databricks-solutions Run Agent Locally
Start the Server
uv run start-app
This starts the agent at http://localhost:8000
Server Options
# Hot-reload on code changes (development)
uv run start-server --reload
# Custom port
uv run start-server --port 8001
# Multiple workers (production-like)
uv run start-server --workers 4
# Combine options
uv run start-server --reload --port 8001
Test the API
Streaming request:
curl -X POST http://localhost:8000/invocations \
-H "Content-Type: application/json" \
-d '{ "input": [{ "role": "user", "content": "hi" }], "stream": true }'
Non-streaming request:
curl -X POST http://localhost:8000/invocations \
-H "Content-Type: application/json" \
-d '{ "input": [{ "role": "user", "content": "hi" }] }'
Run Evaluation
uv run agent-evaluate
Uses MLflow scorers (RelevanceToQuery, Safety).
Run Unit Tests
pytest [path]
Troubleshooting
| Issue | Solution |
|---|---|
| Port already in use | Use --port 8001 or kill existing process |
| Authentication errors | Verify .env is correct; run quickstart skill |
| Module not found | Run uv sync to install dependencies |
| MLflow experiment not found | Ensure MLFLOW_TRACKING_URI in .env is databricks://<profile-name> |
MLflow Experiment Not Found
If you see: "The provided MLFLOW_EXPERIMENT_ID environment variable value does not exist"
Verify the experiment exists:
databricks -p <profile> experiments get-experiment <experiment_id>
Fix: Ensure .env has the correct tracking URI format:
MLFLOW_TRACKING_URI="databricks://DEFAULT" # Include profile name
The quickstart script configures this automatically. If you manually edited .env, ensure the profile name is included.
Next Steps
- Modify your agent: see modify-agent skill
- Deploy to Databricks: see deploy skill