> ## 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.

# Tracking Conversations

> Group related traces into conversations using thread_id so you can view and evaluate entire chat sessions in LangWatch.

When building chatbots or multi-turn agents, each user message creates a separate trace. To group these traces into a single conversation, set the `langwatch.thread.id` attribute on the root span.

## Setting the thread\_id

Inside any traced operation, use `setAttributes()` on the span:

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

setupObservability();

const tracer = getLangWatchTracer("my-chatbot");

async function handleMessage(threadId: string, userId: string, message: string) {
  return await tracer.withActiveSpan("HandleMessage", async (span) => {
    span.setAttributes({
      "langwatch.thread.id": threadId,
      "langwatch.user.id": userId,
    });

    // your LLM pipeline logic here...
  });
}
```

All traces that share the same `langwatch.thread.id` will be grouped into a single conversation thread in the LangWatch dashboard.

You can also use the typed attribute constants:

```typescript  theme={null}
import { attributes } from "langwatch";

span.setAttributes({
  [attributes.ATTR_LANGWATCH_THREAD_ID]: threadId,
  [attributes.ATTR_LANGWATCH_USER_ID]: userId,
});
```

## Example: Express Chatbot

```typescript  theme={null}
import express from "express";
import { setupObservability } from "langwatch/observability/node";
import { getLangWatchTracer } from "langwatch";
import OpenAI from "openai";

setupObservability();

const app = express();
const tracer = getLangWatchTracer("my-chatbot");
const openai = new OpenAI();

app.post("/chat", async (req, res) => {
  const { threadId, userId, message } = req.body;

  const reply = await tracer.withActiveSpan("HandleMessage", async (span) => {
    span.setAttributes({
      "langwatch.thread.id": threadId,
      "langwatch.user.id": userId,
    });

    // Fetch conversation history from your database using threadId
    const history = await getConversationHistory(threadId);

    const response = await openai.chat.completions.create({
      model: "gpt-4.1",
      messages: [...history, { role: "user", content: message }],
    });

    return response.choices[0]?.message?.content;
  });

  res.json({ reply });
});
```

The `threadId` is typically the conversation or session ID from your application. It can be any string, as long as it's consistent across all messages in the same conversation.

## What You Get

Once traces share a `thread_id`, you can:

* **View the full conversation** in the LangWatch dashboard by clicking on any trace in the thread
* **Run evaluations by thread** to assess conversation-level quality (see [Evaluation by Thread](/evaluations/online-evaluation/by-thread))
* **Build datasets from threads** for testing multi-turn scenarios (see [Dataset Threads](/datasets/dataset-threads))
* **Filter and search** traces by conversation in the messages view