@DBOS.workflow()
def agentic_research_workflow(topic: str, max_iterations: int) -> Dict[str, Any]:
    all_findings = []
    research_history = []
    current_iteration = 0
    current_query = topic

    while current_iteration < max_iterations:
        current_iteration += 1
        iteration_result = research_query(topic, current_query, current_iteration)
        research_history.append(iteration_result)
        all_findings.append(iteration_result["evaluation"])

        if iteration_result["stories_found"] == 0:
            alternative_query = generate_follow_ups_step(topic, all_findings, current_iteration)
            if alternative_query:
                current_query = alternative_query
                continue

        if not should_continue_step(topic, all_findings, current_iteration, max_iterations):
            break

        if current_iteration < max_iterations:
            follow_up_query = generate_follow_ups_step(topic, all_findings, current_iteration)
            if follow_up_query:
                current_query = follow_up_query

    final_report = synthesize_findings_step(topic, all_findings)

    return {
        "topic": topic,
        "total_iterations": current_iteration,
        "max_iterations": max_iterations,
        "research_history": research_history,
        "final_report": final_report,
        "summary": {
            "total_stories": sum(r["stories_found"] for r in research_history),
            "total_comments": sum(r["comments_analyzed"] for r in research_history),
            "queries_executed": [r["query"] for r in research_history],
            "avg_relevance": (
                sum(f.get("relevance_score", 0) for f in all_findings) / len(all_findings)
                if all_findings else 0
            ),
        },
    }

@DBOS.workflow()
def research_query(topic: str, query: str, iteration: int) -> Dict[str, Any]:
    stories = search_hackernews_step(query, max_results=30)
    comments = []

    if stories:
        for story in stories:
            story_id = story.get("objectID")
            num_comments = story.get("num_comments", 0)
            if story_id and num_comments > 0:
                story_comments = get_comments_step(story_id, max_comments=10)
                comments.extend(story_comments)

    evaluation = evaluate_results_step(topic, query, stories, comments)

    return {
        "iteration": iteration,
        "query": query,
        "stories_found": len(stories),
        "comments_analyzed": len(comments),
        "evaluation": evaluation,
        "stories": stories,
        "comments": comments,
    }

@DBOS.step()
def search_hackernews_step(query: str, max_results: int = 20) -> List[Dict[str, Any]]:
    params = {"query": query, "hitsPerPage": max_results, "tags": "story"}
    with httpx.Client(timeout=30.0) as client:
        response = client.get("https://hn.algolia.com/api/v1/search", params=params)
        response.raise_for_status()
        return response.json()["hits"]

@DBOS.step()
def get_comments_step(story_id: str, max_comments: int = 50) -> List[Dict[str, Any]]:
    params = {"tags": f"comment,story_{story_id}", "hitsPerPage": max_comments}
    with httpx.Client(timeout=30.0) as client:
        response = client.get("https://hn.algolia.com/api/v1/search", params=params)
        response.raise_for_status()
        return response.json()["hits"]

@DBOS.step()
def synthesize_findings_step(topic: str, all_findings: List[Dict[str, Any]]) -> Dict[str, Any]:
    # Tổng hợp toàn bộ phát hiện thành một đoạn văn bản dài...
    # Chuẩn bị danh sách trích dẫn, citation links...
    # Gửi prompt tới mô hình LLM và nhận kết quả JSON báo cáo.
    
    # Trả về JSON kết quả báo cáo chứa báo cáo nghiên cứu chi tiết.