> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# Trace a LangGraph agent node by node

> Pass the LangWatch callback to your graph's invoke and every node, tool call and LLM call becomes a span you can open.

To trace a LangGraph agent with LangWatch's open-source LLM observability, pass `get_langchain_callback()` to the run of the compiled graph. LangGraph hands that callback to every node it runs, so each node is recorded as a span inside one trace per run, and so is each tool and model call made through a LangChain or LangGraph runnable. A call your node makes with a provider SDK directly needs that SDK instrumented as well.

With LangWatch that is one decorator and one callback in Python, or one `withConfig` call in TypeScript. You then see which node ran, in which order, how often the agent looped, and the exact prompt and tool arguments at each step. Both snippets are in [Trace the graph run in Python or TypeScript](#1-trace-the-graph-run-in-python-or-typescript).

## Why trace the graph and not only the model

A graph agent fails between the model calls: a router picks the wrong edge, a tool node gets the wrong arguments, the agent loops between two nodes until it hits the recursion limit. The final answer shows none of that.

A trace of the whole run shows each step as a span, nested in the order the graph ran them, with its input, its output, its time and its tokens. You get that only when the callback sits on the graph run, as step 1 shows.

## 1. Trace the graph run in Python or TypeScript

Install the SDK and set `LANGWATCH_API_KEY`, which you find in your [project settings](https://app.langwatch.ai/settings):

```bash theme={null}
uv add langwatch
```

Then wrap the function that runs the graph and pass the callback in the run's config:

```python theme={null}
import langwatch
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableConfig

from my_app.graph import agent  # your compiled StateGraph

langwatch.setup()


@langwatch.trace(name="support-agent")
def answer(question: str) -> str:
    result = agent.invoke(
        {"messages": [HumanMessage(content=question)]},
        config=RunnableConfig(
            callbacks=[langwatch.get_current_trace().get_langchain_callback()]
        ),
    )
    return result["messages"][-1].content
```

`@langwatch.trace` opens one trace per call, and the callback records everything the graph does inside it. The same `config` works for `ainvoke`, `stream` and `astream`.

Put the callback on the graph run, as above. A callback attached only to the model inside one node records only that model's calls, so the tool nodes, the plain Python nodes and the order they ran in never appear.

For TypeScript, install the SDK:

```bash theme={null}
npm install langwatch
```

Then attach the handler to the compiled graph:

```typescript theme={null}
import { setupObservability } from "langwatch/observability/node";
import { LangWatchCallbackHandler } from "langwatch/observability/instrumentation/langchain";

setupObservability({ serviceName: "support-agent" });

const agent = graph
  .compile({ checkpointer })
  .withConfig({ callbacks: [new LangWatchCallbackHandler()] });
```

[LangGraph for TypeScript](https://langwatch.ai/docs/integration/typescript/integrations/langgraph.md) has the full example.

## 2. Read a run

Run the agent once, then open the project in LangWatch. The trace appears within a few seconds:

* **Each node is a span** named after the node, nested in the order the graph ran it, so a loop shows up as the same nodes repeating.
* **Each model call is an LLM span** with the full prompt, the response, the model, the tokens and the cost.
* **Each tool call is a tool span** with the arguments the model chose and what the tool returned.

When a run goes wrong, open its trace and walk the spans down to the first one whose output is not what you expected. That is usually the node to fix.

## 3. Group a conversation's runs

Each `invoke` is its own trace. A chat agent that runs the graph once per user message needs those traces grouped into one conversation.

LangGraph's own `thread_id` in `configurable` does not group them: LangWatch groups conversations by its own `thread_id` metadata. Set it to the same value:

```python theme={null}
import langwatch
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableConfig

from my_app.graph import agent  # your compiled StateGraph

langwatch.setup()


@langwatch.trace(name="support-agent")
def answer(thread_id: str, user_id: str, question: str) -> str:
    trace = langwatch.get_current_trace()
    trace.update(metadata={"thread_id": thread_id, "user_id": user_id})

    result = agent.invoke(
        {"messages": [HumanMessage(content=question)]},
        config=RunnableConfig(
            callbacks=[trace.get_langchain_callback()],
            configurable={"thread_id": thread_id},
        ),
    )
    return result["messages"][-1].content
```

The traces then show as one conversation in LangWatch, and you can filter by user. In TypeScript, set `langwatch.thread.id` on the span around the run; see [Tracking conversations](https://langwatch.ai/docs/integration/typescript/tutorials/tracking-conversations.md).

## 4. Go from traces to tests

Once the runs are traced, you can check them in the same project:

* **Score live runs.** An [online evaluation](https://langwatch.ai/docs/evaluations/online-evaluation/setup-monitors.md) scores incoming traces, for example on whether the answer is grounded in what a tool returned.
* **Test the graph before release.** A [simulated-user test](https://langwatch.ai/docs/agent-testing/simulated-users.md) runs your graph against a model that plays the user, and its runs are traced the same way.
* **Debug from your editor.** With the [LangWatch MCP server](https://langwatch.ai/docs/integration/mcp-debug-traces.md), your coding assistant can open the failing trace next to the graph code.

## Questions about tracing LangGraph with LangWatch

### Does LangWatch trace LangGraph's prebuilt agents

Yes. `create_react_agent` and other prebuilt graphs are compiled graphs, so the callback goes in the same `config`.

### Do I need LangSmith to trace LangGraph with LangWatch

No. The callback is a standard LangChain callback, so it works without a LangSmith account, and it can run next to LangSmith's tracer if you use both.

### Can I self-host LangWatch for LangGraph tracing

Yes. Point `LANGWATCH_ENDPOINT` at your own instance; see [Self-hosting](https://langwatch.ai/docs/self-hosting/overview.md).

Next, add the callback to the place your code runs the graph and open the first trace, or read [LangGraph for Python](https://langwatch.ai/docs/integration/python/integrations/langgraph.md) for a complete runnable agent.


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