File size: 24,390 Bytes

8c10cf2

#!/usr/bin/env python3
"""
Analyze retry patterns in the SocialMediaManager-MCP project.

This script summarizes information such as:
- Error locations
- Retry counts
- Retry rates

It focuses on:
- BUSINESS-RETRY: a Chain line ending with the [BUSINESS-RETRY] marker.
  This indicates retries in content_generator (ShakespeareGeneratorCrew) or
  post_reviewer (PostReviewCrew).
- Orchestrator / crew_execution retries (if present), e.g.
  [SPAN] crew_execution (retry N) [RETRYN]
"""

import os
import re
from pathlib import Path
from collections import defaultdict
import json
import csv

# Base directory and model list
BASE_DIR = Path("/Users/wzr/TOSEM-2025/RESULTS")
MODELS = [
    "DeepSeek-R1",
    "DeepSeek-V3-1",
    "GPT-4o-mini",
    "GPT-5",
    "Gemini-2.5-flash",
    "Gemini-2.5-flash-nothinking",
    "Qwen3-235b",
]
PROJECT_NAME = "SocialMediaManager-MCP"


def extract_error_info(line: str) -> dict:
    """Extract error information from an error line.

    Returns:
        {'has_error': bool, 'node_type': str, 'node_name': str, 'error_msg': str}
    """
    # Remove tree drawing characters (including ─)
    clean = re.sub(r"^[│├└─\-\s]+", "", line).strip()

    # Check whether the error marker is present
    if "❌" not in clean:
        return {"has_error": False}

    # Remove the error marker
    clean = clean.split("❌", 1)[1].lstrip()

    # Extract node type and name
    node_match = re.match(
        r"\[(SPAN|Chain|AGENT|Tool|LLM)\]\s+([^\[\]]+?)(?:\s+\[ERROR:(.*))?$",
        clean,
    )
    if not node_match:
        return {
            "has_error": True,
            "node_type": "Unknown",
            "node_name": "Unknown",
            "error_msg": "",
        }

    node_type = node_match.group(1)
    node_name = node_match.group(2).strip()
    error_msg = node_match.group(3).strip() if node_match.group(3) else ""

    # Normalize node names
    if node_type == "AGENT":
        node_name = re.sub(r"\._execute_core$", "", node_name)
    elif node_type == "Tool":
        node_name = re.sub(r"\._use$", "", node_name)
    elif node_type == "Chain":
        node_name = re.sub(r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", node_name)

    return {
        "has_error": True,
        "node_type": node_type,
        "node_name": node_name,
        "error_msg": error_msg,
    }


def extract_retry_info(line: str) -> dict:
    """Extract retry information from a retry line.

    In SocialMediaManager-MCP:
    - BUSINESS-RETRY: a Chain line ending with the [BUSINESS-RETRY] marker.
      Example: [Chain] Crew_xxx.kickoff [...] [BUSINESS-RETRY]
    - Orchestrator retries (if present), e.g.
      [SPAN] crew_execution (retry 1) [RETRY1] ...

    Returns:
        {
            'is_retry': bool,
            'retry_type': str ('orchestrator' or 'business_logic'),
            'retry_number': int (for BUSINESS-RETRY this is inferred from ordering),
            'node_type': str,
            'node_name': str,
            'agent_name': str (inferred from the related AGENT context for grouping)
        }
    """
    # Remove tree drawing characters (including ─)
    clean = re.sub(r"^[│├└─\-\s]+", "", line).strip()

    # BUSINESS-RETRY marker: line contains [BUSINESS-RETRY]
    has_business_retry_flag = "[BUSINESS-RETRY]" in clean

    # Orchestrator retry markers: (retry N) or [RETRYN]
    orchestrator_retry_match = re.search(r"\(retry\s+(\d+)\)", clean)
    if not orchestrator_retry_match:
        orchestrator_retry_match = re.search(r"\[RETRY(\d+)\]", clean)

    # If neither retry marker exists, return False
    if not has_business_retry_flag and not orchestrator_retry_match:
        return {"is_retry": False}

    # Determine retry type and number
    if orchestrator_retry_match:
        retry_type = "orchestrator"
        retry_number = int(orchestrator_retry_match.group(1))
    else:
        retry_type = "business_logic"
        # BUSINESS-RETRY has no explicit retry number; infer from ordering
        retry_number = 0  # Will be set during post-processing

    # Extract node type and name (may have an "❌" prefix)
    node_match = re.match(
        r"(?:❌\s*)?\[(SPAN|Chain|AGENT)\]\s+([^\[\]]+?)(?:\s+\[.*)?$",
        clean,
    )
    if not node_match:
        return {
            "is_retry": True,
            "retry_type": retry_type,
            "retry_number": retry_number,
            "node_type": "Unknown",
            "node_name": "Unknown",
            "agent_name": "",
        }

    node_type = node_match.group(1)
    node_name = node_match.group(2).strip()

    # For Chain nodes, infer which crew (agent) it belongs to
    agent_name = ""
    if node_type == "Chain" and "Crew" in node_name:
        # Normalize UUID
        node_name = re.sub(r"Crew_[a-f0-9\-]+\.kickoff", "Crew***.kickoff", node_name)
        # BUSINESS-RETRY occurs in ShakespeareGeneratorCrew or PostReviewCrew.
        # We associate it using context in analyze_session.
        agent_name = ""  # Will be set during post-processing

    return {
        "is_retry": True,
        "retry_type": retry_type,
        "retry_number": retry_number,
        "node_type": node_type,
        "node_name": node_name,
        "agent_name": agent_name,
    }


def analyze_session(md_file: str) -> dict:
    """Analyze a single session's execution_path.md.

    Returns:
        {
            'has_error': bool,
            'has_retry': bool,
            'has_orchestrator_retry': bool,
            'has_business_retry': bool,
            'max_retry_number': int,
            'total_orchestrator_retries': int,
            'total_business_retries': int,
            'generator_retry_count': int (ShakespeareGeneratorCrew retry count),
            'reviewer_retry_count': int (PostReviewCrew retry count),
            'errors': [{'node_type': str, 'node_name': str, 'error_msg': str}, ...],
            'retries': [{
                'retry_type': str,
                'retry_number': int,
                'node_type': str,
                'node_name': str,
                'agent_name': str
            }, ...]
        }
    """
    if not os.path.exists(md_file):
        return None

    with open(md_file, "r", encoding="utf-8") as f:
        content = f.read()

    # Extract the "Execution Path Tree" section
    tree_match = re.search(
        r"## Execution Path Tree.*?```\n(.*?)```", content, re.DOTALL
    )
    if not tree_match:
        return None

    tree_content = tree_match.group(1)
    lines = tree_content.split("\n")

    errors = []
    retries = []
    current_agent = None  # Used to associate Chain with AGENT

    for i, line in enumerate(lines):
        if not line.strip():
            continue

        # Check errors
        error_info = extract_error_info(line)
        if error_info["has_error"]:
            errors.append(
                {
                    "node_type": error_info.get("node_type", "Unknown"),
                    "node_name": error_info.get("node_name", "Unknown"),
                    "error_msg": error_info.get("error_msg", ""),
                }
            )

        # Track current AGENT (for associating subsequent Chain entries)
        if "[AGENT]" in line:
            agent_match = re.search(
                r"\[AGENT\]\s+([^\._]+)", re.sub(r"^[│├└─\-\s]+", "", line).strip()
            )
            if agent_match:
                current_agent = agent_match.group(1).strip()

        # Check retries
        retry_info = extract_retry_info(line)
        if retry_info["is_retry"]:
            # For BUSINESS-RETRY Chain nodes, associate with the current AGENT
            if (
                retry_info["retry_type"] == "business_logic"
                and retry_info["node_type"] == "Chain"
            ):
                retry_info["agent_name"] = current_agent if current_agent else "Unknown"

            retries.append(retry_info)

    # Assign retry numbers to BUSINESS-RETRY entries (by occurrence order)
    business_retry_counter = {}
    for retry in retries:
        if retry["retry_type"] == "business_logic":
            agent_name = retry["agent_name"]
            if agent_name not in business_retry_counter:
                business_retry_counter[agent_name] = 0
            business_retry_counter[agent_name] += 1
            retry["retry_number"] = business_retry_counter[agent_name]

    # Summarize retries
    orchestrator_retries = [r for r in retries if r["retry_type"] == "orchestrator"]
    business_retries = [r for r in retries if r["retry_type"] == "business_logic"]

    max_retry = (
        max([r["retry_number"] for r in orchestrator_retries])
        if orchestrator_retries
        else 0
    )

    # Count Generator and Reviewer BUSINESS-RETRY occurrences
    generator_retries = [
        r for r in business_retries if "Shakespearean Bard" in r.get("agent_name", "")
    ]
    reviewer_retries = [
        r for r in business_retries if "X Post Verifier" in r.get("agent_name", "")
    ]

    return {
        "has_error": len(errors) > 0,
        "has_retry": len(retries) > 0,
        "has_orchestrator_retry": len(orchestrator_retries) > 0,
        "has_business_retry": len(business_retries) > 0,
        "max_retry_number": max_retry,
        "total_orchestrator_retries": len(orchestrator_retries),
        "total_business_retries": len(business_retries),
        "generator_retry_count": len(generator_retries),
        "reviewer_retry_count": len(reviewer_retries),
        "errors": errors,
        "retries": retries,
    }


def collect_model_stats(model_name: str) -> dict:
    """Collect retry statistics for a single model.

    Returns:
        {
            'model': str,
            'total_sessions': int,
            'sessions_with_error': int,
            'sessions_with_retry': int,
            'sessions_with_orchestrator_retry': int,
            'sessions_with_business_retry': int,
            'total_orchestrator_retry_attempts': int,
            'total_business_retry_attempts': int,
            'total_generator_retries': int,
            'total_reviewer_retries': int,
            'error_by_agent': {agent_name: count},
            'error_types': {error_msg: count},
            'max_retry_number': int,
            'business_retry_by_agent': {agent_name: count},
            'session_details': [...]
        }
    """
    test_results_dir = BASE_DIR / model_name / PROJECT_NAME / "test_results"

    if not test_results_dir.exists():
        return None

    stats = {
        "model": model_name,
        "total_sessions": 0,
        "sessions_with_error": 0,
        "sessions_with_retry": 0,
        "sessions_with_orchestrator_retry": 0,
        "sessions_with_business_retry": 0,
        "sessions_with_only_orchestrator_retry": 0,
        "sessions_with_only_business_retry": 0,
        "sessions_with_both_retries": 0,
        "total_orchestrator_retry_attempts": 0,
        "total_business_retry_attempts": 0,
        "total_generator_retries": 0,
        "total_reviewer_retries": 0,
        "error_by_agent": defaultdict(int),
        "error_by_node_type": defaultdict(int),
        "error_types": defaultdict(int),
        "max_retry_number": 0,
        "business_retry_by_agent": defaultdict(int),
        "session_details": [],
    }

    for session_dir in sorted(test_results_dir.iterdir()):
        if not session_dir.is_dir():
            continue

        exec_path_file = session_dir / "execution_path.md"
        if not exec_path_file.exists():
            continue

        stats["total_sessions"] += 1

        analysis = analyze_session(str(exec_path_file))
        if not analysis:
            continue

        # Count errors and retries
        if analysis["has_error"]:
            stats["sessions_with_error"] += 1

        if analysis["has_retry"]:
            stats["sessions_with_retry"] += 1

        if analysis["has_orchestrator_retry"]:
            stats["sessions_with_orchestrator_retry"] += 1
            stats["total_orchestrator_retry_attempts"] += analysis[
                "total_orchestrator_retries"
            ]
            stats["max_retry_number"] = max(
                stats["max_retry_number"], analysis["max_retry_number"]
            )

        if analysis["has_business_retry"]:
            stats["sessions_with_business_retry"] += 1
            stats["total_business_retry_attempts"] += analysis["total_business_retries"]
            stats["total_generator_retries"] += analysis["generator_retry_count"]
            stats["total_reviewer_retries"] += analysis["reviewer_retry_count"]

        # Count sessions with only one retry type vs. both
        if analysis["has_orchestrator_retry"] and not analysis["has_business_retry"]:
            stats["sessions_with_only_orchestrator_retry"] += 1
        elif analysis["has_business_retry"] and not analysis["has_orchestrator_retry"]:
            stats["sessions_with_only_business_retry"] += 1
        elif analysis["has_orchestrator_retry"] and analysis["has_business_retry"]:
            stats["sessions_with_both_retries"] += 1

        # Count error locations
        for error in analysis["errors"]:
            if error["node_type"] == "AGENT":
                stats["error_by_agent"][error["node_name"]] += 1
            stats["error_by_node_type"][error["node_type"]] += 1

            # Build a short error-type identifier
            error_msg = error["error_msg"]
            if error_msg:
                # Use the first 100 characters as the error-type key
                error_type = (
                    error_msg[:100]
                    if len(error_msg) <= 100
                    else error_msg[:100] + "..."
                )
                stats["error_types"][error_type] += 1

        # Count BUSINESS-RETRY occurrences by agent
        for retry in analysis["retries"]:
            if retry["retry_type"] == "business_logic" and retry["agent_name"]:
                stats["business_retry_by_agent"][retry["agent_name"]] += 1

        # Save session details
        stats["session_details"].append(
            {
                "session": session_dir.name,
                "has_error": analysis["has_error"],
                "has_retry": analysis["has_retry"],
                "has_orchestrator_retry": analysis["has_orchestrator_retry"],
                "has_business_retry": analysis["has_business_retry"],
                "orchestrator_retry_count": analysis["total_orchestrator_retries"],
                "business_retry_count": analysis["total_business_retries"],
                "generator_retry_count": analysis["generator_retry_count"],
                "reviewer_retry_count": analysis["reviewer_retry_count"],
                "errors": analysis["errors"],
                "retries": analysis["retries"],
            }
        )

    # Compute retry rates
    total = stats["total_sessions"]
    stats["retry_rate"] = (
        (stats["sessions_with_retry"] / total * 100) if total > 0 else 0
    )
    stats["orchestrator_retry_rate"] = (
        (stats["sessions_with_orchestrator_retry"] / total * 100) if total > 0 else 0
    )
    stats["business_retry_rate"] = (
        (stats["sessions_with_business_retry"] / total * 100) if total > 0 else 0
    )
    stats["error_rate"] = (
        (stats["sessions_with_error"] / total * 100) if total > 0 else 0
    )

    # Convert defaultdict to dict
    stats["error_by_agent"] = dict(stats["error_by_agent"])
    stats["error_by_node_type"] = dict(stats["error_by_node_type"])
    stats["error_types"] = dict(stats["error_types"])
    stats["business_retry_by_agent"] = dict(stats["business_retry_by_agent"])

    return stats


def print_summary(all_stats):
    """Print a human-readable summary."""
    print("\n" + "=" * 130)
    print(f"Retry Pattern Analysis Summary - {PROJECT_NAME}")
    print("=" * 130 + "\n")

    # Overall summary table
    print("## Per-model stats\n")
    header = f"{'Model':<30} {'Total Sessions':<15} {'Error Rate':<12} {'Retry Rate':<12} {'Business Retry':<15} {'Generator Retry':<16} {'Reviewer Retry':<16}"
    print(header)
    print("-" * 130)

    for stats in all_stats:
        if stats:
            print(
                f"{stats['model']:<30} {stats['total_sessions']:<10} "
                f"{stats['error_rate']:>8.1f}% {stats['retry_rate']:>8.1f}% "
                f"{stats['business_retry_rate']:>10.1f}% {stats['total_generator_retries']:<13} {stats['total_reviewer_retries']:<13}"
            )

    print("\n" + "=" * 130)

    # Detailed information per model
    for stats in all_stats:
        if not stats:
            continue

        print(f"\n### {stats['model']}\n")
        print(f"- **Total sessions**: {stats['total_sessions']}")
        print(
            f"- **Sessions with errors**: {stats['sessions_with_error']} ({stats['error_rate']:.1f}%)"
        )
        print(
            f"- **Sessions with retries**: {stats['sessions_with_retry']} ({stats['retry_rate']:.1f}%)"
        )
        if stats["sessions_with_orchestrator_retry"] > 0:
            print(
                f"  - Orchestrator retries: {stats['sessions_with_orchestrator_retry']} ({stats['orchestrator_retry_rate']:.1f}%) "
                f"[only orchestrator: {stats['sessions_with_only_orchestrator_retry']}]"
            )
        print(
            f"  - Business retries: {stats['sessions_with_business_retry']} ({stats['business_retry_rate']:.1f}%) "
            f"[only business: {stats['sessions_with_only_business_retry']}]"
        )
        if stats["sessions_with_both_retries"] > 0:
            print(f"  - Both types present: {stats['sessions_with_both_retries']}")

        print("- **Total retry attempts**:")
        if stats["total_orchestrator_retry_attempts"] > 0:
            print(
                f"  - Orchestrator retry attempts: {stats['total_orchestrator_retry_attempts']}"
            )
        print(
            f"  - Business retry attempts: {stats['total_business_retry_attempts']} "
            f"(Generator: {stats['total_generator_retries']}, Reviewer: {stats['total_reviewer_retries']})"
        )
        if stats["max_retry_number"] > 0:
            print(f"- **Max orchestrator retry number**: {stats['max_retry_number']}")

        if stats["error_by_agent"]:
            print("\n**Agents where errors occurred**:")
            for agent, count in sorted(
                stats["error_by_agent"].items(), key=lambda x: x[1], reverse=True
            )[:5]:
                print(f"  - {agent}: {count}")

        if stats["business_retry_by_agent"]:
            print("\n**Agents with BUSINESS-RETRY (sorted by count)**:")
            for agent, count in sorted(
                stats["business_retry_by_agent"].items(),
                key=lambda x: x[1],
                reverse=True,
            ):
                print(f"  - {agent}: {count}")

        if stats["error_types"]:
            print("\n**Error types (top 3)**:")
            for error_type, count in sorted(
                stats["error_types"].items(), key=lambda x: x[1], reverse=True
            )[:3]:
                print(f"  - [{count}] {error_type}")

        print("\n" + "-" * 130)


def save_results(all_stats):
    """Save results to files."""
    output_dir = Path(__file__).parent

    # Save detailed JSON
    json_file = output_dir / "retry_analysis.json"
    json_data = []
    for stats in all_stats:
        if stats:
            json_data.append(stats)

    with open(json_file, "w", encoding="utf-8") as f:
        json.dump(json_data, f, indent=2, ensure_ascii=False)
    print(f"\n✅ Detailed JSON saved: {json_file}")

    # Save CSV summary
    csv_file = output_dir / "retry_summary.csv"
    with open(csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(
            [
                "Model",
                "Total_Sessions",
                "Sessions_With_Error",
                "Error_Rate(%)",
                "Sessions_With_Retry",
                "Retry_Rate(%)",
                "Orchestrator_Retry_Sessions",
                "Orchestrator_Retry_Rate(%)",
                "Only_Orchestrator_Retry_Sessions",
                "Business_Retry_Sessions",
                "Business_Retry_Rate(%)",
                "Only_Business_Retry_Sessions",
                "Both_Retries_Sessions",
                "Total_Orchestrator_Retries",
                "Total_Business_Retries",
                "Total_Generator_Retries",
                "Total_Reviewer_Retries",
                "Max_Retry_Number",
            ]
        )

        for stats in all_stats:
            if stats:
                writer.writerow(
                    [
                        stats["model"],
                        stats["total_sessions"],
                        stats["sessions_with_error"],
                        f"{stats['error_rate']:.2f}",
                        stats["sessions_with_retry"],
                        f"{stats['retry_rate']:.2f}",
                        stats["sessions_with_orchestrator_retry"],
                        f"{stats['orchestrator_retry_rate']:.2f}",
                        stats["sessions_with_only_orchestrator_retry"],
                        stats["sessions_with_business_retry"],
                        f"{stats['business_retry_rate']:.2f}",
                        stats["sessions_with_only_business_retry"],
                        stats["sessions_with_both_retries"],
                        stats["total_orchestrator_retry_attempts"],
                        stats["total_business_retry_attempts"],
                        stats["total_generator_retries"],
                        stats["total_reviewer_retries"],
                        stats["max_retry_number"],
                    ]
                )

    print(f"✅ CSV summary saved: {csv_file}")

    # Save error location stats (by AGENT)
    error_csv_file = output_dir / "error_by_agent.csv"
    with open(error_csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Model", "Agent_Name", "Error_Count"])

        for stats in all_stats:
            if stats and stats["error_by_agent"]:
                for agent, count in sorted(
                    stats["error_by_agent"].items(), key=lambda x: x[1], reverse=True
                ):
                    writer.writerow([stats["model"], agent, count])

    print(f"✅ Error location stats (by agent) saved: {error_csv_file}")

    # Save BUSINESS-RETRY stats by agent
    retry_agent_csv_file = output_dir / "business_retry_by_agent.csv"
    with open(retry_agent_csv_file, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Model", "Agent_Name", "Retry_Count"])

        for stats in all_stats:
            if stats and stats["business_retry_by_agent"]:
                for agent, count in sorted(
                    stats["business_retry_by_agent"].items(),
                    key=lambda x: x[1],
                    reverse=True,
                ):
                    writer.writerow([stats["model"], agent, count])

    print(f"✅ BUSINESS-RETRY stats by agent saved: {retry_agent_csv_file}")


if __name__ == "__main__":
    print(f"Starting retry-pattern analysis - {PROJECT_NAME}...")
    print("Retry types:")
    print(
        "  1. Orchestrator retries: e.g. [SPAN] crew_execution (retry N) [RETRYN] (if present)"
    )
    print(
        "  2. BUSINESS-RETRY: a Chain line ending with [BUSINESS-RETRY]\n"
        "     - ShakespeareGeneratorCrew (content_generator)\n"
        "     - PostReviewCrew (post_reviewer)\n"
    )

    all_stats = []
    for model in MODELS:
        print(f"\n📊 Analyzing model: {model}")
        stats = collect_model_stats(model)
        if stats:
            all_stats.append(stats)
            print(
                f"  ✅ Done: {stats['total_sessions']} sessions, "
                f"{stats['sessions_with_error']} errors, "
                f"{stats['sessions_with_business_retry']} business retries "
                f"(Gen: {stats['total_generator_retries']}, Rev: {stats['total_reviewer_retries']})"
            )
        else:
            print("  ⚠️  Skipped (directory not found)")

    print_summary(all_stats)
    save_results(all_stats)

    print("\n" + "=" * 130)
    print("✅ Analysis complete")
    print("=" * 130)