# Extract Trace Tree

Langfuse Trace tree extraction and analysis tool

## Overview

This tool extracts an execution-path tree from a Langfuse trace JSON file (typically `langfuse_trace.json`).
It builds a parent/child tree, sorts siblings by timestamp, applies noise filtering (A2A + HTTP), and outputs
both a console view and a Markdown report.

## Quick Start

### Single file

```bash
python3 extract_trace_tree.py <trace_file.json>
```

Examples:

```bash
python3 extract_trace_tree.py GPT-5/BookWriter-MCP/test_results/run_20251104_045654/langfuse_trace.json
python3 extract_trace_tree.py GPT-5/BookWriter-A2A/test_results/run_20251103_195358/langfuse_trace.json
python3 extract_trace_tree.py GPT-5/BookWriter-H_A2A/test_results/run_20251104_010743/langfuse_trace.json
```

### Batch

```bash
./batch_extract_trees.sh
```

## Output

The script writes `execution_path.md` next to the input trace file. The Markdown contains:

- `# Trace Execution Path` header with metadata
- `## Execution Path Tree` (tree in a fenced code block)
- `## Statistics`
- `### Error Summary` (only when errors exist)

### Token format

LLM nodes show:

```
(prompt→completion [REASONING:X, OUTPUT:Y], total: Z)
```

Where:

- `REASONING` is the reasoning-token count (0 for non-reasoning models)
- `OUTPUT` is `completionTokens - reasoningTokens`

### Batch label (write_a_book_with_flows)

For write-book scenarios the tree may include:

- `BATCH1` / `BATCH2`
- `[BUSINESS-RETRY]` when the same chapter is retried

### Truncated errors

Long errors are truncated and marked with `[TRUNCATED]`.

## Project Type Detection

Project type is detected from the trace path:

- `*-MCP/` -> MCP
- `*-A2A/` -> A2A
- `*-A2A_mix/` -> A2A_mix
- `*-H_A2A/` -> A2A

## Filtering

- HTTP client tracing nodes (OpenTelemetry httpx) are filtered.
- A2A framework internals are filtered for A2A/A2A_mix.
- Tool telemetry spans such as `Tool Usage`/`Tool Repeated Usage` are filtered.

## Requirements

- Python 3
- Standard library only (no third-party dependencies)