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Documentation Index

Fetch the complete documentation index at: https://docs.xenovia.io/llms.txt

Use this file to discover all available pages before exploring further.

Both AutoGen and CrewAI can target OpenAI-compatible endpoints, which makes Xenovia a good fit. The main job is to point the framework at your Xenovia proxy URL and keep a stable session header for the whole run.
AutoGen has multiple major-package eras in the wild. The example below uses the current autogen-agentchat and autogen-ext[openai] packages rather than the older pyautogen setup.

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AutoGen

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/a/{os.environ['XENOVIA_PROXY_ID']}/openai/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())
If your AutoGen client version exposes default request headers, add a stable X-Xenovia-Session-Id there so every turn lands in the same Xenovia session.

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CrewAI

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/a/{os.environ['XENOVIA_PROXY_ID']}/openai/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)

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How multi-agent traces work

In multi-agent setups, each agent turn is a separate call through the proxy. Each call:
  • Gets its own X-Xenovia-Trace-Id
  • Is evaluated independently by the request-stage Rego policy
  • Is grouped under the shared X-Xenovia-Session-Id
Filter by session_id in the Traces view to reconstruct the full agent conversation in order, with per-turn policy decisions and intent scores.

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Handling policy blocks

When a request is blocked by policy, AutoGen and CrewAI surface the upstream 403. Add framework-level error handling around the top-level run call so you can log the failure and trace ID cleanly.