Reviewer Agent

# Reviewer agent with scoring and revision tracking
class ReviewerAgent:
    def __init__(self, approval_threshold: int = 7, max_revisions: int = 2):
        self.approval_threshold = approval_threshold
        self.max_revisions = max_revisions

    def review(self, content: dict, revision_count: int = 0) -> dict:
        """
        Review agent output. Returns decision and detailed feedback.
        """
        print(f"    [Reviewer] Reviewing (revision {revision_count}/{self.max_revisions})")

        # Quality checks
        issues = []
        score = 10

        # Check completeness
        required_fields = ["topic", "key_findings", "sources"]
        for field in required_fields:
            if field not in content or not content[field]:
                issues.append(f"Missing or empty: '{field}'")
                score -= 2

        # Check source quality
        sources = content.get("sources", [])
        if len(sources) < 2:
            issues.append("Fewer than 2 sources — add more citations")
            score -= 1

        # Check finding quality
        findings = content.get("key_findings", [])
        if len(findings) < 3:
            issues.append("Fewer than 3 key findings — research more thoroughly")
            score -= 2

        score = max(0, score)
        approved = score >= self.approval_threshold

        if not approved and revision_count >= self.max_revisions:
            return {
                "decision": "escalate",
                "reason": f"Max revisions ({self.max_revisions}) reached. Score: {score}/10",
                "score": score
            }

        return {
            "decision": "approved" if approved else "revise",
            "score": score,
            "issues": issues,
            "feedback": "; ".join(issues) if issues else "Output meets quality standards"
        }

# Test reviewer
reviewer = ReviewerAgent(approval_threshold=7, max_revisions=2)
content = {
    "topic": "Python AI frameworks",
    "key_findings": ["TensorFlow is popular", "PyTorch is flexible", "JAX is fast"],
    "sources": ["https://example.com/1", "https://example.com/2"]
}
result = reviewer.review(content)
print(f"Decision: {result['decision']}, Score: {result['score']}/10")