To see a LangGraph agent execute while it runs, stream events from the graph and render the updates your interface needs. Use updates for incremental state changes, messages for model output chunks, or custom for progress signals you define. For debugging and trace inspection, LangSmith complements—not replaces—your application’s live view.
Choose what to display while the graph runs
LangGraph’s stream and, in Python, astream methods yield data during execution. Unlike invoke, which returns a result, streaming lets your application receive intermediate information and decide how to present it. The stream mode determines what that information contains; the API supplies events or data, not a ready-made visual dashboard.
LangChain’s current streaming guide recommends event streaming—the typed-projection API introduced in LangGraph v1.2—for new applications. It provides separate iterators for projections such as messages, values, subgraphs, and output. The guide also documents stream modes for direct access to graph-runtime events. The v2 chunk format shown in the guide requires LangGraph 1.1 or later.
| What you want to show | Stream mode or projection | What arrives |
|---|---|---|
| Incremental changes as graph steps run | updates |
State changes after graph steps; multiple updates in a step can be emitted separately. |
| The complete graph state at each step | values |
Full state after each step, rather than just the changes. |
| Model output as it is generated | messages |
Message chunks and metadata, enabling token-level output where supported. |
| Application-specific progress messages | custom |
Updates explicitly emitted by your nodes or tools. |
| Checkpoint activity | checkpoints |
Checkpoint events; a checkpointer is required. |
| Task lifecycle and details | tasks |
Task start and finish events, including node, result, and error details; a checkpointer is required. |
| Extensive execution detail | debug |
Detailed runtime information intended for debugging. |
| Tool lifecycle events in JavaScript | tools |
Tool start, intermediate event, end, and error events, as documented for JavaScript. |
Pick the least noisy stream that answers the user’s question. A progress panel may need node updates or custom status messages, while a chat interface may need message chunks. Full state and debug output can expose more information than a user-facing display should show.
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Build a live progress view from stream events
A typical implementation consumes the iterator and maps each received event to an interface update. In Python, the basic shape is an asynchronous iteration over graph.astream(...); in JavaScript, use the graph’s streaming API and iterate over its yielded chunks. The exact chunk envelope depends on the selected API and version.
- Select a view: choose a stream mode or event-streaming projection based on whether the interface needs state deltas, full state, model messages, or custom progress.
- Start consuming before execution finishes: iterate over yielded chunks and handle each as it arrives instead of waiting for a final result.
- Render only relevant fields: translate the event into a concise status, token update, or state display. Keep internal details and sensitive values out of user-facing output.
- Handle completion and errors: stop the progress indicator when the run completes, and surface an appropriate failure state if execution reports an error.
For example, if a graph has steps that retrieve information and then draft a response, updates can tell your interface when state changes after each step. If you need a clearer message such as “Searching sources,” have a node or tool emit that status through custom. The interface remains responsible for turning those events into a useful progress display.
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Use LangSmith to inspect and debug runs
Application streaming and observability solve related but different problems. Streaming gives your application a live data path for showing progress, state, tool activity, or tokens. LangSmith helps developers inspect traces, debug streamed events, examine token-by-token output, and monitor latency; its online evaluation features provide feedback on production traces.
For a hosted run, the LangSmith SDK documentation shows asynchronously iterating over client.runs.stream(...) and printing each event’s type and data. That is a way for a client to receive run events during execution; it does not mean that every graph stream automatically creates a trace, or that LangSmith is required to build an in-app progress display. See the assistant run configuration documentation for the hosted-run example and the evaluation types documentation for online evaluation details.
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Check tracing behavior for your Agent Server deployment
If you use LangGraph Agent Server, trace destinations and available tracing choices depend on deployment type. The LangSmith data plane documentation describes these options:
| Deployment type | Tracing choices described in the 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. |
Confirm the current configuration for your deployment before relying on a particular trace destination. These Agent Server settings concern tracing; they do not determine which graph events your own application chooses to stream and render.
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Match the stream to the question you need to answer
- “Which step just changed the graph?” Start with
updates. - “What does the state look like after each step?” Use
values, keeping in mind it sends the full state at each step. - “What is the model saying as it generates?” Use
messageswhere the model integration supports streamed chunks. - “What should the end user see as progress?” Emit deliberate, readable signals with
custom, rather than exposing raw debug details. - “Why did this run fail or take time?” Inspect runtime details or traces in the appropriate debugging and observability tools; task and checkpoint modes require a checkpointer.
Available APIs and tracing behavior can change by LangGraph version and deployment. Check the current streaming guide for the language and API you use, especially before adopting the v1.2 event-streaming recommendation or the v2 chunk format.
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