| [ Web Proxy ] |
| Viewing: https://adk.dev/integrations/../../../agents/../../a2a/../../../tools-custom/openapi-tools/ | [Back] [Original] |
[logo]
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.
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.
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.The process involves these main steps when you use OpenAPIToolset:
Initialization & Parsing:
OpenAPIToolset either as a Python dictionary, a JSON string, or a YAML string.$ref) to understand the complete API structure.Operation Discovery:
GET, POST, PUT, DELETE) defined within the paths object of your specification.Tool Generation:
OpenAPIToolset automatically creates a corresponding RestApiTool instance.operationId in the spec (converted to snake_case, max 60 chars). If operationId is missing, a name is generated from the method and path.summary or description from the operation for the LLM.RestApiTool Functionality: Each generated RestApiTool:
FunctionDeclaration based on
the operation's parameters and request body. This schema tells the LLM how
to call the tool (what arguments are expected).httpx library.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.
Follow these steps to integrate an OpenAPI spec into your agent:
.json or .yaml file, fetch from a URL).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)
Add to Agent: Include the retrieved tools in your LlmAgent's tools list.
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.
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.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.
# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import uuid # For unique session IDs
from dotenv import load_dotenv
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
# --- OpenAPI Tool Imports ---
from google.adk.tools.openapi_tool.openapi_spec_parser.openapi_toolset import OpenAPIToolset
# --- Load Environment Variables (If ADK tools need them, e.g., API keys) ---
load_dotenv() # Create a .env file in the same directory if needed
# --- Constants ---
APP_NAME_OPENAPI = "openapi_petstore_app"
USER_ID_OPENAPI = "user_openapi_1"
SESSION_ID_OPENAPI = f"session_openapi_{uuid.uuid4()}" # Unique session ID
AGENT_NAME_OPENAPI = "petstore_manager_agent"
GEMINI_MODEL = "gemini-2.0-flash"
# --- Sample OpenAPI Specification (JSON String) ---
# A basic Pet Store API example using httpbin.org as a mock server
openapi_spec_string = """
{
"openapi": "3.0.0",
"info": {
"title": "Simple Pet Store API (Mock)",
"version": "1.0.1",
"description": "An API to manage pets in a store, using httpbin for responses."
},
"servers": [
{
"url": "https://httpbin.org",
"description": "Mock server (httpbin.org)"
}
],
"paths": {
"/get": {
"get": {
"summary": "List all pets (Simulated)",
"operationId": "listPets",
"description": "Simulates returning a list of pets. Uses httpbin's /get endpoint which echoes query parameters.",
"parameters": [
{
"name": "limit",
"in": "query",
"description": "Maximum number of pets to return",
"required": false,
"schema": { "type": "integer", "format": "int32" }
},
{
"name": "status",
"in": "query",
"description": "Filter pets by status",
"required": false,
"schema": { "type": "string", "enum": ["available", "pending", "sold"] }
}
],
"responses": {
"200": {
"description": "A list of pets (echoed query params).",
"content": { "application/json": { "schema": { "type": "object" } } }
}
}
}
},
"/post": {
"post": {
"summary": "Create a pet (Simulated)",
"operationId": "createPet",
"description": "Simulates adding a new pet. Uses httpbin's /post endpoint which echoes the request body.",
"requestBody": {
"description": "Pet object to add",
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["name"],
"properties": {
"name": {"type": "string", "description": "Name of the pet"},
"tag": {"type": "string", "description": "Optional tag for the pet"}
}
}
}
}
},
"responses": {
"201": {
"description": "Pet created successfully (echoed request body).",
"content": { "application/json": { "schema": { "type": "object" } } }
}
}
}
},
"/get?petId={petId}": {
"get": {
"summary": "Info for a specific pet (Simulated)",
"operationId": "showPetById",
"description": "Simulates returning info for a pet ID. Uses httpbin's /get endpoint.",
"parameters": [
{
"name": "petId",
"in": "path",
"description": "This is actually passed as a query param to httpbin /get",
"required": true,
"schema": { "type": "integer", "format": "int64" }
}
],
"responses": {
"200": {
"description": "Information about the pet (echoed query params)",
"content": { "application/json": { "schema": { "type": "object" } } }
},
"404": { "description": "Pet not found (simulated)" }
}
}
}
}
}
"""
# --- Create OpenAPIToolset ---
petstore_toolset = OpenAPIToolset(
spec_str=openapi_spec_string,
spec_str_type='json',
# No authentication needed for httpbin.org
)
# --- Agent Definition ---
root_agent = LlmAgent(
name=AGENT_NAME_OPENAPI,
model=GEMINI_MODEL,
tools=[petstore_toolset], # Pass the list of RestApiTool objects
instruction="""You are a Pet Store assistant managing pets via an API.
Use the available tools to fulfill user requests.
When creating a pet, confirm the details echoed back by the API.
When listing pets, mention any filters used (like limit or status).
When showing a pet by ID, state the ID you requested.
""",
description="Manages a Pet Store using tools generated from an OpenAPI spec."
)
# --- Session and Runner Setup ---
async def setup_session_and_runner():
session_service_openapi = InMemorySessionService()
runner_openapi = Runner(
agent=root_agent,
app_name=APP_NAME_OPENAPI,
session_service=session_service_openapi,
)
await session_service_openapi.create_session(
app_name=APP_NAME_OPENAPI,
user_id=USER_ID_OPENAPI,
session_id=SESSION_ID_OPENAPI,
)
return runner_openapi
# --- Agent Interaction Function ---
async def call_openapi_agent_async(query, runner_openapi):
print("\n--- Running OpenAPI Pet Store Agent ---")
print(f"Query: {query}")
content = types.Content(role='user', parts=[types.Part(text=query)])
final_response_text = "Agent did not provide a final text response."
try:
async for event in runner_openapi.run_async(
user_id=USER_ID_OPENAPI, session_id=SESSION_ID_OPENAPI, new_message=content
):
# Optional: Detailed event logging for debugging
# print(f" DEBUG Event: Author={event.author}, Type={'Final' if event.is_final_response() else 'Intermediate'}, Content={str(event.content)[:100]}...")
if event.get_function_calls():
call = event.get_function_calls()[0]
print(f" Agent Action: Called function '{call.name}' with args {call.args}")
elif event.get_function_responses():
response = event.get_function_responses()[0]
print(f" Agent Action: Received response for '{response.name}'")
# print(f" Tool Response Snippet: {str(response.response)[:200]}...") # Uncomment for response details
elif event.is_final_response() and event.content and event.content.parts:
# Capture the last final text response
final_response_text = event.content.parts[0].text.strip()
print(f"Agent Final Response: {final_response_text}")
except Exception as e:
print(f"An error occurred during agent run: {e}")
import traceback
traceback.print_exc() # Print full traceback for errors
print("-" * 30)
# --- Run Examples ---
async def run_openapi_example():
runner_openapi = await setup_session_and_runner()
# Trigger listPets
await call_openapi_agent_async("Show me the pets available.", runner_openapi)
# Trigger createPet
await call_openapi_agent_async("Please add a new dog named 'Dukey'.", runner_openapi)
# Trigger showPetById
await call_openapi_agent_async("Get info for pet with ID 123.", runner_openapi)
# --- Execute ---
if __name__ == "__main__":
print("Executing OpenAPI example...")
# Use asyncio.run() for top-level execution
try:
asyncio.run(run_openapi_example())
except RuntimeError as e:
if "cannot be called from a running event loop" in str(e):
print("Info: Cannot run asyncio.run from a running event loop (e.g., Jupyter/Colab).")
# If in Jupyter/Colab, you might need to run like this:
# await run_openapi_example()
else:
raise e
print("OpenAPI example finished.")
| Web Proxy Viewer | New URL | Original Page |