Apollo Python API Docs | dltHub

Apollo is a prospecting and go-to-market platform that provides people and company data, enrichment, search, and CRM automation via a REST API. The REST API base URL is https://api.apollo.io/api/v1 and All requests require a Bearer token (API key) in the Authorization header; partners use OAuth2.0 flows..

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 Apollo data in under 10 minutes.

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What data can I load from Apollo?

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

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How do I authenticate with the Apollo API?

Apollo uses API keys for customer access: include your API key as a Bearer token in the Authorization header. Partners may use OAuth 2.0 when acting on behalf of users.

1. Get your credentials

1. Sign in to your Apollo account at app.apollo.io.
2. Go to Settings > API Keys (or follow 'Create API Keys' in Developer Hub).
3. Create a new API key (scoped as needed).
4. Copy the generated key and store it as your api_key.
5. Use it in requests via header: Authorization: Bearer <api_key>.

2. Add them to .dlt/secrets.toml

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[sources.apollo_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.

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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 Apollo 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 apollo_pipeline.py

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

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Pipeline apollo_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset apollo_data
The duckdb destination used duckdb:/apollo.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 apollo_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 people_search and account_stages from the Apollo 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 apollo_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.apollo.io/api/v1",
            "auth": {
                "type": "bearer",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "people_search", "endpoint": {"path": "mixed_people/search", "data_selector": "contacts"}},
            {"name": "account_stages", "endpoint": {"path": "account_stages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="apollo_pipeline",
        destination="duckdb",
        dataset_name="apollo_data",
    )
    load_info = pipeline.run(apollo_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("apollo_pipeline").dataset()
sessions_df = data.people_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM apollo_data.people_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("apollo_pipeline").dataset()
data.people_search.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 Apollo 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, verify your Authorization header uses 'Bearer <api_key>' and the key is valid/unexpired. Master-only endpoints return 403 if a non-master key is used.

Rate limits

Apollo enforces rate limits and display limits on search endpoints (e.g., People Search display limit of 50,000 records; pagination limited to per_page/page and up to 500 pages). If you receive 429, back off and retry with exponential backoff.

Pagination quirks

Search responses include a 'pagination' object with page, per_page, total_entries and total_pages; use these values to iterate. The People Search returns results in a 'contacts' array inside the response object, not as top-level array.

Common error responses

401 Unauthorized: invalid/missing token. 403 Forbidden: insufficient privileges (master key required for some endpoints). 429 Too Many Requests: rate limited, retry later. Other errors include standard 4xx/5xx with 'message' field in response.

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