Sequential Multi-Agent Workflow

# Sequential multi-agent pipeline
class SequentialMultiAgentPipeline:
    def __init__(self):
        # Define the pipeline: ordered list of (agent_name, agent_fn)
        self.pipeline = [
            ("researcher",  self._research_step),
            ("analyser",    self._analysis_step),
            ("writer",      self._writing_step),
            ("reviewer",    self._review_step),
        ]

    def _research_step(self, context: dict) -> dict:
        print("  [Researcher] Gathering information...")
        return {
            "findings": ["AI adoption increased 40%", "GPT-4 most used model", "Cost is main barrier"],
            "sources": ["https://example.com/report-2024"],
            "quality": 9
        }

    def _analysis_step(self, context: dict) -> dict:
        findings = context.get("researcher", {}).get("findings", [])
        print(f"  [Analyser] Analysing {len(findings)} findings...")
        return {
            "top_insight": findings[0] if findings else "No data",
            "trend": "upward",
            "confidence": "high"
        }

    def _writing_step(self, context: dict) -> dict:
        analysis = context.get("analyser", {})
        print(f"  [Writer] Creating report from analysis...")
        return {
            "report_title": "AI Market Report 2024",
            "summary": f"Key insight: {analysis.get('top_insight')}",
            "word_count": 500
        }

    def _review_step(self, context: dict) -> dict:
        report = context.get("writer", {})
        print(f"  [Reviewer] Reviewing report: '{report.get('report_title')}'")
        return {
            "approved": True,
            "score": 9,
            "feedback": "Excellent clarity and structure"
        }

    def run(self, goal: str) -> dict:
        print(f"Pipeline starting: {goal}")
        context = {"goal": goal}

        for agent_name, agent_fn in self.pipeline:
            result = agent_fn(context)
            context[agent_name] = result
            print(f"    ✓ {agent_name} complete")

            # Stop if reviewer rejects
            if agent_name == "reviewer" and not result.get("approved"):
                return {"status": "rejected", "feedback": result.get("feedback"), "context": context}

        return {"status": "approved", "final_report": context["writer"], "review": context["reviewer"]}

pipeline = SequentialMultiAgentPipeline()
result = pipeline.run("AI market trends 2024")
print(f"\nStatus: {result['status']}")
print(f"Report: {result['final_report']['report_title']}")