Outreach Python API Docs | dltHub

Outreach is a sales engagement platform providing a RESTful JSON:API for accessing CRM-like resources (prospects, sequences, activities, tasks, users, opportunities, etc.). The REST API base URL is https://api.outreach.io/api/v2 and All requests require OAuth2 Bearer tokens (also supports S2S tokens for server‑to‑server limited access)..

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 Outreach data in under 10 minutes.

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What data can I load from Outreach?

Here are some of the endpoints you can load from Outreach:

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How do I authenticate with the Outreach API?

Outreach supports standard OAuth2 authorization_code and refresh_token flows; include "Authorization: Bearer " and Content-Type: application/vnd.api+json. For server‑to‑server (S2S) usage, create a JWT app token, exchange it for an install access token, then use that S2S token in the Authorization header.

1. Get your credentials

1. Create an OAuth app in the Outreach Developer portal to obtain client_id and client_secret. 2) Direct users to https://api.outreach.io/oauth/authorize?client_id=&redirect_uri=&response_type=code&scope= to obtain an authorization code. 3) Exchange the code at POST https://api.outreach.io/oauth/token with client_id, client_secret, redirect_uri, grant_type=authorization_code and code to receive access_token and refresh_token. 4) To refresh, POST the same endpoint with grant_type=refresh_token and refresh_token. For S2S: enable API Access (S2S) in the portal, upload PEM public key(s), note the S2S_GUID, create a JWT app token (iss=S2S_GUID, RS256 signed), then call /api/app/installs/INSTALL_ID/actions/accessToken to receive the S2S token (data.meta.accessToken).

2. Add them to .dlt/secrets.toml

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[sources.outreach_source]
access_token = "your_oauth_or_s2s_token_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 Outreach 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 outreach_pipeline.py

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

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Pipeline outreach_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset outreach_data
The duckdb destination used duckdb:/outreach.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 outreach_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 prospects and sequences from the Outreach 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 outreach_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.outreach.io/api/v2",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "prospects", "endpoint": {"path": "prospects", "data_selector": "data"}},
            {"name": "sequences", "endpoint": {"path": "sequences", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="outreach_pipeline",
        destination="duckdb",
        dataset_name="outreach_data",
    )
    load_info = pipeline.run(outreach_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("outreach_pipeline").dataset()
sessions_df = data.prospects.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM outreach_data.prospects LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("outreach_pipeline").dataset()
data.prospects.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 Outreach 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 your Authorization header is missing, expired, or lacks required OAuth scopes, you will receive 401 or 403 responses. Example JSON error for missing scope:

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{ "errors": [ { "id": "unauthorizedOauthScope", "title": "Unauthorized OAuth Scope", "detail": "Your authorization does not include the required scope 'prospects.read'." } ] }

Rate limiting

Outreach enforces per‑user rate limits (generally 10,000 requests/hour). Responses include X-RateLimit-Limit, X-RateLimit-Remaining and X-RateLimit-Reset (Retry‑After). When exceeded, API returns 429 with JSON error:

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{ "errors": [ { "id": "rateLimitExceeded", "title": "Rate Limit Exceeded", "detail": "You have exceeded your permitted rate limit of 10,000; please try again at ..." } ] }

Pagination

Collections follow JSON:API pagination. Use page[number] and page[size] (or other supported page params) to iterate pages. Responses contain top‑level data (array) and links for next/prev when available.

Maintenance and service errors

During maintenance the API returns 503 with a JSON error and a Retry-After header. Example:

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{ "errors": [ { "id": "scheduledServerMaintenance", "title": "Scheduled Server Maintenance", "detail": "Scheduled server maintenance is under way; please try again at 2017-01-01T00:00:00" } ] }

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