deepset-ai--haystack/docs-website/versioned_docs/version-2.31/pipeline-components/connectors/openapiconnector.mdx

---
title: "OpenAPIConnector"
id: openapiconnector
slug: "/openapiconnector"
description: "`OpenAPIConnector` is a component that acts as an interface between the Haystack ecosystem and OpenAPI services."
---

# OpenAPIConnector

`OpenAPIConnector` is a component that acts as an interface between the Haystack ecosystem and OpenAPI services.

:::tip[Consider using MCP instead]

These OpenAPI components are a legacy way to connect Haystack to external APIs. For most use cases, we recommend the [`MCPTool`](../../tools/mcptool.mdx) instead: it is the modern, standardized way to give your pipelines and agents access to external tools and services.

:::

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Anywhere, after components providing input for its run parameters                                  |
| **Mandatory init variables**           | `openapi_spec`: The OpenAPI specification for the service. Can be a URL, file path, or raw string. |
| **Mandatory run variables**            | `operation_id`: The operationId from the OpenAPI spec to invoke.                                   |
| **Output variables**                   | `response`: A REST service response                                                                |
| **API reference**                      | [OpenAPI](/reference/integrations-openapi)                                                                   |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/openapi       |
| **Package name** | `openapi-haystack` |

</div>

## Overview

The `OpenAPIConnector` is a component within the Haystack ecosystem that allows direct invocation of REST endpoints defined in an OpenAPI (formerly Swagger) specification. It acts as a bridge between Haystack pipelines and any REST API that follows the OpenAPI standard, enabling dynamic method calls, authentication, and parameter handling.

To use the `OpenAPIConnector`, ensure that you have the `openapi-haystack` package installed:

```shell
pip install openapi-haystack
```

Unlike [OpenAPIServiceConnector](openapiserviceconnector.mdx), which works with LLMs, `OpenAPIConnector` directly calls REST endpoints using explicit input arguments.

## Usage

### On its own

You can initialize and use the `OpenAPIConnector` on its own by passing an OpenAPI specification and other parameters:

```python
from haystack.utils import Secret
from haystack_integrations.components.connectors.openapi import OpenAPIConnector

connector = OpenAPIConnector(
    openapi_spec="https://bit.ly/serperdev_openapi",
    credentials=Secret.from_env_var("SERPERDEV_API_KEY"),
    service_kwargs={"config_factory": my_custom_config_factory},
)

response = connector.run(
    operation_id="search",
    arguments={"q": "Who was Nikola Tesla?"},
)
```

#### Output

The `OpenAPIConnector` returns a dictionary containing the service response:

```json
{
    "response": { // here goes REST endpoint response JSON
    }
}
```

### In a pipeline

The `OpenAPIConnector` can be integrated into a Haystack pipeline to interact with OpenAPI services. For example, here’s how you can link the `OpenAPIConnector` to a pipeline:

```python
from haystack import Pipeline
from haystack_integrations.components.connectors.openapi import OpenAPIConnector
from haystack.dataclasses.chat_message import ChatMessage
from haystack.utils import Secret

# Initialize the OpenAPIConnector
connector = OpenAPIConnector(
    openapi_spec="https://bit.ly/serperdev_openapi",
    credentials=Secret.from_env_var("SERPERDEV_API_KEY"),
)

# Create a ChatMessage from the user
user_message = ChatMessage.from_user(text="Who was Nikola Tesla?")

# Define the pipeline
pipeline = Pipeline()
pipeline.add_component("openapi_connector", connector)

# Run the pipeline
response = pipeline.run(
    data={
        "openapi_connector": {
            "operation_id": "search",
            "arguments": {"q": user_message.text},
        },
    },
)

# Extract the answer from the response
answer = response.get("openapi_connector", {}).get("response", {})
print(answer)
```