Integrationsleitfaden für Agent-Frameworks

Dieses Dokument zeigt Ihnen, wie Sie mit verschiedenen KI-Frameworks erstellte Agenten in die Novita Agent Runtime integrieren. Wir stellen Integrationsbeispiele für beliebte KI-Agent-Frameworks bereit:

LangGraph - Erstellen komplexer mehrstufiger Agent-Workflows
OpenAI Agents SDK - Das offizielle Agent-Framework von OpenAI
Microsoft AutoGen - Microsofts Framework für Multi-Agent-Konversationen
Google ADK - Google Agent Development Kit

Zentrales Integrationsmuster

Alle Framework-Integrationen folgen diesem zentralen Muster:

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 ist das offizielle Framework von LangChain zum Erstellen zustandsbehafteter, mehrstufiger Agent-Anwendungen.

Beispielcode

Das vollständige Beispielprojekt finden Sie hier

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 ist das offizielle Toolkit von OpenAI zum Erstellen von KI-Agenten.

Beispielcode

Das vollständige Beispielprojekt finden Sie hier

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 ist ein von Microsoft entwickeltes Framework für Multi-Agent-Konversationen, das die Zusammenarbeit und Konversationen mehrerer Agenten unterstützt und unabhängig oder gemeinsam mit Menschen arbeiten kann.

Beispielcode

Das vollständige Beispielprojekt finden Sie hier

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) ist Googles Toolkit für die Agent-Entwicklung.

Vollständiges Beispiel

Das vollständige Beispielprojekt finden Sie hier

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

Integrationsleitfaden für Agent-Frameworks

Zentrales Integrationsmuster

LangGraph

Beispielcode

OpenAI Agents SDK

Beispielcode

Microsoft AutoGen

Beispielcode

Google ADK

Vollständiges Beispiel

​Zentrales Integrationsmuster

​LangGraph

​Beispielcode

​OpenAI Agents SDK

​Beispielcode

​Microsoft AutoGen

​Beispielcode

​Google ADK

​Vollständiges Beispiel

Zentrales Integrationsmuster

LangGraph

Beispielcode

OpenAI Agents SDK

Beispielcode

Microsoft AutoGen

Beispielcode

Google ADK

Vollständiges Beispiel