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

# Using Datawizz with LangSmith

> Export LangSmith traces and import them into Datawizz

LangSmith is LangChain's platform for debugging, testing, and monitoring LLM applications. If you have existing traces in LangSmith, you can export them and import them into Datawizz for training custom SLMs and evaluation.

## Exporting Traces from LangSmith

We provide a Python script that exports LangSmith traces to a CSV file compatible with Datawizz. The script extracts comprehensive metadata from your LLM calls including messages, tokens, costs, and latency information.

### Prerequisites

You'll need a LangSmith API key. You can get one from: [https://smith.langchain.com/settings](https://smith.langchain.com/settings)

Set your API key as an environment variable:

```bash theme={null}
export LANGSMITH_API_KEY=your_api_key_here
```

Optionally, if you're using a custom LangSmith endpoint:

```bash theme={null}
export LANGSMITH_ENDPOINT=https://your-custom-endpoint.com
```

### Running the Export Script

You can run the script using Python directly or with `uv`:

```bash theme={null}
# With Python
python export_traces_to_csv.py

# Or with uv (no manual dependency installation required)
uv run --with langsmith export_traces_to_csv.py
```

The script is available as a [GitHub Gist](https://gist.github.com/iddogino/ccddf21638e607a1b5b00ec557489999).

### Output Format

The script creates a `langsmith_traces.csv` file with 21 columns per LLM call:

| Category        | Columns                                                                   |
| --------------- | ------------------------------------------------------------------------- |
| **Core**        | `trace_id`, `run_id`, `parent_run_id`, `input_messages`, `output_message` |
| **Model**       | `model`, `provider`, `temperature`, `finish_reason`                       |
| **Status**      | `status`, `error`                                                         |
| **Tokens**      | `total_tokens`, `prompt_tokens`, `completion_tokens`                      |
| **Cost**        | `total_cost`, `prompt_cost`, `completion_cost`                            |
| **Performance** | `latency_ms`, `start_time`, `end_time`                                    |
| **Metadata**    | `tags`                                                                    |

The `input_messages` column uses OpenAI message format, making it directly compatible with Datawizz dataset imports.

## Importing into Datawizz

Once you have your exported CSV file:

1. Navigate to the **Datasets** tab in the Datawizz dashboard
2. Create a new dataset or open an existing one
3. Click **Import CSV** and select your `langsmith_traces.csv` file
4. Map the `input_messages` column to `input` and `output_message` to `output`

Read more about uploading CSVs and using them for training: [Using Datasets](/models/datasets)