Claude Code Workflow Analysis

Comprehensive tools for analyzing, tracking, and visualizing Claude Code agent workflows.

Overview

This project provides a complete suite of tools to:

• Extract entities from Claude Code request logs (API requests, messages, tools, agents)
• Track agent instances across multiple requests and conversation turns
• Build workflow DAGs showing agent relationships and tool dependencies
• Visualize workflows with interactive HTML viewer
• Deduplicate entities to reduce redundancy and improve analysis

Features

Entity Extraction

• Parse JSONL request logs from Claude Code proxy
• Extract all entities: requests, responses, messages, content blocks, tools, tasks, agents
• Deduplicate entities while preserving references
• Export to structured JSON format

Agent Instance Tracking

• Identify unique agent instances across requests
• Track conversation continuations
• Link spawned subagents to parent agents
• Compute conversation fingerprints
• Track tool usage per agent

Workflow DAG

• Build directed acyclic graph of agent interactions
• Track spawn edges (parent → child agents)
• Track tool result edges (tool use → result)
• Compute workflow metrics (depth, branching, etc.)
• Identify root and leaf agents

Interactive Visualization

• React-based viewer: Real-time log browsing with multiple panels
• Timeline panel: Chronological request visualization
• Statistics panel: Overview of workflow metrics
• Workflow graph: D3.js force-directed graph with zoom/pan
• Agent Gantt chart: Timeline view with spawn arrows showing parent-child relationships
• Request-level nodes: Each node = one API request, click to inspect

Screenshots

Quick Start

1. Extract Entities

python3 -m analysis.extract_all_entities \
    proxy/logs/requests_20260110.jsonl \
    -o proxy/logs/requests_20260110.json

2. Start the Viewer

# Start the log API server
cd proxy
uv run python log_api.py

# In another terminal, start the React viewer
cd proxy/viewer
pnpm dev

Open http://localhost:58735 in a browser.

Project Structure

.
├── analysis/                    # Entity extraction and tracking
│   ├── agent_tracker.py         # Agent instance tracking
│   ├── entity_deduplicator.py   # Entity deduplication
│   └── extract_all_entities.py  # Main extraction script
│
├── proxy/                       # Claude Code proxy server
│   ├── proxy_server.py          # HTTP proxy for logging
│   ├── log_api.py               # Log file API server
│   ├── log_classifier.py        # Log entry enrichment
│   ├── workflow_graph.py        # Workflow DAG construction
│   └── viewer/                  # React-based visualization
│       ├── src/App.jsx          # Main viewer application
│       ├── src/WorkflowPanel.jsx    # D3.js workflow graph
│       ├── src/AgentGanttPanel.jsx  # Agent Gantt chart
│       └── workflow_tree_viz.html   # Standalone HTML viewer
│
├── scripts/                     # Utility scripts
│   ├── analyze_system_prompts.py  # System prompt analysis
│   └── extract_all_tools.py       # Tool definition extraction
│
├── docs/                        # Documentation
│   ├── AGENT_WORKFLOW_TRACKING.md  # Technical documentation
│   └── LOG_ANALYSIS.md             # Log structure analysis
│
└── DevLog/                      # Development logs

Documentation

• Agent Workflow Tracking: Complete technical documentation
• Log Analysis: Log structure and entity extraction details

Usage Examples

Extract and Visualize

# Extract entities from logs
python3 -m analysis.extract_all_entities \
    proxy/logs/requests.jsonl \
    -o proxy/logs/entities.json

# Start API server and viewer
cd proxy && uv run python log_api.py &
cd proxy/viewer && pnpm dev

Analyze System Prompts

python3 scripts/analyze_system_prompts.py proxy/logs/requests.jsonl

Run Proxy Server

cd proxy
source .venv/bin/activate
python proxy_server.py

Then configure Claude Code to use the proxy:

export ANTHROPIC_API_URL=http://127.0.0.1:58734

or create a settings file for Claude Code (see .claude/settings.json.example for an example):

{
    "env": {
        "ANTHROPIC_BASE_URL": "http://127.0.0.1:58734"
    }
}

Key Concepts

Agent Instance

A unique conversation with Claude, identified by:

• Conversation fingerprint (hash of message sequence)
• System prompt hash (agent type)
• Message count and timestamps

Workflow DAG

A directed acyclic graph showing:

• Nodes: Agent instances with metadata
• Edges: Spawn relationships and tool dependencies
• Metrics: Depth, branching factor, tool usage

Request-Level Visualization

Each node represents a single API request:

• Label: agent_4 [2/3] - 7:45:06 PM
• Click to view full conversation
• Sequential edges (blue) and spawn edges (grey)
• merge back edges (purple)

Statistics Example

ENTITY EXTRACTION SUMMARY
Entity Counts:
  api_requests        :   263
  messages            :  1686
  tool_uses           :  2350
  tool_results        :  2074

AGENT TRACKING
  Total Agent Instances: 129
  Root Agents: 82
  Child Agents (spawned): 47
  Avg Requests/Agent: 2.04

DEDUPLICATION
  Total Unique Entities: 193
  Total Occurrences: 2412
  Duplication Ratio: 12.5x
  Duplicates Removed: 2219

Requirements

• Python 3.10+
• Flask, requests, python-dotenv (for proxy server)
• Node.js and pnpm (for React viewer)
• Modern web browser for visualization

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests and documentation
5. Submit a pull request

License

MIT

Acknowledgments

Built for analyzing Claude Code workflows and understanding agentic AI behavior.

This is for educational purpose.