AutoGen & CrewAI

pip install -U "autogen-agentchat" "autogen-ext[openai]"

import asyncio
import os
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(
    model="gpt-4o-mini",
    api_key=os.environ["XENOVIA_API_KEY"],
    base_url=f"https://runtime.xenovia.io/{os.environ['XENOVIA_AGENT_ID']}/v1"
)

assistant = AssistantAgent(
    "assistant",
    model_client=model_client,
    system_message="You are a helpful assistant."
)

async def main() -> None:
    result = await assistant.run(task="Summarise the key principles of AI governance.")
    print(result.messages[-1].content)

asyncio.run(main())

pip install crewai

import os, uuid
from crewai import Agent, Task, Crew, LLM

session_id = str(uuid.uuid4())

llm = LLM(
    model="openai/gpt-4o-mini",
    api_key=os.environ["XENOVIA_API_KEY"],
    base_url=f"https://runtime.xenovia.io/{os.environ['XENOVIA_AGENT_ID']}/v1",
    extra_headers={"X-Xenovia-Session-Id": session_id}
)

researcher = Agent(
    role="Researcher",
    goal="Find accurate information on the given topic",
    backstory="You are an expert researcher with strong analytical skills.",
    llm=llm
)

writer = Agent(
    role="Writer",
    goal="Write clear and concise reports",
    backstory="You are an experienced technical writer.",
    llm=llm
)

research_task = Task(
    description="Research AI governance frameworks",
    expected_output="A summary of key AI governance principles",
    agent=researcher
)

write_task = Task(
    description="Write a report based on the research",
    expected_output="A structured report on AI governance",
    agent=writer
)

crew = Crew(agents=[researcher, writer], tasks=[research_task, write_task])
result = crew.kickoff()
print(result)