google__adk-docs/docs/tools-custom/openapi-tools.md

Integrate REST APIs with OpenAPI

Supported in ADKPython v0.1.0

ADK simplifies interacting with external REST APIs by automatically generating callable tools directly from an OpenAPI Specification (v3.x). This eliminates the need to manually define individual function tools for each API endpoint.

!!! tip "Core Benefit" Use OpenAPIToolset to instantly create agent tools (RestApiTool) from your existing API documentation (OpenAPI spec), enabling agents to seamlessly call your web services.

Key Components

• OpenAPIToolset: This is the primary class you'll use. You initialize it with your OpenAPI specification, and it handles the parsing and generation of tools.
• RestApiTool: This class represents a single, callable API operation (like GET /pets/{petId} or POST /pets). OpenAPIToolset creates one RestApiTool instance for each operation defined in your spec.

How it Works

The process involves these main steps when you use OpenAPIToolset:

1. Initialization & Parsing:

   • You provide the OpenAPI specification to OpenAPIToolset either as a Python dictionary, a JSON string, or a YAML string.
   • The toolset internally parses the spec, resolving any internal references ($ref) to understand the complete API structure.

2. Operation Discovery:

   • It identifies all valid API operations (e.g., GET, POST, PUT, DELETE) defined within the paths object of your specification.

3. Tool Generation:

   • For each discovered operation, OpenAPIToolset automatically creates a corresponding RestApiTool instance.
   • Tool Name: Derived from the operationId in the spec (converted to snake_case, max 60 chars). If operationId is missing, a name is generated from the method and path.
   • Tool Description: Uses the summary or description from the operation for the LLM.
   • API Details: Stores the required HTTP method, path, server base URL, parameters (path, query, header, cookie), and request body schema internally.

4. RestApiTool Functionality: Each generated RestApiTool:

   • Schema Generation: Dynamically creates a FunctionDeclaration based on the operation's parameters and request body. This schema tells the LLM how to call the tool (what arguments are expected).
   • Execution: When called by the LLM, it constructs the correct HTTP request (URL, headers, query params, body) using the arguments provided by the LLM and the details from the OpenAPI spec. It handles authentication (if configured) and executes the API call using the requests library.
   • Response Handling: Returns the API response (typically JSON) back to the agent flow.

5. Authentication: You can configure global authentication (like API keys or OAuth - see Authentication for details) when initializing OpenAPIToolset. This authentication configuration is automatically applied to all generated RestApiTool instances.

Usage Workflow

Follow these steps to integrate an OpenAPI spec into your agent:

1. Obtain Spec: Get your OpenAPI specification document (e.g., load from a .json or .yaml file, fetch from a URL).

2. Instantiate Toolset: Create an OpenAPIToolset instance, passing the spec content and type (spec_str/spec_dict, spec_str_type). Provide authentication details (auth_scheme, auth_credential) if required by the API.

   from google.adk.tools.openapi_tool.openapi_spec_parser.openapi_toolset import OpenAPIToolset
   
   # Example with a JSON string
   openapi_spec_json = '...' # Your OpenAPI JSON string
   toolset = OpenAPIToolset(spec_str=openapi_spec_json, spec_str_type="json")
   
   # Example with a dictionary
   # openapi_spec_dict = {...} # Your OpenAPI spec as a dict
   # toolset = OpenAPIToolset(spec_dict=openapi_spec_dict)
   

3. Add to Agent: Include the retrieved tools in your LlmAgent's tools list.

   from google.adk.agents import LlmAgent
   
   my_agent = LlmAgent(
       name="api_interacting_agent",
       model="gemini-flash-latest", # Or your preferred model
       tools=[toolset], # Pass the toolset
       # ... other agent config ...
   )
   

4. Instruct Agent: Update your agent's instructions to inform it about the new API capabilities and the names of the tools it can use (e.g., list_pets, create_pet). The tool descriptions generated from the spec will also help the LLM.

5. Run Agent: Execute your agent using the Runner. When the LLM determines it needs to call one of the APIs, it will generate a function call targeting the appropriate RestApiTool, which will then handle the HTTP request automatically.

Example

This example demonstrates generating tools from a simple Pet Store OpenAPI spec (using httpbin.org for mock responses) and interacting with them via an agent.

???+ "Code: Pet Store API"

```python title="openapi_example.py"
--8<-- "examples/python/snippets/tools/openapi_tool.py"
```