Agent Memory: How Short-Term Memory Works

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Building stateful agents requires more than just a list of messages—it requires a system that can pause, save, and resume a conversation exactly where it left off. In LangGraph, this is handled by Checkpointers.
In this video, we discuss the mechanics of Short-Term Memory (or thread-level persistence).

You'll learn how LangGraph uses checkpointers to create a "persistence layer" for your agents, allowing them to maintain context across multiple user turns without manually passing the entire history back and forth. We’ll also explore the State Snapshot, the core object returned by a checkpointer that gives you a complete "time-travel" view of your agent's state at any given super-step.

What You Will Learn

Short-Term Memory Defined: Why "thread-scoped" memory is the foundation of multi-turn conversations.
The Role of Checkpointers: How objects like MemorySaver (in-memory) or PostgresSaver (production) automatically save the graph state after every node execution.
Understanding Threads: Using the thread_id to isolate different conversations and ensure users never see each other's data.
Working with Snapshots:
How to call graph.get_state(config) to retrieve a StateSnapshot.
Deconstructing the snapshot: values (the current state), next (the next node to execute), and metadata.
Time Travel Debugging: Using checkpoint_id and get_state_history to view and even resume from historical versions of the conversation.
Production Persistence: A quick look at migrating from InMemorySaver to persistent databases like SQLite or Postgres.

By the end of this video, you’ll understand how your agents "remember" in a way that is robust, scalable, and fully observable.

🔥 Ready to give your agent a memory that lasts? Master LangGraph Checkpointers!
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