Dev Tutorial: Give AI Agents Persistent Memory to Avoid Repeating Failed API Calls

A developer tutorial published on DEV Community demonstrates how to prevent AI agents from repeatedly making the same failed tool calls across sessions. The core problem is that when an agent encounters an error — such as an API rate limit — that failure is lost once the session ends, causing the agent to repeat the mistake indefinitely. The proposed solution is a Python-based MemoryGatekeeper class that uses the Mem0 library to store and retrieve outcomes of past tool calls via semantic search. Before executing any tool call, the gatekeeper checks memory for similar past failures and blocks the attempt if a relevant failure is found above a set similarity threshold. The setup runs locally with Python 3.10+, an OpenAI API key, and the mem0ai package, requiring no external Mem0 account or hosted service.