LangGraph 0.4 Agent Memory 2026: Checkpointers, Stores & Recall

Your LangGraph agent works fine in the demo, then falls apart in week two: the thread state balloons past the context window, the agent “forgets” what the user told it three sessions ago, and the same fact gets written to the store four times with three different spellings. You wire up a checkpointer, then discover it only saves the thread — not the user. You bolt on a vector store, then watch recall pull back stale preferences the user reversed a month ago. Meanwhile 0.4 shifted the APIs under you, the tutorials you’re copying are built on 0.2-era patterns, and every hot-path memory write adds latency your users feel on every turn.

This is for developers who already ship LangGraph — you have built graphs, you know nodes and edges, you can read Python type hints and you are comfortable with async. You should know what an embedding is. It is not an intro to LLMs, not a LangChain tutorial, and not a general RAG course; it assumes you can stand up Postgres or accept SQLite for local work. Framework comparison is covered, but the core is LangGraph itself.

Be honest about the ceiling: LLMs are genuinely good at extracting structured facts from messy conversation and at deciding what is worth remembering in the moment. They are unreliable at resolving contradictions between an old memory and a new one, at knowing when a fact has expired, and at not confidently persisting something the user never said. Anything that writes durably to a user’s long-term profile deserves a review path — automated extraction plus human-reviewable memory is the pattern that survives production, and this guide treats that as non-negotiable rather than optional polish.

What This Guide Covers

• A clear mental model of the full memory stack — state, checkpointers, stores, and recall — so you stop conflating short-term threads with long-term knowledge
• How to choose between MemorySaver, SqliteSaver, and AsyncPostgresSaver, and what each one actually costs you in production
• Long-term memory that persists across threads and sessions: namespace design, key strategy, and semantic search over a BaseStore
• The three memory types every real agent needs — semantic, episodic, and procedural — and how to tell which one your feature actually requires
• Schema design with Pydantic, including the profile-versus-collection decision that quietly determines how hard your memory is to update later
• When to write memory on the hot path versus offload it to a background subgraph, and how to keep per-turn latency from creeping
• Reliable structured memory updates with Trustcall, so patching an existing record doesn’t clobber the fields you meant to keep
• Concrete tactics for token bloat: trimming, summarization nodes, and message deletion that doesn’t corrupt your checkpoint history
• Vector recall that holds up under load — pgvector setup, embedding choices, and how to measure whether retrieval is actually returning the right memories
• Deduplication, conflict resolution, TTL, and deliberate forgetting policies, so your store doesn’t become a landfill of near-duplicate facts
• Human-in-the-loop memory review and time-travel debugging to inspect, rewind, and correct what your agent believes
• The real cost picture: latency budgets, storage growth, and the token economics of carrying memory into every call
• Ten specific failure modes that break agent memory in production — what causes each one and how to catch it before your users do
• Deployment and evaluation guidance, plus a straight comparison of building on LangGraph Platform versus adopting Mem0, Zep, or Letta

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