understanding.
Steve Krouse’s “Vibe code is legacy code” captures the concern that led me to build RunWield: it’s easy to accumulate code faster than you understand it.
I built RunWield around reviewing the approach, checking the result, and carrying decisions and lessons into the next change. The aim is for your understanding to grow alongside your codebase.
Planning gets better when everyone can point at the same thing.
A Plan is not ceremony. It is the shared working surface where intent becomes specific enough for agents to act without guessing.
The agent said it's done. RunWield delivers proof.
- 01Mechanical validation
Tests, types, lint, formatting, and project-specific checks run automatically. Failures go back through automatic repair.
- 02Automated LLM reviewer
An independent reviewer compares the implementation with the Plan. Material findings trigger another repair pass.
- 03Human review, guided
Inspect the diff, follow review cues, annotate exact lines, and approve only when the evidence supports the result.
Help shape a better way to build with agents.
We're looking for experienced developers using AI on real codebases.