AgileCRM Python API Docs | dltHub

AgileCRM is a cloud-based CRM platform that provides REST APIs to manage contacts, companies, deals, tasks, events, campaigns, documents, tickets and related CRM objects. The REST API base URL is https://{domain}.agilecrm.com/dev/ and Basic HTTP auth using account email and REST 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 AgileCRM data in under 10 minutes.

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What data can I load from AgileCRM?

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

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How do I authenticate with the AgileCRM API?

AgileCRM uses HTTP Basic Authentication where the username is the account email and the password is the REST API key. Send requests over HTTPS and include Accept: application/json to receive JSON responses (XML by default).

1. Get your credentials

1. Log into your AgileCRM account as an admin. 2) Go to Admin Settings -> Developers & API (or API & Analytics -> API Key). 3) Copy the REST API key labeled for REST clients. 4) Note your AgileCRM subdomain (the {domain} part of your portal URL) and the admin email to use as the username.

2. Add them to .dlt/secrets.toml

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[sources.agile_crm_source]
email = "user@example.com"
api_key = "your_rest_api_key"
subdomain = "your_subdomain"

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 AgileCRM 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 agile_crm_pipeline.py

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

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Pipeline agile_crm_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset agile_crm_data
The duckdb destination used duckdb:/agile_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 agile_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 contacts and opportunity from the AgileCRM 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 agile_crm_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{domain}.agilecrm.com/dev/",
            "auth": {
                "type": "http_basic",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "contacts", "endpoint": {"path": "dev/api/contacts"}},
            {"name": "deals", "endpoint": {"path": "dev/api/opportunity"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="agile_crm_pipeline",
        destination="duckdb",
        dataset_name="agile_crm_data",
    )
    load_info = pipeline.run(agile_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("agile_crm_pipeline").dataset()
sessions_df = data.contacts.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM agile_crm_data.contacts LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("agile_crm_pipeline").dataset()
data.contacts.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 AgileCRM 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 you are using the account email as username and the REST API key as password in HTTP Basic Auth. Ensure you call the correct subdomain in the base URL (https://{subdomain}.agilecrm.com/dev/). Include Accept: application/json to get JSON responses.

Pagination quirks

Listing endpoints (contacts, deals, campaigns, etc.) use cursor-based pagination: responses are top-level arrays where the first item may include a total/count and the last item may include a cursor field. Use page_size and cursor (query or form param depending on endpoint) to iterate. If no cursor is present in the last item, it means that it is the end of list.

Rate limits and errors

API responses documented include status 200 for success, 204 for no-content (empty lists), 400 for bad requests (invalid input), 401 for unauthorized (bad credentials), 406 for limits exceeded (e.g., contact limit), and other 4xx for input issues. No public numeric rate-limit header documented in official docs; implement exponential backoff and treat 429/503 as transient.

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