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To share persistent memory across Python LangGraph agents, use a store for application data that agents need across threads, and a checkpointer for the execution state of each thread. A graph can use both. Share the store only among agents that are meant to access the same records, and design namespaces and permissions around your users and data boundaries.
Separate shared memory from thread state
LangGraph’s two persistence mechanisms solve different problems. A checkpointer saves graph state for a thread, supporting continuity and recovery when that thread is interrupted. A store holds application-defined records outside graph state, so agents can retrieve relevant information across threads. The LangGraph persistence documentation describes how a graph can be compiled with both.
- Checkpointer: preserve the state of a particular execution thread.
- Store: make selected application data available beyond one thread, including to multiple agents that share access.
Using a shared store does not, by itself, define who is allowed to read or change its contents. Treat identity, namespace design, and access control as application responsibilities.
Choose how you will provide the store
LangGraph’s persistence interfaces support database-backed implementations, while MemorySync documents an integration that supplies a LangGraph-compatible store and additional memory features. The choice depends on who will operate persistence and what retrieval behavior your application needs; the available documentation does not establish a fair cost, latency, scale, or retrieval-quality ranking.
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| Consideration | LangGraph-native persistence | MemorySync integration |
|---|---|---|
| Cross-thread memory | Use the store interface for application-defined records. | MemorySync documents a MemorySyncStore that implements LangGraph’s BaseStore. |
| Thread execution state | Use a checkpointer. | Keep LangGraph checkpoints separate; MemorySync describes its integration as supplying store, injection, persistence, and search components. |
| Operational ownership | Operate the selected database-backed store and checkpointer; database implementations can require operational ownership and migrations. | Use an external service for the documented memory integration. |
| Retrieval options | Use the store for cross-thread records; choose retrieval behavior to fit the application. | The guide documents a callable semantic-search tool. It also says index=False skips embedding and uses word-overlap ranking; this is the vendor’s description, not an independent performance result. |
LangGraph’s references include PostgreSQL-backed stores and checkpointers; its memory guide also names MongoDB, Redis, and Upstash as production store examples. See the LangGraph memory guide and Python reference for the supported interfaces and backend details.
Plan the memory contract before connecting agents
Before making a store common to multiple agents, decide what shared memory means in your application. A store is a persistence mechanism, not a policy for resolving conflicting facts or protecting one user’s records from another’s.
- Define what agents may write. Decide which facts belong in durable application memory and which should remain in a thread’s working state.
- Define who may retrieve each record. Choose identity and namespace boundaries that separate users, tenants, or other groups with different access rights.
- Limit each agent’s scope. Give an agent only the shared memory needed for its role rather than granting broad access by default.
- Specify updates. Decide how stale, corrected, or conflicting values are handled, including whether a new value replaces an old one or requires review.
These are application design decisions, not a universal policy prescribed by the LangGraph references. In particular, sharing one store among agents should not be treated as automatic tenant isolation.
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Build the LangGraph-native version
- Select durable implementations. Choose a store for cross-thread application records and a checkpointer for thread execution state. LangGraph’s persistence documentation lists backend options; confirm the current backend-specific setup and operational requirements in its persistence documentation.
- Compile the graph with both mechanisms. The documented pattern is to provide the store and checkpointer when compiling the graph. This lets the application retain thread continuity while exposing selected records through the store.
- Give participating agents the intended shared access. Configure agents that need common knowledge to use the same appropriately scoped store. Keep separate namespaces or other boundaries where users or roles must not share records.
- Implement retrieval and updates. Decide when agents consult the store and how they write or revise records. Match retrieval to the application’s needs rather than assuming persistence alone makes memory useful.
- Validate boundaries and recovery. Check that an agent can retrieve only the records its identity and role permit, that writes follow the memory contract, and that thread state remains available through the checkpointer when execution resumes.
The persistence guide provides the compile-with-both quickstart. Use the current reference for exact imports and backend-specific API details, since package versions and API patterns can change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add MemorySync when its integration fits
MemorySync’s vendor guide documents a MemorySyncStore as a LangGraph BaseStore, plus several ways to connect memory behavior to agents. Its stated Python requirements for the documented LangGraph integration are langgraph 1.2 or later and Python 3.10 or later. These are vendor-reported requirements; recheck the guide before installing because versions can change.
- For
create_agent, the guide documents middleware. - For
create_react_agent, it documents a pre-model hook. - An optional persistence node is available in the documented integration.
- A callable semantic-search tool is another documented option.
Choose only the integration components your agent design needs. The guide says the service embeds stored values server-side and that index=False skips embedding and uses word-overlap ranking. Those are MemorySync’s capability descriptions, not independently measured performance claims. See the MemorySync LangGraph guide for its current setup and API instructions.
Quick Recap
Check the design against your use case
- If an agent needs to resume its own conversation or interrupted execution, use thread-level checkpointing.
- If several threads or agents need selected durable application facts, use a store and explicitly scope the shared access.
- If you need a managed integration with documented injection and search components, evaluate MemorySync’s documented options against your requirements.
- If you need to choose among backends on cost, latency, scale, or retrieval quality, measure them in your own workload; the cited documentation does not provide a controlled comparison.
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