Trestle API Python API Docs | dltHub
Build a Trestle API-to-database pipeline in Python using dlt with AI harness support for Claude Code, Cursor, and Codex.
Last updated:
Trestle is a real estate data platform providing access to MLS data via a RESO Web API and OData compliant interface. The REST API base URL is https://api.cotality.com/trestle/odata and all requests require a Bearer token in the Authorization header.
dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv add "dlt[hub]" and start loading Trestle API data in under 10 minutes.
What data can I load from Trestle API?
Here are some of the endpoints you can load from Trestle API:
| Resource | Endpoint | Method | Data selector | Description |
|---|---|---|---|---|
| property | /trestle/odata/Property | GET | value | Main property listing resource |
| media | /trestle/odata/Media | GET | value | Media (photos) resource |
| member | /trestle/odata/Member | GET | value | Real estate agent/member data |
| office | /trestle/odata/Office | GET | value | Real estate office data |
| open_house | /trestle/odata/OpenHouse | GET | value | Open house schedules |
How do I authenticate with the Trestle API API?
The service uses OAuth2 client_credentials flow, requiring a POST request to the token endpoint to obtain a Bearer token; this token is then passed in the Authorization header for subsequent API calls.
1. Get your credentials
To obtain credentials for the Trestle Identity APIs, sign up for a Developer Portal account at https://portal.trestleiq.com/signup. Once your account is verified, log in to the Trestle Developer Portal to access your account dashboard, where you can generate and manage your API keys. Your API key is used as the primary authentication method by including it in the x-api-key HTTP header in your requests. Note that Trestle also provides real estate-specific data (RESO Web API) which uses a separate OAuth2 client_credentials authentication flow.
2. Add them to .dlt/secrets.toml
[sources.trestle_api_source] api_key = "your_api_key_here"
dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.
How do I set up and run the pipeline?
Set up a virtual environment and install dlt:
uv init uv add "dlt[hub]"
1. Install the dlt AI harness:
uv run dlthub ai init --agent <your-agent> # <agent>: claude | cursor | codex
This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →
2. Install the rest-api-pipeline toolkit:
uv run dlthub ai toolkit install rest-api-pipeline
This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →
3. Start LLM-assisted coding:
Use /find-source to load data from the Trestle API API into DuckDB.
The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.
4. Run the pipeline:
uv run python trestle_api_pipeline.py
If everything is configured correctly, you'll see output like this:
Pipeline trestle_api_pipeline load step completed in 0.26 seconds 1 load package(s) were loaded to destination duckdb and into dataset trestle_api_data The duckdb destination used duckdb:/trestle_api.duckdb location to store data Load package 1749667187.541553 is LOADED and contains no failed jobs
Inspect your pipeline and data:
uv run dlthub show
This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.
Python pipeline example
This example loads phone (for Identity APIs) and Property (for OData/RESO Web API) from the Trestle API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:
import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def trestle_api_source(token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://api.cotality.com/trestle/odata", "auth": {"type": "bearer", "token": token}, }, "resources": [ {"name": "property", "endpoint": {"path": "trestle/odata/Property", "data_selector": "value"}}, {"name": "media", "endpoint": {"path": "trestle/odata/Media", "data_selector": "value"}} ], } yield from rest_api_resources(config) def get_data() -> None: pipeline = dlt.pipeline( pipeline_name="trestle_api_pipeline", destination="duckdb", dataset_name="trestle_api_data", ) load_info = pipeline.run(trestle_api_source()) print(load_info)
To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.
How do I query the loaded data?
Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.
Python (pandas DataFrame):
import dlt data = dlt.pipeline("trestle_api_pipeline").dataset() sessions_df = data.property.df() print(sessions_df.head())
SQL (DuckDB example):
SELECT * FROM trestle_api_data.property LIMIT 10;
In a marimo or Jupyter notebook:
import dlt data = dlt.pipeline("trestle_api_pipeline").dataset() data.property.df().head()
See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.
What destinations can I load Trestle API data to?
dlt supports loading into any of these destinations — only the destination parameter changes:
| Destination | Example value |
|---|---|
| DuckDB (local, default) | "duckdb" |
| PostgreSQL | "postgres" |
| BigQuery | "bigquery" |
| Snowflake | "snowflake" |
| Redshift | "redshift" |
| Databricks | "databricks" |
| Filesystem (S3, GCS, Azure) | "filesystem" |
Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.
Next steps
Continue your data engineering journey with the other toolkits of the dltHub AI harness:
data-exploration— Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.dlthub-platform— Deploy, schedule, and monitor your pipeline in production.
uv run dlthub ai toolkit install data-exploration uv run dlthub ai toolkit install dlthub-platform
Was this page helpful?
Community Hub
Need more dlt context for Trestle API?
Request dlt skills, commands, AGENT.md files, and AI-native context.