How do I build a multi-agent system with LangGraph? Define the agents’ responsibilities, the state they can read and update, and the graph’s control flow—including how it pauses, resumes, and recovers. LangGraph provides orchestration infrastructure for those decisions; it does not make them for you. The practical companion question is: Should you use a supervisor or let agents hand off work to one another? Choose based on who should route the work and what information should cross each handoff.
What LangGraph provides—and what your application must decide
The LangGraph reference maintained by LangChain describes it as “a low-level orchestration framework for building, managing, and deploying long-running, stateful agents.” In practice, the framework gives an application explicit graph state and control flow, along with mechanisms for persistence, streaming, and human-in-the-loop pauses.
Your team still designs the system: which agents exist, which tools they may use, how work is routed, which state each agent sees, and what happens when a node or external action fails. A graph can make those choices visible and manageable; it does not guarantee correct delegation, useful specialist responses, lower costs, or safer actions. Those outcomes depend on the models, prompts, tools, application policies, and evaluation.
LangGraph’s lower-level control is suited to workflows where explicit composition of deterministic steps and agent behavior matters. LangChain’s prebuilt agent architectures may be a better fit when their built-in structure matches the task and a quicker setup matters more than custom workflow control.
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Supervisor, handoff, or custom graph?
The key difference between supervisor and handoff designs is routing ownership: a central supervisor chooses the next specialist, while a handoff-capable agent can yield control to another agent as the task develops. A custom graph gives the application more direct control over those transitions and other workflow steps.
| Pattern | Who routes the work? | What information crosses a transition? | Useful when |
|---|---|---|---|
| Supervisor | A central agent selects and coordinates specialists. | The parent can be configured to receive a worker’s last answer or fuller history; choose deliberately. | One component should own task decomposition and specialist selection. |
| Handoff or swarm-style routing | An agent can transfer control to another agent through a tool-based handoff. | The swarm package documentation says subagent state updates are applied to the parent graph state by default during handoff. | Responsibility may move among agents as the task develops. |
| Custom graph or subgraphs | The application defines graph nodes and transitions, including where agent decisions occur. | State crossing a subgraph boundary depends on the graph and persistence design; visibility should not be assumed. | Workflow control, specialist encapsulation, or a mix of deterministic and agentic steps warrants the implementation effort. |
What a supervisor adds
A supervisor centralizes task decomposition and routing. That can make the decision about which specialist runs next explicit, and hierarchical systems can use multiple levels of supervisors. The trade-off is an additional central decision point: the supervisor must choose appropriate workers and provide enough context for them to succeed. Its presence alone does not make delegation accurate or efficient.
Also decide what the parent receives when a worker finishes. Passing only the worker’s last answer can keep the parent’s view focused; passing fuller history can preserve context the worker used. These are different information boundaries, not just display options.
What handoffs change
With handoffs, routing can follow the task’s evolving needs rather than always returning to one central selector. That flexibility makes the transition contract especially important. Since the swarm package applies subagent state updates to the parent graph state by default, review which messages and structured values travel onward. Broad propagation may preserve continuity, but it can also carry unnecessary, sensitive, or oversized history.
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“Swarm” describes a routing style here, not a guarantee of unconstrained autonomy or a claim that it is better than a supervisor. The meaningful design choice is whether a central router or a worker chooses the next agent, and whether the transition carries selected outputs or shared state.
When to build a custom graph
Choose a custom graph when you need explicit control over routing, state transitions, deterministic checks, or the placement of human review. Subgraphs can encapsulate a specialist workflow, but a parent graph may not immediately see a subgraph’s updates. When data must cross that boundary, the persistence documentation describes using shared Store state or writing to the parent checkpoint as possible approaches.
More control also means more workflow behavior for your team to define and maintain. If a prebuilt agent architecture already fits the application, using it can avoid unnecessary orchestration code.
Design state boundaries before adding agents
Multi-agent systems often fail in less visible ways than a bad model response: a worker lacks necessary context, the next agent receives irrelevant history, or durable user information is confused with the current conversation. Define what belongs in each state scope before adding more agents.
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- Worker input: Specify the messages and structured fields a specialist needs, rather than assuming every worker should see the full conversation.
- Worker output: Decide whether the worker returns a concise result, structured updates, or conversation history, and which of those the parent retains.
- Handoff scope: Review the default propagation of subagent updates in a swarm design; restrict or shape information where the application needs a narrower boundary.
- Subgraph boundary: Identify what remains inside a subgraph and what the parent must be able to read. Use a shared Store or parent-checkpoint writes when cross-boundary data needs call for them.
- Sensitive and bulky data: Avoid propagating information that the next agent does not need, particularly where user data or large histories are involved.
Checkpoints, stores, and recovery are different jobs
LangGraph’s persistence documentation distinguishes a checkpointer from a Store. A checkpointer records graph-state snapshots associated with a thread, supporting continuity, interruption, time travel, and recovery. A Store holds application-defined information across threads, such as durable facts or preferences. Thread-local conversation state is not automatically the same thing as cross-session or cross-user memory.
Make thread persistence durable on purpose
In-memory savers such as MemorySaver or InMemorySaver keep checkpoints in process memory. They lose those checkpoints when the process restarts, so they are not durable storage for production recovery. The documentation identifies persistent backends such as PostgreSQL and SQLite for durable checkpointing. Checkpoints can accumulate over time; define a retention or pruning policy rather than allowing storage growth to be accidental.
Thread-scoped persistence also depends on consistently passing the same thread_id when accessing a thread. The JavaScript persistence guide states that PostgresSaver limits thread IDs to 255 characters. If an external identifier might exceed that limit, use a short stable identifier or a hash while preserving a reliable mapping in the application.
Understand what resumption does—and does not—promise
Checkpointing can preserve successful node writes when another node fails, allowing the graph to resume without rerunning completed work. This recovery behavior is not a blanket exactly-once guarantee for external side effects. If a node calls a payment service, sends a message, or changes another system, design that operation’s retries and duplicate protection separately.
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A shared Store can make durable information available across threads, but the framework’s cross-thread capability does not define who is authorized to read or update each user’s data. Establish tenant boundaries and authorization checks in the application before storing user information there.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use interrupts for review and resumption
An interrupt pauses graph execution to request external input. The interrupt guide describes saving the state and waiting until the caller resumes the run by invoking the graph with a Command carrying the resume value. This supports approval gates, edits to proposed tool calls, and collection or validation of user input.
For tool-call review, the documented interaction options include approving and continuing, manually modifying the call, or giving the agent natural-language feedback. Choose a review point where the consequences justify human attention, and make the interrupt payload clear enough for the reviewer to understand what they are being asked to approve or change. The application must still enforce its own permissions and action policies; pausing for review does not automatically make an action safe.
Stream progress and inspect nested work
LangGraph’s streaming guide describes graph stream modes and nested subgraph streaming. Namespaces can identify which subgraph emitted a message, helping developers distinguish parent activity from nested specialist work. Decide which events are appropriate to show end users; internal tool activity or intermediate reasoning may be useful for debugging without belonging in the product interface.
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For development, tracing and debugging streams can help follow agent and tool activity. The documentation also describes a typed-projection event-streaming API introduced in LangGraph v1.2 and recommends it for new applications on that guide page. Because API surfaces and recommendations can change, check the installed LangGraph version and its matching documentation before adopting that API.
Streaming makes activity observable; the documented capability does not establish that streaming improves model quality or reduces latency.
Evaluate the design against your workload
The official material describes capabilities and architecture, not an apples-to-apples performance comparison of supervisor, swarm, and custom-graph implementations. It establishes no universal winner for latency, cost, or accuracy. Test representative tasks using your own workload and evaluation criteria before committing to a pattern.
Quick Recap
- Compare whether routing choices are appropriate, including cases where a specialist is unavailable or returns an unusable result.
- Inspect what context each worker receives and what state is propagated to the parent or next agent.
- Exercise interruption, resumption, node failure, and external-action retry behavior.
- Measure the operational costs that matter to your application, including model calls, latency, and persistence needs, rather than inferring them from the pattern name.
- Check that streamed events and stored data respect your product’s user-facing and authorization boundaries.
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