Trestle API Python API Docs | dltHub

Trestle API is a real‑estate data platform exposing RESO WebAPI OData endpoints and identity/enrichment REST endpoints for property, media, and contact intelligence. The REST API base URL is https://api.cotality.com/trestle/odata and All WebAPI requests require a Bearer token obtained via OAuth2 client_credentials; Trestle IQ endpoints require an API key..

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 pip install "dlt[workspace]" and start loading Trestle API data in under 10 minutes.

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What data can I load from Trestle API?

Here are some of the endpoints you can load from Trestle API:

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How do I authenticate with the Trestle API API?

WebAPI uses OAuth2 Client Credentials to obtain a Bearer token (include "Authorization: Bearer <access_token>"); Trestle IQ uses an API‑key header.

1. Get your credentials

1. Request API access from CoreLogic/Trestle to obtain a client_id and client_secret for the WebAPI, or an API key for Trestle IQ.
2. For WebAPI, POST a form‑encoded request to https://api.cotality.com/trestle/oidc/connect/token with client_id, client_secret, grant_type=client_credentials, and scope=api.
3. The response returns { "access_token": "...", "expires_in": ... }. Use this token in the Authorization: Bearer <access_token> header for subsequent OData calls.
4. For Trestle IQ, add the provided API key to the request headers as specified (e.g., api_key: <your_key>).

2. Add them to .dlt/secrets.toml

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[sources.trestle_api_source]
client_id = "your_client_id"
client_secret = "your_client_secret"
# or for Trestle IQ API key
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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt 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:

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dlt ai toolkit rest-api-pipeline install

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:

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python trestle_api_pipeline.py

If everything is configured correctly, you'll see output like this:

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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:

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dlt pipeline trestle_api_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads property and media from the Trestle API API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def trestle_api_source(client_id=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.cotality.com/trestle/odata",
            "auth": {
                "type": "bearer",
                "token": client_id,
            },
        },
        "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.

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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):

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import dlt

data = dlt.pipeline("trestle_api_pipeline").dataset()
sessions_df = data.property.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM trestle_api_data.property LIMIT 10;

In a marimo or Jupyter notebook:

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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.

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What destinations can I load Trestle API data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

Authentication failures

If you receive 401 Unauthorized, verify that you have obtained a valid access token from POST https://api.cotality.com/trestle/oidc/connect/token using the client_credentials grant and that the Authorization: Bearer <access_token> header is included.

Quota and rate limits

Trestle returns quota information in HTTP response headers such as Minute-Quota-Limit and Hour-Quota-Limit. A 429 Too Many Requests response indicates the quota has been exceeded; implement exponential back‑off and respect per‑feed limits.

Pagination and replication

OData responses include a top‑level value array and may contain an @odata.nextLink URL for additional pages. Use $top (max 1000) and $skip or follow @odata.nextLink. For large data extracts, use the Replication endpoint which returns bulk records in OData format.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack