data/processed/extract_trace_tree.py

#!/usr/bin/env python3
"""
Generic Langfuse trace tree extraction tool.

Builds a parent-child tree and sorts siblings by timestamp.
"""

import json
import sys
import os
import re
from typing import Dict, List, Tuple, Optional
from datetime import datetime


def format_time(latency_ms: float) -> str:
    """Format latency for display. `latency_ms` is in milliseconds."""
    if latency_ms < 1000:
        return f"{latency_ms:.0f}ms"
    else:
        seconds = latency_ms / 1000
        return f"{seconds:.2f}s"


def format_tokens(
    prompt_tokens: int,
    completion_tokens: int,
    total_tokens: int,
    reasoning_tokens: int = 0,
) -> str:
    """Format token usage. Always shows REASONING tokens (0 for non-reasoning models)."""
    actual_output = completion_tokens - reasoning_tokens
    return f"({prompt_tokens}→{completion_tokens} [REASONING:{reasoning_tokens}, OUTPUT:{actual_output}], total: {total_tokens})"


def calculate_subtree_stats(
    obs_id: str, children_map: Dict[str, List[Dict]]
) -> Tuple[int, int, int, int, float]:
    """Compute subtree stats: prompt, completion, reasoning, total tokens, and total time (ms)."""
    total_prompt = 0
    total_completion = 0
    total_reasoning = 0
    total_tokens = 0
    total_time = 0.0

    children = children_map.get(obs_id, [])
    for child in children:
        child_type = child.get("type", "")

        # Accumulate tokens for the current node (LLM only)
        if child_type == "GENERATION":
            total_prompt += child.get("promptTokens", 0)
            total_completion += child.get("completionTokens", 0)
            total_tokens += child.get("totalTokens", 0)

            # Extract REASONING tokens
            usage_details = child.get("usageDetails", {})
            if isinstance(usage_details, dict):
                total_reasoning += usage_details.get("completion_details.reasoning", 0)

        # Accumulate time for the current node (LLM and Tool)
        if child_type in ["GENERATION", "TOOL"]:
            total_time += child.get("latency", 0.0)

        # Recurse into children
        child_stats = calculate_subtree_stats(child["id"], children_map)
        total_prompt += child_stats[0]
        total_completion += child_stats[1]
        total_reasoning += child_stats[2]
        total_tokens += child_stats[3]
        total_time += child_stats[4]

    return total_prompt, total_completion, total_reasoning, total_tokens, total_time


def simplify_name(
    obs_type: str, obs_name: str, obs: Dict, children_map: Dict[str, List[Dict]] = None
) -> str:
    """Build a display name and append token/time information."""
    base_name = ""
    suffix = ""
    error_prefix = ""

    # Error handling
    obs_level = obs.get("level", "DEFAULT")
    status_message = obs.get("statusMessage")
    if obs_level == "ERROR" and status_message:
        error_prefix = "[ERROR] "
        # Keep error text short for readability (cannot update error_stats here)
        if len(status_message) > 80:
            error_msg = status_message[:70] + "[TRUNCATED]"
        else:
            error_msg = status_message
        suffix = f" [ERROR: {error_msg}]" + suffix

    # SPAN
    if obs_type == "SPAN":
        if "Crew Created" in obs_name:
            base_name = "[Crew Created]"
        elif "Task Created" in obs_name:
            base_name = "[Task Created]"
        # Note: check longer strings first to avoid partial matches
        elif "Tool Usage Error" in obs_name:
            base_name = "[Tool Usage Error]"
        elif "Tool Repeated Usage" in obs_name:
            base_name = "[Tool Repeated Usage]"
        elif "Tool Usage" in obs_name:
            base_name = "[Tool Usage]"
        else:
            base_name = f"[SPAN] {obs_name}"

    # AGENT
    elif obs_type == "AGENT":
        base_name = f"[AGENT] {obs_name}"

    # GENERATION (LLM) - token/time
    elif obs_type == "GENERATION":
        model = obs.get("model", "unknown")
        if "/" in model:
            model = model.split("/")[-1]
        base_name = f"[LLM] {model}"

        # Token usage
        prompt_tokens = obs.get("promptTokens", 0)
        completion_tokens = obs.get("completionTokens", 0)
        total_tokens = obs.get("totalTokens", 0)

        # Extract REASONING tokens
        reasoning_tokens = 0
        usage_details = obs.get("usageDetails", {})
        if isinstance(usage_details, dict):
            reasoning_tokens = usage_details.get("completion_details.reasoning", 0)

        if total_tokens > 0:
            suffix += f" {format_tokens(prompt_tokens, completion_tokens, total_tokens, reasoning_tokens)}"

        # Latency
        latency = obs.get("latency", 0.0)
        if latency > 0:
            suffix += f" [{format_time(latency)}]"

    # TOOL - latency
    elif obs_type == "TOOL":
        base_name = f"[Tool] {obs_name}"

        # Latency
        latency = obs.get("latency", 0.0)
        if latency > 0:
            suffix += f" [{format_time(latency)}]"

    # CHAIN
    elif obs_type == "CHAIN":
        base_name = f"[Chain] {obs_name}"

    # Other
    else:
        base_name = f"[{obs_type}] {obs_name}"

    # For parent nodes (SPAN/CHAIN/AGENT), show subtree summary.
    if obs_type in ["SPAN", "CHAIN", "AGENT"] and children_map:
        stats = calculate_subtree_stats(obs["id"], children_map)
        (
            total_prompt,
            total_completion,
            total_reasoning,
            total_tokens_sum,
            total_time,
        ) = stats

        stats_parts = []
        if total_tokens_sum > 0:
            stats_parts.append(
                f"tokens: {format_tokens(total_prompt, total_completion, total_tokens_sum, total_reasoning)}"
            )

        # Time: SPAN/CHAIN/AGENT use their own latency
        node_latency = obs.get("latency", 0.0)
        if node_latency and node_latency > 0:
            stats_parts.append(f"time: {format_time(node_latency)}")

        if stats_parts:
            suffix += f" [∑ {', '.join(stats_parts)}]"

    return error_prefix + base_name + suffix


def should_filter_observation(obs: Dict, is_a2a_project: bool) -> bool:
    """Return True if an observation should be filtered out."""
    obs_name = obs.get("name", "")
    obs_type = obs.get("type", "")

    # 1) Filter HTTP client tracing nodes (noise)
    metadata = obs.get("metadata", {})
    scope_name = metadata.get("scope", {}).get("name", "")
    if scope_name == "opentelemetry.instrumentation.httpx" and obs_type == "SPAN":
        # HTTP method nodes
        if obs_name in ["POST", "GET", "PUT", "DELETE", "PATCH", "HEAD", "OPTIONS"]:
            return True

    # 2) A2A framework noise patterns
    if is_a2a_project:
        a2a_noise_patterns = [
            "a2a.server.events.event_queue.",
            "a2a.server.events.in_memory_queue_manager.",
            "a2a.server.events.event_consumer.",
            "a2a.server.request_handlers.default_request_handler.",
            "a2a.server.request_handlers.jsonrpc_handler.",
        ]

        # Prefix match
        for pattern in a2a_noise_patterns:
            if obs_name.startswith(pattern):
                return True

    return False


def detect_project_variant_from_path(trace_file: str) -> Optional[str]:
    file_path = os.path.abspath(trace_file)
    path_lower = file_path.lower()

    m = re.search(r"(?:^|/)[^/]+[-_](mcp|a2a_mix|a2a|h_a2a)(?:/|$)", path_lower)
    if m:
        return m.group(1)

    return None


def build_children_map(
    observations: List[Dict], is_a2a_project: bool = False, is_a2a_mix: bool = False
) -> Tuple[Dict[str, List[Dict]], Dict]:
    """Build parent->children mapping; children are sorted by timestamp and name.

    Returns:
        children_map: mapping of parent observation id -> list of child observations
        error_stats: error statistics
    """
    children_map = {}
    filtered_obs = []
    error_stats = {
        "total_errors": 0,
        "filtered_errors": 0,
        "visible_errors": 0,
        "error_messages": [],
        "http_filtered": 0,  # number of filtered HTTP request nodes
        # Error categories
        "a2a_framework_errors": [],  # A2A framework/internal errors
        "http_request_errors": [],  # HTTP request node errors
        "tool_usage_errors": [],  # Tool Usage node errors
        "tool_child_span_errors": [],  # Tool child SPAN errors (A2A_mix only)
        # Context-related errors
        "filtered_errors_no_parent_error": [],  # filtered error node with no ancestor error
        "filtered_errors_no_child_error": [],  # filtered error node with no descendant error
        # Truncation stats
        "truncated_errors_count": 0,  # number of truncated error messages
    }

    # Map id -> observation (for ancestor/descendant lookup)
    all_obs_map = {obs["id"]: obs for obs in observations}

    # Build raw children mapping (for descendant lookup)
    all_children_map = {}
    for obs in observations:
        parent_id = obs.get("parentObservationId")
        if parent_id:
            if parent_id not in all_children_map:
                all_children_map[parent_id] = []
            all_children_map[parent_id].append(obs)

    # Helper: any ancestor has ERROR
    def has_ancestor_error(obs_id: str) -> bool:
        """Check whether any ancestor node has level=ERROR."""
        obs = all_obs_map.get(obs_id)
        if not obs:
            return False

        parent_id = obs.get("parentObservationId")
        while parent_id:
            parent_obs = all_obs_map.get(parent_id)
            if not parent_obs:
                break
            if parent_obs.get("level") == "ERROR":
                return True
            parent_id = parent_obs.get("parentObservationId")
        return False

    # Helper: any descendant has ERROR
    def has_descendant_error(obs_id: str) -> bool:
        """Check whether any descendant node has level=ERROR."""
        children = all_children_map.get(obs_id, [])
        for child in children:
            if child.get("level") == "ERROR":
                return True
            if has_descendant_error(child["id"]):
                return True
        return False

    # Step 1: filter noise observations and collect error statistics
    for obs in observations:
        obs_name = obs.get("name", "")
        obs_type = obs.get("type", "")
        obs_level = obs.get("level", "DEFAULT")
        status_msg = obs.get("statusMessage") or "Unknown error"

        # Count errors
        if obs_level == "ERROR":
            error_stats["total_errors"] += 1

            if should_filter_observation(obs, is_a2a_project):
                error_stats["filtered_errors"] += 1

                # Categorize filtered errors
                metadata = obs.get("metadata", {})
                scope_name = metadata.get("scope", {}).get("name", "")

                # Determine filtered-node type
                obs_id = obs.get("id")
                error_info = {
                    "name": obs_name,
                    "type": obs_type,
                    "message": status_msg,
                    "id": obs_id,
                }

                if (
                    scope_name == "opentelemetry.instrumentation.httpx"
                    and obs_name
                    in ["POST", "GET", "PUT", "DELETE", "PATCH", "HEAD", "OPTIONS"]
                ):
                    error_stats["http_request_errors"].append(error_info)
                else:
                    # A2A framework/internal errors
                    error_stats["a2a_framework_errors"].append(error_info)

                # Check context (ancestors/descendants)
                if not has_ancestor_error(obs_id):
                    error_stats["filtered_errors_no_parent_error"].append(error_info)
                if not has_descendant_error(obs_id):
                    error_stats["filtered_errors_no_child_error"].append(error_info)
            else:
                error_stats["visible_errors"] += 1
                # Record visible errors (for the summary section)
                if status_msg and status_msg not in error_stats["error_messages"]:
                    error_stats["error_messages"].append(status_msg)

        # Filtering
        is_filtered = should_filter_observation(obs, is_a2a_project)
        if is_filtered:
            # Count filtered HTTP request nodes
            metadata = obs.get("metadata", {})
            scope_name = metadata.get("scope", {}).get("name", "")
            if scope_name == "opentelemetry.instrumentation.httpx" and obs_name in [
                "POST",
                "GET",
                "PUT",
                "DELETE",
                "PATCH",
                "HEAD",
                "OPTIONS",
            ]:
                error_stats["http_filtered"] += 1
        else:
            filtered_obs.append(obs)

    # Step 2: rebuild parent-child relationships (skipping filtered nodes)
    # Map id -> filtered observation
    id_to_obs = {obs["id"]: obs for obs in filtered_obs}

    # Helper: check if a node is under Crew***.kickoff chain
    def is_under_crew_chain(obs: Dict, all_observations: List[Dict]) -> bool:
        """Return True if the node is under a Crew_*.kickoff CHAIN."""
        parent_id = obs.get("parentObservationId")
        visited = set()  # avoid cycles

        while parent_id and parent_id not in visited:
            visited.add(parent_id)
            parent = next((o for o in all_observations if o["id"] == parent_id), None)
            if not parent:
                break

            # Check Crew chain
            if parent.get("type") == "CHAIN":
                chain_name = parent.get("name", "")
                # Match Crew_<uuid>.kickoff
                if re.match(r"Crew_[a-f0-9\-]+\.kickoff", chain_name):
                    return True

            parent_id = parent.get("parentObservationId")

        return False

    # Helper: parent-based filtering rules
    def should_filter_by_parent(
        child_obs: Dict, parent_obs: Dict, is_a2a_mix_project: bool
    ) -> bool:
        """Decide whether to filter a child based on its parent and project type."""
        if not parent_obs:
            return False

        parent_type = parent_obs.get("type", "")
        child_name = child_obs.get("name", "")
        child_type = child_obs.get("type", "")

        # Rule 1: all projects - filter [Tool] -> Tool Usage spans
        if parent_type == "TOOL":
            if "Tool Usage" in child_name or "Tool Repeated Usage" in child_name:
                return True

            # Rule 2: A2A_mix only - filter specific MCP spans under [Tool]
            if is_a2a_mix_project and child_type == "SPAN":
                mcp_tool_noise = [
                    "GET",
                    "POST",
                    "mcp client/operation",
                    "mcp initialize",
                    "mcp tools/call",
                    "mcp tools/list",
                ]
                if child_name in mcp_tool_noise:
                    return True

        # Rule 3: only under Crew chain - filter [AGENT] telemetry spans.
        if parent_type == "AGENT":
            if child_type == "SPAN" and (
                "Tool Usage" in child_name or "Tool Repeated Usage" in child_name
            ):
                # Only apply under Crew chain
                if is_under_crew_chain(parent_obs, observations):
                    return True

        return False

    # Build parent-child relationships; if the direct parent is filtered, climb upwards
    for obs in filtered_obs:
        parent_id = obs.get("parentObservationId")

        # Find a valid parent (skip filtered nodes)
        while parent_id and parent_id not in id_to_obs:
            # Lookup parent in the original list
            parent_obs = next((o for o in observations if o["id"] == parent_id), None)
            if parent_obs:
                parent_id = parent_obs.get("parentObservationId")
            else:
                parent_id = None

        # Parent-based filtering
        parent_obs = id_to_obs.get(parent_id) if parent_id else None
        if should_filter_by_parent(obs, parent_obs, is_a2a_mix):
            # If this node has an error, count it into the appropriate category
            if obs.get("level") == "ERROR":
                obs_name = obs.get("name", "")
                obs_type = obs.get("type", "")
                status_msg = obs.get("statusMessage") or "Unknown error"

                obs_id = obs.get("id")
                error_info = {
                    "name": obs_name,
                    "type": obs_type,
                    "message": status_msg,
                    "id": obs_id,
                }

                if "Tool Usage" in obs_name or "Tool Repeated Usage" in obs_name:
                    error_stats["tool_usage_errors"].append(error_info)
                elif is_a2a_mix and parent_obs and parent_obs.get("type") == "TOOL":
                    # Tool child SPAN error (A2A_mix only)
                    error_stats["tool_child_span_errors"].append(error_info)

                # Context checks
                if not has_ancestor_error(obs_id):
                    error_stats["filtered_errors_no_parent_error"].append(error_info)
                if not has_descendant_error(obs_id):
                    error_stats["filtered_errors_no_child_error"].append(error_info)

            continue  # skip this node

        if parent_id:
            if parent_id not in children_map:
                children_map[parent_id] = []
            children_map[parent_id].append(obs)
        else:
            # Root node
            if "ROOT" not in children_map:
                children_map["ROOT"] = []
            children_map["ROOT"].append(obs)

    # Sort children by timestamp, then by name
    for parent_id in children_map:
        children = children_map[parent_id]
        children.sort(key=lambda x: (x.get("startTime", ""), x.get("name", "")))

    return children_map, error_stats


def print_tree_recursive(
    obs: Dict,
    children_map: Dict[str, List[Dict]],
    prefix: str,
    is_last: bool,
    output_lines: List[str],
    batch_info: Dict[str, Dict] = None,
    self_eval_retry_info: Dict[str, bool] = None,
):
    """Recursively render the tree."""
    obs_type = obs.get("type", "UNKNOWN")
    obs_name = obs.get("name", "unnamed")
    obs_id = obs["id"]

    # Display name (pass children_map to compute subtree stats)
    display_name = simplify_name(obs_type, obs_name, obs, children_map)

    # SQL series: detect business_retry (can appear at any level)
    if obs_type == "SPAN" and "business_retry" in obs_name.lower():
        m_business = re.search(r"\bbusiness_retry\s*(\d+)\b", obs_name, re.IGNORECASE)
        if m_business and "[BUSINESS-RETRY]" not in display_name:
            display_name = f"{display_name} [BUSINESS-RETRY]"

    # Add batch annotation (write_a_book_with_flows only)
    if batch_info and obs_id in batch_info:
        info = batch_info[obs_id]
        batch_str = f"BATCH{info['batch']}"
        if info["is_retry"]:
            batch_str += " [BUSINESS-RETRY]"
        if info["chapter_title"]:
            batch_str += f" ({info['chapter_title']})"
        display_name = f"{display_name} {batch_str}"

    # Add self_evaluation_loop retry annotations
    if self_eval_retry_info and obs_id in self_eval_retry_info:
        if self_eval_retry_info[obs_id]:
            display_name = f"{display_name} [BUSINESS-RETRY]"

    # Current line
    connector = "└─ " if is_last else "├─ "
    output_lines.append(f"{prefix}{connector}{display_name}")

    # Prefix for children
    if is_last:
        new_prefix = prefix + "   "  # 3 spaces
    else:
        new_prefix = prefix + "│  "  # │ + 2 spaces

    # Recurse into children
    children = children_map.get(obs_id, [])

    # Filter nested LLM calls: for a GENERATION node, hide its GENERATION children
    if obs_type == "GENERATION":
        children = [child for child in children if child.get("type") != "GENERATION"]

    for i, child in enumerate(children):
        is_last_child = i == len(children) - 1
        print_tree_recursive(
            child,
            children_map,
            new_prefix,
            is_last_child,
            output_lines,
            batch_info,
            self_eval_retry_info,
        )


def build_tree_structure(
    observations: List[Dict],
    is_a2a_project: bool = False,
    is_a2a_mix: bool = False,
    project_type: Optional[str] = None,
    self_eval_project_type: Optional[str] = None,
) -> Tuple[List[str], Dict]:
    """Build the tree output lines."""
    output_lines = []

    # Parent-child map
    children_map, error_stats = build_children_map(
        observations, is_a2a_project, is_a2a_mix
    )

    # Batch analysis (write_a_book_with_flows only)
    batch_info = {}
    if project_type:
        batch_info = analyze_write_chapters_batches(
            observations, children_map, project_type
        )

    # Retry analysis (self_evaluation_loop_flow only)
    self_eval_retry_info = {}
    if self_eval_project_type:
        self_eval_retry_info = analyze_self_evaluation_retries(
            observations, children_map, self_eval_project_type
        )

    # Render from root nodes
    root_nodes = children_map.get("ROOT", [])

    for i, root in enumerate(root_nodes):
        obs_type = root.get("type", "UNKNOWN")
        obs_name = root.get("name", "unnamed")
        display_name = simplify_name(obs_type, obs_name, root, children_map)

        # Root node (no prefix)
        output_lines.append(display_name)

        # Children
        children = children_map.get(root["id"], [])
        for j, child in enumerate(children):
            # Detect RETRY on the first layer SPAN under root
            if child.get("type") == "SPAN":
                name = child.get("name", "")

                # Detect business_retry N (SQL series)
                m_business = re.search(
                    r"\bbusiness_retry\s*(\d+)\b", name, re.IGNORECASE
                )
                if (
                    m_business
                    and "[BUSINESS-RETRY]" not in name
                    and "[RETRY" not in name
                ):
                    child["name"] = f"{name} [BUSINESS-RETRY]"

                # Detect retry N (orchestrator-level)
                elif not m_business:
                    m = re.search(r"\bretry\s*(\d+)\b", name, re.IGNORECASE)
                    if m:
                        retry_idx = m.group(1)
                        # Add marker if missing
                        if "[RETRY" not in name:
                            child["name"] = f"{name} [RETRY{retry_idx}]"
            is_last_child = j == len(children) - 1
            print_tree_recursive(
                child,
                children_map,
                "",
                is_last_child,
                output_lines,
                batch_info,
                self_eval_retry_info,
            )

    return output_lines, error_stats


def detect_a2a_project(trace_file: str) -> bool:
    """Detect whether the trace belongs to an A2A/A2A_mix project (by path)."""
    file_path = os.path.abspath(trace_file)
    path_lower = file_path.lower()

    variant = detect_project_variant_from_path(file_path)
    if variant in {"a2a", "a2a_mix", "h_a2a"}:
        return True

    if "-a2a" in path_lower or "_a2a" in path_lower:
        return True
    if "a2a-" in path_lower or "a2a_" in path_lower:
        return True

    return False


def detect_a2a_mix_project(trace_file: str) -> bool:
    """Detect whether the trace belongs to an A2A_mix project (by path)."""
    file_path = os.path.abspath(trace_file)
    path_lower = file_path.lower()

    variant = detect_project_variant_from_path(file_path)
    if variant == "a2a_mix":
        return True

    # Check A2A_mix markers
    if "-a2a_mix" in path_lower or "_a2a_mix" in path_lower:
        return True
    if "a2a-mix" in path_lower or "a2a_mix" in path_lower:
        return True

    return False


def detect_write_book_project(trace_file: str) -> Optional[str]:
    """Detect write_a_book_with_flows traces and return project type (MCP/A2A/A2A_mix) or None."""
    file_path = os.path.abspath(trace_file)
    path_lower = file_path.lower()

    if (
        "write_a_book_with_flows" not in path_lower
        and "write-a-book-with-flows" not in path_lower
    ):
        return None

    variant = detect_project_variant_from_path(file_path)
    if variant == "a2a_mix":
        return "A2A_mix"
    if variant in {"a2a", "h_a2a"}:
        return "A2A"
    if variant == "mcp":
        return "MCP"

    if (
        "-a2a_mix" in path_lower
        or "_a2a_mix" in path_lower
        or "a2a-mix" in path_lower
        or "a2a_mix" in path_lower
    ):
        return "A2A_mix"
    elif (
        "-a2a" in path_lower
        or "_a2a" in path_lower
        or "a2a-" in path_lower
        or "a2a_" in path_lower
    ):
        return "A2A"
    elif (
        "-mcp" in path_lower
        or "_mcp" in path_lower
        or "mcp-" in path_lower
        or "mcp_" in path_lower
    ):
        return "MCP"

    return None


def detect_self_evaluation_project(trace_file: str) -> Optional[str]:
    """Detect self_evaluation_loop_flow traces and return project type MCP/A2A/A2A_mix."""
    path_lower = trace_file.lower()

    # Check marker
    if (
        "self_evaluation_loop_flow" not in path_lower
        and "self-evaluation-loop-flow" not in path_lower
    ):
        return None

    # Detect concrete type
    if (
        "-a2a_mix" in path_lower
        or "_a2a_mix" in path_lower
        or "a2a-mix" in path_lower
        or "a2a_mix" in path_lower
    ):
        return "A2A_mix"
    elif (
        "-a2a" in path_lower
        or "_a2a" in path_lower
        or "a2a-" in path_lower
        or "a2a_" in path_lower
    ):
        return "A2A"
    elif (
        "-mcp" in path_lower
        or "_mcp" in path_lower
        or "mcp-" in path_lower
        or "mcp_" in path_lower
    ):
        return "MCP"

    return None


def analyze_self_evaluation_retries(
    observations: List[Dict],
    children_map: Dict[str, List[Dict]],
    project_type: str,
) -> Dict[str, bool]:
    """Analyze RETRY behavior for self_evaluation_loop_flow.

    Returns: {obs_id: is_retry}
    """
    retry_info = {}

    # Find all content_generation_loop SPANs
    content_loop_nodes = []
    for obs in observations:
        if (
            obs.get("type") == "SPAN"
            and "content_generation_loop" in obs.get("name", "").lower()
        ):
            content_loop_nodes.append(obs)

    if not content_loop_nodes:
        return {}

    if project_type == "MCP":
        # MCP: detect retries within each loop
        for content_loop_node in content_loop_nodes:
            # Recursively find CHAIN kickoff nodes under the loop
            chain_nodes = []

            def find_chain_nodes(parent_id: str, depth: int = 0, max_depth: int = 5):
                if depth > max_depth:
                    return
                children = children_map.get(parent_id, [])
                for child in children:
                    if (
                        child.get("type") == "CHAIN"
                        and "kickoff" in child.get("name", "").lower()
                    ):
                        chain_nodes.append(child)
                    else:
                        find_chain_nodes(child["id"], depth + 1, max_depth)

            find_chain_nodes(content_loop_node["id"])

            # Extract agent role from each CHAIN node (retry detection within this loop)
            role_seen = {}  # role -> first-seen node

            for node in chain_nodes:
                # Try to extract role from AGENT children
                agent_children = children_map.get(node["id"], [])
                for agent in agent_children:
                    if agent.get("type") == "AGENT":
                        agent_name = agent.get("name", "")
                        # Extract role name (before _execute_core)
                        role = (
                            agent_name.split("._execute_core")[0]
                            if "._execute_core" in agent_name
                            else agent_name
                        )

                        # Target roles
                        if "Shakespearean Bard" in role or "X Post Verifier" in role:
                            if role in role_seen:
                                # Second occurrence => RETRY
                                retry_info[node["id"]] = True
                            else:
                                # First occurrence
                                role_seen[role] = node
                                retry_info[node["id"]] = False
                        break

    else:
        # A2A/A2A_mix: detect retries within each loop
        for content_loop_node in content_loop_nodes:
            # Find a2a_call_content_generator / a2a_call_post_reviewer spans under the loop
            target_spans = []

            def find_target_spans(parent_id: str, depth: int = 0, max_depth: int = 5):
                if depth > max_depth:
                    return
                children = children_map.get(parent_id, [])
                for child in children:
                    if child.get("type") == "SPAN":
                        name = child.get("name", "").lower()
                        if (
                            "a2a_call_content_generator" in name
                            or "a2a_call_post_reviewer" in name
                        ):
                            target_spans.append(child)
                        else:
                            find_target_spans(child["id"], depth + 1, max_depth)

            find_target_spans(content_loop_node["id"])

            # Sort by time
            target_spans_with_time = []
            for node in target_spans:
                start_time = node.get("startTime", "")
                if start_time:
                    try:
                        dt = datetime.fromisoformat(start_time.replace("Z", "+00:00"))
                        target_spans_with_time.append((node, dt))
                    except:
                        pass

            target_spans_with_time.sort(key=lambda x: x[1])

            # Detect retry within the loop
            span_type_seen = {}  # span_type -> first-seen

            for node, dt in target_spans_with_time:
                name = node.get("name", "").lower()

                # Determine type
                if "content_generator" in name:
                    span_type = "content_generator"
                elif "post_reviewer" in name:
                    span_type = "post_reviewer"
                else:
                    continue

                if span_type in span_type_seen:
                    # Second occurrence => RETRY
                    retry_info[node["id"]] = True
                else:
                    # First occurrence
                    span_type_seen[span_type] = node
                    retry_info[node["id"]] = False

    return retry_info


def extract_chapter_title(obs: Dict) -> Optional[str]:
    """Extract chapter_title from an observation."""
    # Try `input`
    obs_input = obs.get("input")
    if obs_input:
        if isinstance(obs_input, dict):
            return obs_input.get("chapter_title")
        elif isinstance(obs_input, str):
            try:
                input_dict = json.loads(obs_input)
                if isinstance(input_dict, dict):
                    return input_dict.get("chapter_title")
            except:
                pass

    # Try `metadata`
    metadata = obs.get("metadata", {})
    if isinstance(metadata, dict):
        return metadata.get("chapter_title")

    return None


def analyze_write_chapters_batches(
    observations: List[Dict],
    children_map: Dict[str, List[Dict]],
    project_type: str,
) -> Dict[str, Dict]:
    """Analyze batch info under write_chapters.

    Returns: {obs_id: {'batch': batch_no, 'is_retry': is_retry, 'chapter_title': chapter_title}}
    """
    # Find all write_chapters SPANs
    write_chapters_nodes = []
    for obs in observations:
        if (
            obs.get("type") == "SPAN"
            and "write_chapters" in obs.get("name", "").lower()
        ):
            write_chapters_nodes.append(obs)

    if not write_chapters_nodes:
        return {}

    # Locate crew nodes based on project type.
    # Collect all crew nodes to detect retries across orchestrator retries.
    crew_nodes = []

    if project_type == "MCP":
        # MCP: CHAIN kickoff nodes are directly under write_chapters
        for write_chapters_node in write_chapters_nodes:
            children = children_map.get(write_chapters_node["id"], [])
            for child in children:
                if (
                    child.get("type") == "CHAIN"
                    and "kickoff" in child.get("name", "").lower()
                ):
                    crew_nodes.append(child)
    else:
        # A2A/A2A_mix: write_chapters has extra SPAN wrappers; find CHAIN kickoff recursively
        def find_crew_nodes(parent_id: str, depth: int = 0, max_depth: int = 3):
            if depth > max_depth:
                return
            children = children_map.get(parent_id, [])
            for child in children:
                if (
                    child.get("type") == "CHAIN"
                    and "kickoff" in child.get("name", "").lower()
                ):
                    crew_nodes.append(child)
                elif child.get("type") == "SPAN":
                    # Keep searching
                    find_crew_nodes(child["id"], depth + 1, max_depth)

        for write_chapters_node in write_chapters_nodes:
            find_crew_nodes(write_chapters_node["id"])

    if not crew_nodes:
        return {}

    # Sort by start time
    crew_nodes_with_time = []
    for node in crew_nodes:
        start_time = node.get("startTime", "")
        if start_time:
            try:
                dt = datetime.fromisoformat(start_time.replace("Z", "+00:00"))
                crew_nodes_with_time.append((node, dt))
            except:
                pass

    crew_nodes_with_time.sort(key=lambda x: x[1])

    # Extract chapter_title and detect retry
    crew_info = []
    chapter_titles_seen = {}

    for node, dt in crew_nodes_with_time:
        chapter_title = extract_chapter_title(node)
        is_retry = False
        is_error = node.get("level") == "ERROR"

        # Method 1: retry by repeated chapter_title (A2A/A2A_mix)
        if chapter_title and chapter_title in chapter_titles_seen:
            is_retry = True

        # Method 2: without chapter_title, infer from failure + large time gap (MCP)
        if not chapter_title and crew_info:
            # Heuristic: if the previous task failed and the gap is large, treat as retry
            prev_info = crew_info[-1]
            time_diff = (dt - prev_info["dt"]).total_seconds()
            # If the gap is large (>60s) and previous failed, this may be a retry
            if time_diff > 60 and prev_info.get("is_error"):
                is_retry = True

        crew_info.append(
            {
                "node": node,
                "dt": dt,
                "chapter_title": chapter_title,
                "is_retry": is_retry,
                "is_error": is_error,
            }
        )

        if chapter_title:
            chapter_titles_seen[chapter_title] = True

    # Batch assignment logic:
    # 1) group by time (starts within 10s belong to the same batch for initial tasks)
    # 2) retry tasks inherit the original chapter's batch number
    # 3) each batch has at most 4 distinct chapters; retries do not count as new chapters
    batch_info = {}
    current_batch = 1
    batch_start_time = None
    batch_chapters = set()  # chapters in the current batch (excluding retries)
    chapter_to_batch = {}  # chapter_title -> batch_no

    for info in crew_info:
        node = info["node"]
        dt = info["dt"]
        chapter_title = info["chapter_title"]
        is_retry = info["is_retry"]

        assigned_batch = current_batch

        if is_retry and chapter_title:
            # Retry: inherit original batch
            if chapter_title in chapter_to_batch:
                assigned_batch = chapter_to_batch[chapter_title]
            # If not found (shouldn't happen), use the current batch
        else:
            # Non-retry: batch by time and capacity
            if batch_start_time is None:
                # First task starts batch 1
                batch_start_time = dt
                batch_chapters = {chapter_title} if chapter_title else set()
            else:
                time_diff = (dt - batch_start_time).total_seconds()

                # Start a new batch if >10s or batch already has 4 chapters
                if time_diff > 10 or len(batch_chapters) >= 4:
                    current_batch += 1
                    batch_start_time = dt
                    batch_chapters = {chapter_title} if chapter_title else set()
                else:
                    # Add to current batch
                    if chapter_title:
                        batch_chapters.add(chapter_title)

            assigned_batch = current_batch

            # Record mapping
            if chapter_title:
                chapter_to_batch[chapter_title] = assigned_batch

        batch_info[node["id"]] = {
            "batch": assigned_batch,
            "is_retry": is_retry,
            "chapter_title": chapter_title,
        }

    return batch_info


def extract_trace_tree(trace_file: str) -> None:
    """Extract and render the trace tree."""

    # Validate file
    if not os.path.exists(trace_file):
        print(f"ERROR: File not found: {trace_file}")
        return

    # Detect project type
    is_a2a_project = detect_a2a_project(trace_file)
    is_a2a_mix = detect_a2a_mix_project(trace_file)
    write_book_project_type = detect_write_book_project(trace_file)
    self_eval_project_type = detect_self_evaluation_project(trace_file)

    # Read JSON
    try:
        with open(trace_file, "r", encoding="utf-8") as f:
            data = json.load(f)
    except json.JSONDecodeError as e:
        print(f"ERROR: JSON parse error: {e}")
        return
    except Exception as e:
        print(f"ERROR: Failed to read file: {e}")
        return

    # Basic info
    trace_id = data.get("id", "N/A")
    timestamp = data.get("timestamp", "N/A")
    observations = data.get("observations", [])

    if not observations:
        print("WARNING: This trace has no observations")
        return

    # Build tree
    tree_lines, error_stats = build_tree_structure(
        observations,
        is_a2a_project,
        is_a2a_mix,
        write_book_project_type,
        self_eval_project_type,
    )

    # Helper: truncate error messages and count truncations (display-stage only)
    def truncate_error_msg(msg: str, max_length: int = 80) -> str:
        """Truncate error message and update truncation counter."""
        if len(msg) > max_length:
            error_stats["truncated_errors_count"] += 1
            return msg[: max_length - 10] + "[TRUNCATED]"
        return msg

    # Build full output (Markdown)
    header_lines = [
        f"# Trace Execution Path",
        f"",
        f"**Trace ID**: `{trace_id}`",
        f"",
        f"**Time**: {timestamp}",
        f"",
    ]

    if is_a2a_project:
        header_lines.append("**Project Type**: A2A (framework noise filtered)")
        header_lines.append(f"")

    if write_book_project_type:
        header_lines.append(
            f"**write_a_book_with_flows Project Type**: {write_book_project_type}"
        )
        header_lines.append(
            "**Batch Annotation**: enabled (concurrent writing batch analysis)"
        )
        header_lines.append(f"")

    if self_eval_project_type:
        header_lines.append(
            f"**self_evaluation_loop_flow Project Type**: {self_eval_project_type}"
        )
        header_lines.append(
            "**RETRY Annotation**: enabled (content_generator and post_reviewer retry detection)"
        )
        header_lines.append(f"")

    # Statistics
    original_count = len(observations)
    filtered_count = len(
        [
            obs
            for obs in observations
            if not should_filter_observation(obs, is_a2a_project)
        ]
    )

    # Tree section
    header_lines.append("## Execution Path Tree")
    header_lines.append(f"")
    header_lines.append(f"```")

    # Tree block (code fenced)
    tree_block = tree_lines

    # Summary
    footer_lines = [
        f"```",
        f"",
        f"## Statistics",
        f"",
    ]

    if is_a2a_project:
        footer_lines.append(f"- **Original observations**: {original_count}")
        footer_lines.append(f"- **Observations after filtering**: {filtered_count}")
        a2a_filtered = original_count - filtered_count - error_stats["http_filtered"]
        footer_lines.append(f"- **Filtered**: {original_count - filtered_count} nodes")
        if error_stats["http_filtered"] > 0:
            footer_lines.append(f"  - A2A framework internals: {a2a_filtered} nodes")
            footer_lines.append(
                f'  - HTTP request nodes: {error_stats["http_filtered"]} nodes'
            )
    else:
        footer_lines.append(f"- **Total observations**: {original_count}")
        if error_stats["http_filtered"] > 0:
            footer_lines.append(
                f'- **Filtered HTTP request nodes**: {error_stats["http_filtered"]} nodes'
            )

    # Error summary
    if error_stats["total_errors"] > 0:
        footer_lines.append(f"")
        footer_lines.append("### Error Summary")
        footer_lines.append(f"")
        footer_lines.append(f'- **Total errors**: {error_stats["total_errors"]}')
        footer_lines.append(f'- **Visible errors**: {error_stats["visible_errors"]}')

        if error_stats["filtered_errors"] > 0:
            footer_lines.append(
                f'- **Filtered errors**: {error_stats["filtered_errors"]}'
            )
            if error_stats["visible_errors"] == 0:
                footer_lines.append(
                    "- **Note**: All errors are inside filtered nodes; the tree does not show error nodes"
                )

        # Truncation stats
        if error_stats["truncated_errors_count"] > 0:
            footer_lines.append(
                f'- **Truncated error messages**: {error_stats["truncated_errors_count"]}'
            )

        if error_stats["filtered_errors"] > 0:
            # Details of errors inside filtered nodes
            footer_lines.append(f"")
            footer_lines.append("#### Errors Inside Filtered Nodes")
            footer_lines.append(f"")

            # A2A framework/internal errors
            if error_stats["a2a_framework_errors"]:
                footer_lines.append(
                    f'**A2A framework/internal errors** ({len(error_stats["a2a_framework_errors"])}):'
                )
                for i, err in enumerate(error_stats["a2a_framework_errors"], 1):
                    msg = truncate_error_msg(err["message"])
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`: {msg}')
                footer_lines.append(f"")

            # HTTP request node errors
            if error_stats["http_request_errors"]:
                footer_lines.append(
                    f'**HTTP request node errors** ({len(error_stats["http_request_errors"])}):'
                )
                for i, err in enumerate(error_stats["http_request_errors"], 1):
                    msg = truncate_error_msg(err["message"])
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`: {msg}')
                footer_lines.append(f"")

            # Tool Usage node errors
            if error_stats["tool_usage_errors"]:
                footer_lines.append(
                    f'**Tool Usage node errors** ({len(error_stats["tool_usage_errors"])}):'
                )
                for i, err in enumerate(error_stats["tool_usage_errors"], 1):
                    msg = truncate_error_msg(err["message"])
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`: {msg}')
                footer_lines.append(f"")

            # Tool child SPAN errors
            if error_stats["tool_child_span_errors"]:
                footer_lines.append(
                    f'**Tool child SPAN errors (A2A_mix only)** ({len(error_stats["tool_child_span_errors"])}):'
                )
                for i, err in enumerate(error_stats["tool_child_span_errors"], 1):
                    msg = truncate_error_msg(err["message"])
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`: {msg}')
                footer_lines.append(f"")

            # Context notes
            if error_stats["filtered_errors_no_parent_error"]:
                footer_lines.append("#### Context Notes")
                footer_lines.append(f"")
                footer_lines.append(
                    f'**Filtered error nodes with no ancestor error** ({len(error_stats["filtered_errors_no_parent_error"])}):'
                )
                # De-duplicate (a node may appear in multiple categories)
                unique_errors = {
                    err["id"]: err
                    for err in error_stats["filtered_errors_no_parent_error"]
                }.values()
                for i, err in enumerate(unique_errors, 1):
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`')
                footer_lines.append(f"")

            if error_stats["filtered_errors_no_child_error"]:
                if not error_stats["filtered_errors_no_parent_error"]:
                    footer_lines.append("#### Context Notes")
                    footer_lines.append(f"")
                footer_lines.append(
                    f'**Filtered error nodes with no descendant error** ({len(error_stats["filtered_errors_no_child_error"])}):'
                )
                # De-duplicate (a node may appear in multiple categories)
                unique_errors = {
                    err["id"]: err
                    for err in error_stats["filtered_errors_no_child_error"]
                }.values()
                for i, err in enumerate(unique_errors, 1):
                    footer_lines.append(f'{i}. `[{err["type"]}] {err["name"]}`')
                footer_lines.append(f"")

        if error_stats["error_messages"]:
            footer_lines.append("**Visible error types**:")
            for i, msg in enumerate(error_stats["error_messages"], 1):
                # Show full messages in the summary (do not truncate)
                if not msg:
                    msg = "Unknown error"
                footer_lines.append(f"{i}. `{msg}`")
            footer_lines.append(f"")

    output_lines = header_lines + tree_block + footer_lines

    # Print to console
    print("\n".join(output_lines))

    # Write Markdown file next to the trace file
    output_file = os.path.join(os.path.dirname(trace_file), "execution_path.md")
    try:
        with open(output_file, "w", encoding="utf-8") as f:
            f.write("\n".join(output_lines))
        print(f"\nSaved: {os.path.basename(output_file)}")
    except Exception as e:
        print(f"\nERROR: Failed to write output file: {e}")


def main():
    """CLI entrypoint."""
    if len(sys.argv) < 2:
        print("Usage: python3 extract_trace_tree.py <trace_file.json>")
        print("\nNotes:")
        print("  - Extracts the tree from langfuse_trace.json")
        print("  - Sorts by timestamp and renders a hierarchy")
        print("  - Writes execution_path.md next to the trace file")
        sys.exit(1)

    trace_file = sys.argv[1]
    extract_trace_tree(trace_file)


if __name__ == "__main__":
    main()