Intercom Python API Docs | dltHub

Intercom is a customer messaging platform that provides REST APIs to access and manage conversations, contacts (users, leads), companies, tickets and other workspace data. The REST API base URL is https://api.intercom.io/ (also regional: https://api.eu.intercom.io/ and https://api.au.intercom.io/) and All requests require a Bearer access token (Access Token for private apps or OAuth token for public apps)..

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 Intercom data in under 10 minutes.

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What data can I load from Intercom?

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

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How do I authenticate with the Intercom API?

Intercom uses Bearer token authorization. Include Authorization: Bearer <access_token> and Accept: application/json in request headers. Optionally set Intercom-Version or use versioned endpoints.

1. Get your credentials

1. In your Intercom workspace go to Settings > Developer Hub (Apps) > Create a private app (Access Token) or configure OAuth for a public app.
2. For private apps generate an Access Token; for public apps follow OAuth to obtain an access token.
3. Copy the token and use it in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.intercom_source]
access_token = "your_intercom_access_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 Intercom 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 intercom_pipeline.py

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

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Pipeline intercom_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset intercom_data
The duckdb destination used duckdb:/intercom.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 intercom_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 users and conversations from the Intercom 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 intercom_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.intercom.io/ (also regional: https://api.eu.intercom.io/ and https://api.au.intercom.io/)",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            {"name": "users", "endpoint": {"path": "users", "data_selector": "users"}},
            {"name": "conversations", "endpoint": {"path": "conversations", "data_selector": "conversations"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="intercom_pipeline",
        destination="duckdb",
        dataset_name="intercom_data",
    )
    load_info = pipeline.run(intercom_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("intercom_pipeline").dataset()
sessions_df = data.conversations.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM intercom_data.conversations LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("intercom_pipeline").dataset()
data.conversations.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 Intercom 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 Authorization: Bearer <token> header and that the token has the required scopes. Ensure you're using the correct regional base URL.

Rate limiting

Intercom returns 429 Too Many Requests when rate limits are exceeded. Respect the Retry-After header and implement exponential backoff.

Pagination

Many list endpoints return paginated responses. Use the returned pagination links (page‑based or cursor‑based starting_after/ending_before) and request parameters per docs to iterate results.

Validation and other errors

400/422 responses indicate malformed requests or validation errors; inspect the response body for details. 500 indicates server errors—retry with backoff.

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