Skip to main content
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 Set your API key as an environment variable: 
export LANGSMITH_API_KEY=your_api_key_here
Optionally, if you’re using a custom LangSmith endpoint: 
export LANGSMITH_ENDPOINT=https://your-custom-endpoint.com

​
Running the Export Script

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

​
Output Format

The script creates a langsmith_traces.csv file with 21 columns per LLM call:
CategoryColumns
Coretrace_id, run_id, parent_run_id, input_messages, output_message
Modelmodel, provider, temperature, finish_reason
Statusstatus, error
Tokenstotal_tokens, prompt_tokens, completion_tokens
Costtotal_cost, prompt_cost, completion_cost
Performancelatency_ms, start_time, end_time
Metadatatags
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