Guía de integración de frameworks de agentes

Este documento te muestra cómo integrar agentes creados con diversos frameworks de IA en Novita Agent Runtime. Proporcionamos ejemplos de integración para frameworks populares de agentes de IA:

LangGraph - Crea flujos de trabajo de agentes complejos de varios pasos
OpenAI Agents SDK - El framework oficial de agentes de OpenAI
Microsoft AutoGen - El framework de conversación multiagente de Microsoft
Google ADK - Google Agent Development Kit

Patrón principal de integración

Todas las integraciones de frameworks siguen este patrón principal:

from novita_sandbox.agent_runtime import AgentRuntimeApp

# 1. Create Agent Runtime application instance
app = AgentRuntimeApp()

# 2. Initialize your Agent framework

# 3. Define entry point with decorator
@app.entrypoint
def agent_invocation(request: dict) -> dict:
    """
    Args:
        request: Request data, typically contains fields like prompt
    Returns:
        Response data dictionary
    """
    prompt = request.get("prompt", "")
    
    # Call your Agent framework
    result = your_agent.run(prompt)
    
    return {"result": result}

# 4. Run the application
if __name__ == "__main__":
    app.run()

LangGraph

LangGraph es el framework oficial de LangChain para crear aplicaciones de agentes con estado y de varios pasos.

Código de ejemplo

Para ver el proyecto de ejemplo completo, consulta aquí

from langchain_community.chat_models import ChatOpenAI
from typing_extensions import TypedDict
from typing import Annotated
from langgraph.graph import StateGraph, START
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

# Import Novita Agent Runtime
from novita_sandbox.agent_runtime import AgentRuntimeApp

app = AgentRuntimeApp()

# Define state
class State(TypedDict):
    messages: Annotated[list, add_messages]

# Initialize LLM
llm = ChatOpenAI(model="gpt-4")

# Define tools
def get_weather(location: str) -> str:
    """Get weather information for a specified location"""
    return f"The weather in {location} is sunny, 23°C"

tools = [get_weather]
llm_with_tools = llm.bind_tools(tools)

# Define node function
def chatbot(state: State):
    return {"messages": [llm_with_tools.invoke(state["messages"])]}

# Build graph
graph_builder = StateGraph(State)
graph_builder.add_node("chatbot", chatbot)
graph_builder.add_node("tools", ToolNode(tools=tools))
graph_builder.add_conditional_edges(
    "chatbot",
    tools_condition,
)
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge(START, "chatbot")

graph = graph_builder.compile()

# Define entry point
@app.entrypoint
def agent_invocation(request: dict) -> dict:
    """LangGraph Agent entry point"""
    prompt = request.get("prompt", "Hello!")
    
    # Invoke LangGraph
    result = graph.invoke({
        "messages": [{"role": "user", "content": prompt}]
    })
    
    # Extract the last message
    final_message = result['messages'][-1].content
    
    return {"result": final_message}

if __name__ == "__main__":
    app.run()

OpenAI Agents SDK

OpenAI Agents SDK es el kit de herramientas oficial de OpenAI para crear agentes de IA.

Código de ejemplo

Para ver el proyecto de ejemplo completo, consulta aquí

import os
from novita_sandbox.agent_runtime import AgentRuntimeApp
from openai import AsyncOpenAI

# Create application
app = AgentRuntimeApp()

# Define tool function
def get_weather(city: str) -> str:
    """Get city weather (simulated)"""
    weather_data = {
        "Beijing": "Sunny, 15°C",
        "Shanghai": "Cloudy, 20°C",
        "Shenzhen": "Light rain, 25°C",
    }
    return weather_data.get(city, f"{city}: Sunny, 23°C")

# Tool definition
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather information for a specified city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "City name, e.g. 'Beijing', 'Shanghai'"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

# Agent core logic
async def run_agent(query: str) -> str:
    """Run OpenAI Agent (supports function calling)"""
    client = AsyncOpenAI(
        base_url=os.getenv("OPENAI_API_BASE"),
        api_key=os.getenv("NOVITA_API_KEY"),
    )
    
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant that can query weather."},
        {"role": "user", "content": query}
    ]
    
    # First call
    response = await client.chat.completions.create(
        model=os.getenv("MODEL_NAME", "deepseek/deepseek-v3.1-terminus"),
        messages=messages,
        tools=TOOLS,
        tool_choice="auto"
    )
    
    response_message = response.choices[0].message
    
    # If tool call is needed
    if response_message.tool_calls:
        messages.append(response_message)
        
        for tool_call in response_message.tool_calls:
            function_args = eval(tool_call.function.arguments)
            function_response = get_weather(**function_args)
            
            messages.append({
                "tool_call_id": tool_call.id,
                "role": "tool",
                "name": "get_weather",
                "content": function_response
            })
        
        # Second call to get final response
        final_response = await client.chat.completions.create(
            model=os.getenv("MODEL_NAME", "deepseek/deepseek-v3.1-terminus"),
            messages=messages
        )
        return final_response.choices[0].message.content
    
    return response_message.content

# Novita Agent Runtime entry point
@app.entrypoint
async def agent_invocation(request: dict) -> dict:
    """Entry point function"""
    prompt = request.get("prompt", "Hello!")
    result = await run_agent(prompt)
    return {"result": result}

# Start application
if __name__ == "__main__":
    app.run()

Microsoft AutoGen

AutoGen es un framework de conversación multiagente desarrollado por Microsoft que admite colaboración y conversaciones entre múltiples agentes, capaz de funcionar de forma independiente o con humanos.

Código de ejemplo

Para ver el proyecto de ejemplo completo, consulta aquí

import os
from novita_sandbox.agent_runtime import AgentRuntimeApp
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.messages import TextMessage
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_core.models import ModelFamily, ModelInfo

# Create application
app = AgentRuntimeApp()

# Define tool function
async def get_weather(city: str) -> str:
    """Get weather for a specified city"""
    weather_data = {
        "Beijing": "Sunny, 15°C",
        "Shanghai": "Cloudy, 20°C",
        "Shenzhen": "Light rain, 25°C",
    }
    return weather_data.get(city, f"{city}: Sunny, 23°C")

# Create AutoGen Agent
def create_agent():
    """Create AutoGen Agent"""
    model_client = OpenAIChatCompletionClient(
        base_url=os.getenv("OPENAI_BASE_URL", "https://api.novita.ai/v3/openai"),
        model=os.getenv("MODEL_NAME", "deepseek/deepseek-v3.1-terminus"),
        api_key=os.getenv("OPENAI_API_KEY"),
        model_info=ModelInfo(
            vision=False,
            function_calling=True,
            json_output=True,
            family=ModelFamily.UNKNOWN,
        ),
    )
    
    agent = AssistantAgent(
        name="assistant",
        model_client=model_client,
        tools=[get_weather],
        system_message="You are a helpful AI assistant that can query weather information.",
        reflect_on_tool_use=True,
    )
    
    return agent

# Run Agent
async def run_agent(prompt: str) -> str:
    """Run AutoGen Agent"""
    agent = create_agent()
    
    # Create user message
    message = TextMessage(content=prompt, source="user")
    
    # Run Agent
    response_message = await agent.on_messages([message])
    
    # Extract response content
    if response_message and hasattr(response_message, 'chat_message'):
        return response_message.chat_message.content
    
    return str(response_message)

# Novita Agent Runtime entry point
@app.entrypoint
async def agent_invocation(request: dict):
    """Entry point function"""
    prompt = request.get("prompt", "Hello!")
    result = await run_agent(prompt)
    return {"result": result}

# Start application
if __name__ == "__main__":
    app.run()

Google ADK

Google Agent Development Kit (ADK) es el kit de herramientas de Google para el desarrollo de agentes.

Ejemplo completo

Para ver el proyecto de ejemplo completo, consulta aquí

import os
import uuid
import asyncio
from google.adk.agents import LlmAgent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools import google_search
from google.genai import types
from novita_sandbox.agent_runtime import AgentRuntimeApp

# Create application
app = AgentRuntimeApp()

APP_NAME = "google_search_agent"

# Create Google ADK Agent
root_agent = LlmAgent(
    model=os.getenv("GEMINI_MODEL", "gemini-2.0-flash"), 
    name=APP_NAME,
    instruction="I can answer your questions by searching the internet. Ask me anything!",
    tools=[google_search]
)

# Create Session service and Runner
session_service = InMemorySessionService()
runner = Runner(
    agent=root_agent, 
    app_name=APP_NAME, 
    session_service=session_service
)

# Run Agent
async def run_agent(query: str) -> str:
    """Run Google ADK Agent"""
    user_id = "user_default"
    session_id = str(uuid.uuid4())
    
    # Create Session
    await session_service.create_session(
        app_name=APP_NAME, 
        user_id=user_id, 
        session_id=session_id
    )
    
    # Create user message
    user_content = types.Content(
        role='user', 
        parts=[types.Part(text=query)]
    )
    
    # Run Agent
    result = ""
    async for event in runner.run_async(
        user_id=user_id, 
        session_id=session_id, 
        new_message=user_content
    ):
        if event.is_final_response() and event.content and event.content.parts:
            result = event.content.parts[0].text
    
    return result

# Novita Agent Runtime entry point
@app.entrypoint
def agent_invocation(request: dict):
    """Entry point function"""
    prompt = request.get("prompt", "Hello!")
    result = asyncio.run(run_agent(prompt))
    return {"result": result}

# Start application
if __name__ == "__main__":
    app.run()

Get started

Model APIs

Agent Sandbox

GPUs

Observability

Resources

Guía de integración de frameworks de agentes

Patrón principal de integración

LangGraph

Código de ejemplo

OpenAI Agents SDK

Código de ejemplo

Microsoft AutoGen

Código de ejemplo

Google ADK

Ejemplo completo

​Patrón principal de integración

​LangGraph

​Código de ejemplo

​OpenAI Agents SDK

​Código de ejemplo

​Microsoft AutoGen

​Código de ejemplo

​Google ADK

​Ejemplo completo

Patrón principal de integración

LangGraph

Código de ejemplo

OpenAI Agents SDK

Código de ejemplo

Microsoft AutoGen

Código de ejemplo

Google ADK

Ejemplo completo