Enrich-crm Python API Docs | dltHub

Apollo Enrich CRM is a sales intelligence enrichment API that returns detailed people and organization data to augment CRM records. The REST API base URL is https://api.apollo.io/api/v1 and API keys (x-api-key) for customers; OAuth/Bearer for partner integrations..

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 Enrich-crm data in under 10 minutes.

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What data can I load from Enrich-crm?

Here are some of the endpoints you can load from Enrich-crm:

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How do I authenticate with the Enrich-crm API?

Customer API keys are sent in requests via the X-Api-Key (case-insensitive) header. Partners building OAuth integrations may use Bearer tokens via standard OAuth2 flows for requests that require OAuth.

1. Get your credentials

1. Log in to Apollo and go to Settings > Integrations.
2. Click Connect beside Apollo API.
3. Open API Keys and click Create new key.
4. Enter a name and description, then select the endpoints the key should access (or toggle Set as master key).
5. Click Create API key, copy the displayed key and store it securely.
6. Use that key in the X-Api-Key header for requests.

2. Add them to .dlt/secrets.toml

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[sources.enrich_crm_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 Enrich-crm 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 enrich_crm_pipeline.py

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

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Pipeline enrich_crm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset enrich_crm_data
The duckdb destination used duckdb:/enrich_crm.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 enrich_crm_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/match and organizations/enrich from the Enrich-crm 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 enrich_crm_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.apollo.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "people_match", "endpoint": {"path": "people/match"}},
            {"name": "organizations_enrich", "endpoint": {"path": "organizations/enrich"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="enrich_crm_pipeline",
        destination="duckdb",
        dataset_name="enrich_crm_data",
    )
    load_info = pipeline.run(enrich_crm_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("enrich_crm_pipeline").dataset()
sessions_df = data.people_match.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM enrich_crm_data.people_match LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("enrich_crm_pipeline").dataset()
data.people_match.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 Enrich-crm 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 or 403 responses, verify that you are sending a valid API key in the X-Api-Key header. Some endpoints (e.g., usage stats) require a master key; using a non‑master key will result in a 403.

Rate limits and credits

Each endpoint enforces minute/hour/day rate limits and consumes credits per enrichment call. The developer dashboard Usage page shows per‑endpoint limits and total credits consumed. Bulk enrichment endpoints have a reduced per‑minute limit (50% of the single‑enrich limit) but share the same hourly/daily caps.

Pagination and bulk behaviour

Bulk enrichment endpoints accept up to 10 items per request and return results in a top‑level matches array. Single‑enrich endpoints return a single object (no array). Search or list endpoints (not covered here) provide pagination parameters; enrichment endpoints do not paginate beyond the fixed batch size.

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