Use LangGraph streaming to send output or progress to an application as a graph runs. Use LangSmith tracing to record and inspect what happened during a run. They solve different problems, so an app that needs both a responsive user experience and useful diagnostics can use them together.
What is the difference between streaming and tracing?
Streaming delivers runtime events to a caller while graph execution is in progress. Depending on the mode, those events can include message chunks, state changes, or progress data. Tracing records execution work—such as model, tool, and retrieval calls—so you can inspect its structure and inputs and outputs later.
In short, streaming answers “What can I show the user now?” Tracing answers “What happened during this operation, and where should I investigate?” One does not replace the other.
Should you use LangGraph streaming or LangSmith?
| Need | Better starting point | What it provides |
|---|---|---|
| Show model output as it is generated | LangGraph streaming with messages |
Incremental LLM message chunks and metadata from graph execution. |
| Show graph progress or changed state | LangGraph streaming with updates or custom |
State changes after graph steps, or application-defined progress payloads. |
| Investigate a slow or failed operation | LangSmith trace | Nested runs and execution data for an operation. |
| Follow an agent session across turns | LangSmith thread | Linked traces that preserve multi-turn structure and timing. |
| Read conversation messages in order | LangSmith trajectory | A flattened sequence of human, AI, and tool messages without nested run structure. |
| Deliver a responsive interface and diagnose behavior | Use both | Stream events to the application and use traces for execution inspection. |
Streaming is the better fit for application-side delivery; tracing is the better fit for post-run investigation. Operational concerns such as privacy settings, latency, retention, and cost depend on the deployment and are not determined by these capabilities alone.
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How do you stream tokens or progress from LangGraph?
The LangGraph guide documents synchronous stream() and asynchronous astream() iterators. Choose a mode based on what the consumer needs rather than sending full state when only a smaller event will do.
messagesyields LLM message or token chunks and metadata.updatesyields state changes after graph steps.valuesyields the full state after each step.customyields data emitted by graph nodes, useful for application-defined progress events.- Other documented modes include checkpoints, tasks, and debug information.
API shape is version-sensitive. The current guide recommends its typed-projection event-streaming API for new applications and says it was introduced in LangGraph v1.2. For the stream-mode API, the guide says the unified v2 chunk format requires LangGraph 1.1 or later. Check the LangGraph streaming guide for the API and format matching your installed version.
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How do you debug a LangGraph run with LangSmith?
LangSmith represents execution with runs, traces, threads, and trajectories. A run is a unit of work, comparable to a span in OpenTelemetry terminology; runs for one operation make up a trace. A trace can expose nested model, tool, and retrieval work.
- Open a trace to inspect the details of one operation, such as where it failed or spent time.
- Use a thread to follow linked traces across a multi-turn interaction, including timing and nesting.
- Use a trajectory when you want the exchanged messages in order without the nested execution structure.
LangChain’s observability documentation specifies a limit of 25,000 runs per trace; additional runs sent after that limit is reached are rejected. This is a documented product limit, not a general performance benchmark. See LangSmith observability concepts for the definitions and product details.
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Can you use LangGraph streaming and LangSmith tracing together?
Yes. A typical division of responsibility is to stream the events your interface needs while tracing execution for later inspection. Streaming can provide incremental output or progress to a caller; tracing can help a developer understand the model, tool, and retrieval work behind that output. The documentation establishes these distinct capabilities, but deployment-specific privacy, retention, latency, and cost should be evaluated separately.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you enable LangSmith tracing for a LangChain app?
For LangChain applications in Python and JavaScript/TypeScript, the LangSmith quick start documents environment-based setup: enable tracing, provide an API key, and then run normal LangChain code. The quick start says traces go to the default project unless another project is configured. It also documents selective tracing and configuring a regional endpoint for accounts outside the default US region.
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LANGSMITH_TRACING=truein the application’s environment. - Set the LangSmith API key in the environment as directed by the setup guide.
- Run the LangChain application; configure a project or regional endpoint if needed.
The “no extra code” guidance applies after this environment configuration and to the documented LangChain setup; it should not be assumed to cover every framework or deployment. Follow the LangSmith tracing setup guide for current Python and JavaScript/TypeScript instructions.
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