LangGraph does not stream one universal kind of “agent output.” The data you receive depends on the stream mode: it can be the graph’s full state, changes made by nodes, language-model message chunks, application-defined progress, or runtime diagnostics. For new applications, LangChain recommends event streaming, while stream modes remain useful for understanding runtime output and existing implementations.
What does LangGraph stream during agent execution?
A stream is an observation channel over a graph run. It exposes selected parts of execution as they happen; it does not mean every event is text intended for the user. The LangChain LangGraph streaming guide documents modes for observing state, node updates, model messages, custom application data, and execution activity.
The practical choice depends on what the consumer needs: synchronize a complete state, apply only changes, render generated text, show application progress, or inspect task and checkpoint activity.
| Mode | What it carries | Typical use | Granularity or requirement |
|---|---|---|---|
values |
The full graph state after each graph step | Keep a client’s view synchronized with accumulated state | Step-level snapshots |
updates |
Node or task names and the updates they return | Apply or inspect state changes without receiving a full snapshot each time | Step-level deltas; a step can emit multiple updates |
messages |
LLM message chunks paired with invocation metadata | Render model output as it is generated | Incremental message chunks, potentially token-level |
custom |
Arbitrary data emitted by graph code | Show application progress that is neither model text nor a state value | Defined by the application and its emissions |
checkpoints |
Checkpoint events in a format corresponding to graph-state inspection | Observe persisted state milestones | Requires a checkpointer |
tasks |
Task start and finish events, including results and errors | Inspect task lifecycle | Requires a checkpointer |
debug |
Checkpoint and task events with additional metadata | Detailed runtime inspection | Diagnostic output; inspect rather than display unfiltered to end users |
These descriptions are documented in the LangGraph streaming guide and the Python StreamMode API reference. Exact output shape depends on the API version and configuration.
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What is the difference between LangGraph values and updates?
values: accumulated state snapshots
values provides the full state after a graph step. Choose it when the consumer needs the current whole picture, such as a client that replaces its displayed state with each snapshot.
updates: changes returned by nodes or tasks
updates reports which node or task produced an update and what it returned, rather than repeating the entire accumulated state. It is useful when a consumer wants to apply only changes. Do not assume one update object per graph step: a step can emit multiple updates, so process all relevant chunks.
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A node can write tool results, routing information, or other values into state. Those are state changes; they are not automatically the same thing as text generated by a model. The LangGraph streaming guide describes the snapshot and update distinction.
How do I stream tokens from a LangGraph agent?
Use messages when the goal is to render incremental LLM output. The stream supplies message chunks together with metadata about the invocation. A UI can use the chunks to build up model text as generation proceeds, while keeping metadata available for handling the event appropriately.
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messages is not a substitute for updates or values: it reports model message output, whereas those modes report graph state changes or snapshots. A graph run can involve all three kinds of information, each answering a different question.
How can I stream custom progress events from a LangGraph node?
Use custom for application-defined data emitted by graph code through the stream writer. It fits progress such as “searching documents” or a percentage when that information is not naturally a state value or model message. Your application defines what the progress payload means and how the UI presents it.
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This separation lets an interface show useful activity without treating every runtime event as user-facing prose. The LangGraph streaming guide documents both custom data and message streaming.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When should I use checkpoints, tasks, or debug streaming?
Use checkpoints to observe checkpoint events, or tasks to inspect task starts and finishes, including results and errors. Both modes require a checkpointer. debug combines checkpoint and task events with additional metadata for closer runtime inspection.
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These modes are primarily for observability and debugging. If an end-user interface consumes them, filter and translate the events deliberately rather than exposing diagnostic payloads as ordinary progress or chat output.
How do stream modes relate to event streaming and API versions?
Event streaming for new applications
The LangChain LangGraph streaming documentation states: “For new applications, we recommend event streaming—the typed-projection API introduced in LangGraph v1.2.” Event streaming provides separate iterators for projections such as messages, values, subgraphs, and output. Stream modes remain documented for direct access to graph-runtime events or a particular mode’s output. See the official streaming guide.
Unified v2 stream-mode chunks
The current guide documents version="v2" as a unified chunk format with type, ns, and data, regardless of stream mode, the number of modes, or subgraph settings. Consumers can dispatch on type; ns carries namespace information for subgraph events.
The guide also describes v1 output as varying with whether one or multiple stream modes are selected and with subgraph settings. When reading or writing code that consumes chunks, verify the installed LangGraph version and its language-specific documentation. The cited guide does not establish a complete Python, JavaScript, and provider compatibility matrix or a universal migration recipe.
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- Need the whole current graph state after each step? Choose
values. - Need to know what nodes or tasks changed? Choose
updates. - Need to render generated model text incrementally? Choose
messages. - Need application-defined progress? Emit data from graph code and consume
custom. - Need checkpoint or task lifecycle visibility? Use
checkpointsortaskswith a checkpointer. - Need detailed runtime inspection? Use
debug, and filter its output before exposing anything to an end user.
The mode determines the meaning of each payload. Treating state snapshots, deltas, model chunks, custom progress, and diagnostics as interchangeable is the main source of confusion when building a streaming consumer.
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