Afterpaths

About

Afterpaths provides a unified view across all your AI coding tools, automatically capturing valuable context, identifying breakthroughs, costly dead ends, and recurring patterns. It generates actionable rules from these insights, ensuring your agents don't repeat past mistakes and become smarter with every session, ultimately saving time and resources. The tool also offers analytics to compare model performance and powerful search capabilities to quickly retrieve past discussions and architectural decisions, consolidating learning that would otherwise be lost across various tools.

Key Features

• Automated LLM-generated summaries extracting discoveries, dead ends, and decisions from sessions.
• Cross-session search capabilities to find past discussions and discoveries across all logged history.
• 2 GitHub stars
• Automatic extraction and export of actionable rules (gotchas, dead ends, decisions, patterns) to guide AI agents.
• Performance analytics for AI models, tracking tokens, activity, and error rates.
• Unified session logging and browsing across multiple AI coding tools (Claude Code, Cursor, Codex CLI).

Use Cases

• Preventing AI coding agents from repeating past mistakes and costly diversions by codifying learned lessons.
• Comparing the effectiveness and performance of different AI coding models and tools for specific projects.
• Retaining critical context, architectural decisions, and important discoveries from coding sessions for long-term knowledge retention.