> For the complete documentation index, see [llms.txt](https://docs.quadratichq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.quadratichq.com/connections/agent-connections.md).

# Agent connections

## Agent connections

Agent connections let you pull data from third-party services — CRMs, payment processors, analytics tools, internal APIs, and more — directly into your spreadsheets. Unlike database connections, agent connections work with any service that exposes a REST API.

### Create an agent connection

1. In a sheet, press `/` in a cell and choose **Connections**, or go to **Dashboard > Connections** under the Team menu.
2. Pick a service from the catalog, or choose **Connect to anything** to set up a custom API.
3. The AI walks you through setup conversationally: it researches the service's API, proposes a connection plan, collects credentials, tests the connection, and finalizes it.

For services that use OAuth, you'll be redirected to sign in to the service and grant access. For API-key services, the AI asks for your key and stores it securely as a team secret.

{% hint style="info" %}
AI messages during agent connection setup are free — they don't count against your team's AI usage.
{% endhint %}

### Use an agent connection

Once created, agent connections can be used in two ways:

#### From AI chat

Mention the connection with `@` in the AI chat. The AI can query the connected service and combine its data with your sheet data.

#### From code cells

Agent connections are available from Python and JavaScript code cells. Requests run through Quadratic's authenticated proxy, which injects your stored credentials.

Reference team secrets with `{{SECRET_NAME}}` placeholders in URLs, headers, or request bodies — the actual values never appear in your code or your sheet:

```python
import requests
import pandas as pd

response = requests.get(
    'https://api.example.com/v1/customers',
    headers={'Authorization': 'Bearer {{EXAMPLE_API_KEY}}'}
)
df = pd.DataFrame(response.json()['data'])
df
```

### Team secrets

Secrets are managed in your team settings under **Secrets**. Each secret has a name and a value; the value is write-only after creation. Secrets are scoped to your team and only substituted for requests to the domains allowed by the connection.

### Permissions

Agent connections follow the same permission model as database connections: team editors can use connections in sheets; owners can create, edit, and remove them.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.quadratichq.com/connections/agent-connections.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.