dbwls99706/deadends.dev

Overview

A Python-based infrastructure for capturing and structuring failure knowledge for AI agents. It models dead ends, workarounds, and error transitions as graphs, enabling agents to avoid repeated mistakes and recover from failures.

Best for

Best for
Developers building AI agents that need to learn from past failures.

Use cases

• Track known dead ends in agent workflows
• Document workarounds for common errors
• Build error transition graphs for debugging agent behavior

Notes

A Python-based infrastructure for capturing and structuring failure knowledge for AI agents. It models dead ends, workarounds, and error transitions as graphs, enabling agents to avoid repeated mistakes and recover from failures.

0 stars on GitHub. Last updated 2026-09-08. Licensed MIT.

Use cases

• Track known dead ends in agent workflows
• Document workarounds for common errors
• Build error transition graphs for debugging agent behavior

Pros

• Structured approach to failure knowledge
• Graph-based representation clarifies error paths
• Useful for improving AI agent reliability

Cons

• No stars or community traction yet
• Limited documentation or examples implied by early stage
• Python-only, may require integration effort

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