How to Summarise Jira with AutoGen

import asyncio
import os
from typing import Annotated

import requests
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

ENCONVERT_API_KEY = os.environ["ENCONVERT_API_KEY"]
JIRA_URL = os.environ["JIRA_URL"]  # a publicly viewable Jira issue, Epic, or board URL


# Step 1: Wrap Distill as an AutoGen tool the agent can call on demand
# AutoGen reads the signature, annotations, and docstring to build the tool schema
def summarise_jira_page(
    url: Annotated[str, "Jira issue, Epic, board, or backlog URL to summarise"],
) -> str:
    """Fetch a Jira page and return a clean summary of its content."""
    response = requests.post(
        "https://api.enconvert.com/v2/distill",
        headers={
            "X-API-Key": ENCONVERT_API_KEY,
            "Content-Type": "application/json",
        },
        json={
            "urls": [url],
            "schema": {
                "summary": (
                    "a thorough summary of the Jira page, including its title, "
                    "current status, description, acceptance criteria, and the "
                    "key points from the comment thread"
                ),
            },
        },
        timeout=120,
    )
    response.raise_for_status()

    item = response.json()["results"][0]
    summary = (item.get("data") or {}).get("summary")
    if item.get("error") or not summary:
        raise RuntimeError(
            f"Distill failed for {url}: {item.get('error') or item.get('warnings')}"
        )
    return summary


# Step 2: Create an agent that can call the tool
model_client = OpenAIChatCompletionClient(
    model="gpt-4.1",
    api_key=os.environ["OPENAI_API_KEY"],
)

agent = AssistantAgent(
    name="jira_assistant",
    model_client=model_client,
    tools=[summarise_jira_page],
    system_message=(
        "You help engineering teams understand Jira work. "
        "Use the summarise_jira_page tool to read a Jira URL before answering."
    ),
    # Send the tool result back to the model so it writes the final answer
    reflect_on_tool_use=True,
)


# Step 3: Ask the agent about the Jira page
# agent.run is async, so it must be awaited inside an event loop
async def main() -> None:
    result = await agent.run(
        task=(
            f"Summarise {JIRA_URL} "
            "and list the acceptance criteria and current status."
        )
    )
    print(result.messages[-1].content)
    await model_client.close()


asyncio.run(main())