Show HN: Agent-recall-AI – Auto-save for AI agents that die mid-task
agent-recall-ai is a tool that automatically saves the state of AI agents during long-running tasks, allowing them to resume from where they left off after interruptions like memory limits or session crashes. It works with existing AI coding environments like Claude Code, Cursor, and Windsurf without requiring code changes. The saved checkpoints include goals, decisions, constraints, and file changes in a structured format for accurate restoration.
- ▪agent-recall-ai provides auto-save functionality for AI agents that may lose progress due to context window limits or session failures.
- ▪It requires no code changes for Claude Code users and can be set up with two simple commands: 'pip install agent-recall-ai' and 'agent-recall-ai install-hooks'.
- ▪The tool captures structured data such as goals, constraints, decisions with reasoning, rejected alternatives, and modified files.
- ▪Users can resume interrupted sessions using the 'agent-recall-ai resume <session-name>' command, which generates a structured recap for the AI to continue seamlessly.
- ▪agent-recall-ai supports integration with development environments including Cursor and Windsurf through command-line flags.
Opening excerpt (first ~120 words) tap to expand
agent-recall-ai Your AI agent died mid-task. This is how it comes back. What is this for? (non-technical version) Imagine you ask an AI assistant to help you with a big project — writing a report, refactoring code, building an API. It works away for an hour, makes dozens of decisions, and then... it runs out of memory and forgets everything it was doing. When you start a new conversation, you're back to square one. agent-recall-ai is an auto-save for AI agents. Every decision the agent makes, every file it touches, every constraint you gave it — all saved automatically in a structured format. When the session dies, you get a compact briefing that any AI can read to pick up exactly where it left off.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.
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