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For most current LangChain agents, use a checkpointer to preserve state within a conversation thread and a store for useful information that should carry across threads. Many applications need both. Choose durable database-backed persistence for production, and decide deliberately what to retain, retrieve, and eventually remove.
Choose by scope and access pattern
Start with two questions: Does the information belong to one conversation, or should it be available in separate conversations? And is it graph state, or application-defined information that your code retrieves and updates?
| Need | Use | What it holds |
|---|---|---|
| Resume or preserve one conversation’s graph state | Checkpointer | Thread-scoped state, commonly including conversation messages |
| Make selected information available across conversations | Store | Application-defined items such as user preferences, facts, or shared knowledge |
The LangChain persistence guide describes these as complementary: a checkpointer tracks the current thread, while a store tracks durable information across threads. A store does not automatically preserve a conversation’s graph state, and a checkpointer does not by itself provide cross-thread memory.
Use a checkpointer for short-term, thread-scoped memory
In current LangChain agent guidance, short-term memory is part of the agent’s state. Conversation history is commonly stored under a messages key. A checkpointer saves state so a thread can continue later; the graph reads state at the start of a step and updates it as the agent runs, including after steps such as tool calls. The short-term memory guide shows that a thread_id in graph configuration identifies the conversation thread.
When creating an agent, configure a checkpointer if you need thread-level persistence. The persistence implementation determines whether saved state is temporary or durable; adding a checkpointer alone does not guarantee that state survives a process restart.
In-memory examples versus durable persistence
InMemorySaver (also referred to as MemorySaver in examples) is useful for quickstarts and local experiments. It keeps checkpoints in the running process, so they disappear when that process restarts. For a local file-based development setup, LangChain’s persistence guidance identifies SQLite as an option. For production, its documentation recommends a database-backed checkpointer and demonstrates PostgreSQL; integration documentation also includes MongoDB. The documentation does not establish a universal performance or reliability winner among database vendors.
Use a store for selected information across threads
A store holds application-defined data outside the current graph state. Nodes or application code can read and write those items when needed, making a store suitable for information that should follow a user or be shared across conversations. Examples include a stated preference or a fact that the application has chosen to retain.
Design namespaces and access rules so one user’s stored information cannot be exposed to another. The LangMem conceptual guide discusses namespace-based scoping. A namespace is part of the application’s isolation design; it does not eliminate the need to enforce authorization in the surrounding application.
Decide what deserves to become memory
Before implementing long-term memory, identify what the agent needs to learn or do later. LangMem distinguishes three useful categories:
- Semantic memory: facts and knowledge.
- Episodic memory: past interactions, examples, actions, and outcomes.
- Procedural memory: instructions, workflows, and behavior patterns.
These categories help define what to capture and how it might affect a later run; they are not a requirement to implement three separate storage systems. In its June 24, 2026 article on building memory into agents, LangChain describes a cycle of capturing traces, analyzing them for useful signal, and updating retrievable context. A transcript or log records what happened; it becomes memory only when a useful lesson is turned into information the agent can retrieve later.
Memory is not automatically RAG or logging
If a document corpus is the authoritative source and does not depend on interaction history, retrieval over that corpus may be enough. That is different from agent memory built from selected information learned through interactions and made available to influence a later run. Keep traces for debugging or analysis when useful, but do not assume every trace or retrieved document belongs in durable memory.
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Set up database schemas
Database-backed persistence requires schema setup. LangChain’s memory guide notes that implementations commonly expose a setup() method, while advising developers to check the specific implementation. Treat schema creation or migration as an explicit deployment concern: verify the integration’s requirements and run setup as a dedicated deployment step or ensure it is handled at startup.
Manage history and checkpoint growth
Long conversations can exceed a model’s context window. Even when the full history fits, LangChain warns that very long context can make models attend to stale or irrelevant material, increase response latency, and raise costs. Choose a policy for trimming, deleting, or summarizing messages that matches the application’s needs.
Checkpoints can also accumulate as a thread continues, increasing storage use and potentially affecting latency. The persistence guide recommends pruning old checkpoints or setting a retention policy. These are related but distinct tasks: reducing the messages supplied as context does not necessarily remove persisted checkpoint records.
Use stable, appropriately scoped identifiers
Checkpointer operations are associated with a thread_id, so use an identifier that consistently refers to the intended thread without crossing user or tenant boundaries. LangChain’s persistence guide recommends keeping PostgresSaver thread IDs under 255 characters; that limit is specific to the documented PostgresSaver implementation, not a general rule for every checkpointer. Design store namespaces with the same care to prevent cross-user memory leakage.
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Test memory quality, not just storage
Saving data successfully does not prove that it improves later behavior. Select a small, useful subset of information, confirm that future runs actually retrieve it, and use evaluations to protect important behavior as memory policies change. A memory item that is stale, irrelevant, or never loaded adds complexity without helping the agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical implementation decision
- Define the scope. Keep conversation continuity and resumable graph state on the thread-scoped path; put only information that should cross threads in a store.
- Select a persistence implementation. Use an in-memory saver for an ephemeral example, or select a database-backed integration appropriate to the deployment when state must survive restarts.
- Complete setup. Check the chosen integration’s schema and migration requirements, then include setup in deployment planning.
- Set the memory policy. Decide what to retain, how it is retrieved, how long it remains useful, and how messages and checkpoints are trimmed or pruned.
- Verify the outcome. Test that a thread resumes as intended, cross-thread data is scoped correctly, and retrieved information changes later runs in the desired way.
Older examples built around langchain.memory may describe legacy abstractions. For current agent development, reason in terms of thread state and checkpointers, cross-thread stores, and an explicit retrieval and retention policy.
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