Use LangGraph’s streaming API to receive execution data while a graph runs, then render the events your interface needs. Choose updates for state changes, messages for model output chunks, or custom for progress signals you define. For new applications, the current LangChain guide recommends event streaming, a typed-projection API introduced in LangGraph v1.2.
How live execution works
A normal invoke call returns a result after execution. The graph’s stream method—and astream in Python for asynchronous iteration—lets your application consume output as execution proceeds. The stream provides data; your application decides how to display it. It does not create a visualization automatically.
LangGraph’s current streaming guide recommends event streaming for new applications. Introduced in LangGraph v1.2, this typed-projection API provides separate iterators for views such as messages, values, subgraphs, and output. The same guide documents stream modes for direct access to graph-runtime events.
Choose a stream mode for the view you want
| Mode | What it yields | Useful for |
|---|---|---|
updates |
State changes after graph steps; multiple updates in a step may arrive separately. | Showing incremental node or state progress without sending the entire state each time. |
values |
The full graph state after each step. | Displaying a current snapshot when the interface needs all state fields. |
messages |
Model message chunks with metadata; token-level output is available where supported. | Rendering a model response as it is generated. |
custom |
Application-defined updates emitted by graph nodes or tools. | Sending meaningful progress such as “searching documents” or “drafting response,” provided your code emits those signals. |
checkpoints |
Checkpoint events. | Observing persistence-related events; requires a checkpointer. |
tasks |
Task start and finish events, including node, result, and error details. | Inspecting task lifecycle; requires a checkpointer. |
debug |
Extensive execution information. | Detailed development-time inspection. |
tools (JavaScript) |
Tool lifecycle events such as start, intermediate event, end, and error. | Showing tool activity in a JavaScript application. |
The names and availability above follow the current documentation; verify the guide for the LangGraph version and language you use. In Python examples, the guide shows requesting multiple modes and consuming a typed chunk envelope in v2; that format requires LangGraph 1.1 or later.
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Match the stream to the interface
Show what changed
Use updates when a client needs incremental changes rather than a repeated full-state snapshot. For a state panel that should reflect the whole graph state after every step, use values. The distinction is the amount and shape of state data, not whether the graph produces a polished progress screen.
Stream the model’s response
Use messages to receive message chunks and metadata. This is the relevant view for progressively rendering generated text, where the model integration and runtime support chunked output. It is separate from node-level state updates: a token stream shows model output, not necessarily the full sequence of graph actions.
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Report application-specific progress
Use custom when the application needs status messages that are more useful to a person than raw state changes. The node or tool must emit those updates; LangGraph cannot infer labels such as “checking sources” from arbitrary graph logic. In JavaScript, the documented tools mode can expose tool lifecycle events directly.
Inspect runtime and persistence details
Use tasks for task lifecycle information and checkpoints for checkpoint events. Both require a checkpointer. Use debug when extensive runtime detail is useful during diagnosis, rather than treating it as a ready-made end-user display.
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Consume a hosted run’s events
For a hosted assistant run, LangSmith’s SDK documentation demonstrates asynchronously iterating over client.runs.stream(...) and reading each event’s type and data. That lets a client receive run events during execution; consult the assistant configuration documentation for the current API details.
Separate live UI streaming from tracing
Graph streaming is the data path an application uses to render progress, state, messages, or tool activity while a run is underway. LangSmith tracing serves a different purpose: inspecting and debugging traces and monitoring latency. Its online evaluation documentation also describes feedback from production traces. Streaming does not, by itself, mean that a trace is created, and LangSmith is not required to build a custom in-app live view.
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Tracing configuration for LangGraph Agent Server depends on deployment type. According to the LangSmith data plane documentation, Cloud traces to LangSmith SaaS; Hybrid can disable tracing or trace to LangSmith SaaS; Self-Hosted can disable tracing or send traces to LangSmith SaaS or Self-Hosted LangSmith. Check the current deployment documentation before relying on a particular tracing destination or default.
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A practical selection checklist
- For incremental state changes, choose
updates; for a complete state snapshot after each step, choosevalues. - For generated model text, choose
messages; for human-readable status your application defines, emitcustomupdates. - For task lifecycle or checkpoint events, configure a checkpointer and use
tasksorcheckpoints. - For detailed debugging, inspect
debugoutput or use LangSmith traces; do not expose raw debug data as a user-facing progress design by default. - Before implementing examples, confirm your language, LangGraph version, model integration, deployment type, and current account configuration against the official documentation.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
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