u/SkyWalkerr0x

Hey everyone,


I’ve been working with LangGraph for complex agent workflows, but one recurring pain point is managing memory across disjointed sessions. LangGraph checkpointers are great for thread-local state, but they don't easily allow an agent to "remember" something from a conversation two weeks ago in a brand-new thread.


I just submitted a PR to the **Memanto** repo that solves this with a Hybrid Memory approach.


How it works:

I wrapped the Memanto SDK into LangChain tools: remember, recall, and answer.

The agent uses Memanto as a global persistent brain that sits outside the standard graph state.

In my demo script, the agent learns specific facts in Session 1 and recalls them in Session 2 with a fresh thread_id using semantic search.

Why this matters:

Context Management: You don’t have to stuff the entire history into the prompt.

Knowledge Sharing: Different agents can share the same memory namespace.

RAG-on-the-fly: The agent can synthesize answers from its entire history using Memanto’s grounded RAG tools.

Check out the code here: https://github.com/moorcheh-ai/memanto/pull/440


Would love to hear how you guys are handling global agent memory!