vercel-labs/ralph-loop-agent

What it solves

Standard AI agent loops often stop as soon as a model finishes its tool calls, which can lead to incomplete tasks or failures in complex workflows. Ralph-loop-agent provides "continuous autonomy," allowing an agent to iteratively work, verify its results, and retry with feedback until a specific goal is actually achieved.

How it works

It wraps the AI SDK's generateText in an outer loop. The process follows these steps:

1. Inner Loop: The AI agent uses tools to attempt the task.
2. Verification: A user-defined verifyCompletion function checks if the task is truly finished.
3. Feedback: If the task is incomplete, the reason for failure is injected back into the agent as context for the next iteration.
4. Termination: The loop continues until the verification function returns success or a safety stop condition (based on iterations, tokens, or cost) is met.

Who it’s for

Developers building complex AI agents that require high reliability for long-running tasks, such as codebase migrations, multi-file refactors, or any workflow requiring real-world verification.

Highlights

• Iterative Completion: Continues running until a custom verification function confirms success.
• AI SDK Compatibility: Fully compatible with AI SDK tools and AI Gateway string formats.
• Flexible Stop Conditions: Prevents infinite loops by limiting execution via iteration count, total token usage, or monetary cost.
• Context Management: Includes built-in summarization for long-running loops to manage context windows.
• Feedback Injection: Uses the results of failed verifications to guide the agent's subsequent attempts.