---
title: "Comment by Tech Simplified on \"AI Agents Memory: Mem0 + LangGraph Agent Integration\" | Hashnode"
description: "This is exactly what I was looking for! The integration between Mem0 and LangGraph opens up so many possibilities for stateful AI agents. The step-by-step appro"
url: "https://hashnode.com/posts/ai-agents-memory-mem0-langgraph-agent-integration/6874e8edbec864eaa22bbaca/comment/698a6836575957db90801523"
---

[Discussion](https://hashnode.com/posts/ai-agents-memory-mem0-langgraph-agent-integration/6874e8edbec864eaa22bbaca)

Comment [AI Agents Memory: Mem0 + LangGraph Agent Integration](https://hashnode.com/posts/ai-agents-memory-mem0-langgraph-agent-integration/6874e8edbec864eaa22bbaca)

[Tech Simplified](https://hashnode.com/@techsimplifiednews)

[Feb 9](https://hashnode.com/posts/ai-agents-memory-mem0-langgraph-agent-integration/6874e8edbec864eaa22bbaca/comment/698a6836575957db90801523)

This is exactly what I was looking for! The integration between Mem0 and LangGraph opens up so many possibilities for stateful AI agents. The step-by-step approach makes it really accessible. Question: how does the memory retrieval performance scale as you accumulate more conversational history? Have you experimented with memory pruning strategies for production use?