Multi-Agent Debugger

A powerful Python package that uses multiple AI agents to debug API failures by analyzing logs, code, and user questions. Built with CrewAI, it supports LLM providers including OpenAI, Anthropic, Google, Ollama, and more.

🎥 Demo Video

Watch the multiagent-debugger in action:

🏗️ Architecture

The Multi-Agent Debugger uses a sophisticated architecture that combines multiple specialized AI agents working together to analyze and debug API failures.

Core Agent Flow

Detailed Architecture

✨ Features

🤖 Multi-Agent Architecture

• Question Analyzer Agent: Extracts key entities from natural language questions and classifies error types
• Log Analyzer Agent: Searches and filters logs for relevant information, extracts stack traces
• Code Path Analyzer Agent: Validates and analyzes code paths found in logs
• Code Analyzer Agent: Finds API handlers, dependencies, and error handling code
• Root Cause Agent: Synthesizes findings to determine failure causes and generates visual flowcharts

🔧 Comprehensive Analysis Tools

• Log Analysis: Enhanced grep, filtering, stack trace extraction, and error pattern analysis
• Code Analysis: API handler discovery, dependency mapping, error handler identification, multi-language support
• Flowchart Generation: Error flow, system architecture, decision trees, sequence diagrams, and debugging storyboards
• Natural Language Processing: Convert user questions into structured queries

🌐 Multi-Provider LLM Support

• OpenAI
• Anthropic
• Google
• Ollama
• Azure OpenAI
• AWS Bedrock
• And 50+ more providers

Features

• Visual Flowcharts: Mermaid diagrams for error propagation and system architecture
• Copyable Output: Clean, copyable flowchart code for easy sharing
• Multi-language Support: Python, JavaScript, Java, Go, Rust, and more

📊 Output Formats

• Structured JSON: Programmatic access to analysis results
• Text Documents: Human-readable reports saved to local files
• Visual Flowcharts: Mermaid diagrams for documentation and sharing

🚀 Installation

# From PyPI
pip install multiagent-debugger

# From source
git clone https://github.com/VishApp/multiagent-debugger.git
cd multiagent-debugger
pip install -e .

⚡ Quick Start

1. Set up your configuration:

multiagent-debugger setup

2. Debug an API failure:

multiagent-debugger debug "Why did my /api/users endpoint fail yesterday?"

3. View generated files:

• Analysis results in JSON format
• Text documents in current directory
• Visual flowcharts for documentation

🖥️ Command-Line Usage

Debug Command

Usage: multiagent_debugger debug [OPTIONS] QUESTION

  Debug an API failure or error scenario with multi-agent assistance.

Arguments:
  QUESTION    The natural language question or debugging prompt.
              Example: 'find the common errors and the root-cause'

Options:
  -c, --config PATH             Path to config file (YAML)
  -v, --verbose                 Enable verbose output for detailed logs
  --mode [frequent|latest|all]  Log analysis mode:
                                  frequent: Find most common error patterns
                                  latest:   Focus on most recent errors
                                  all:      Analyze all available log lines
  --time-window-hours INT       Time window (hours) for log analysis
  --max-lines INT               Maximum log lines to analyze
  --code-path PATH              Path to source code directory/file for analysis
  -h, --help                    Show this message and exit

Examples:
  multiagent-debugger debug 'find the common errors and the root-cause' \
      --config ~/.config/multiagent-debugger/config.yaml --mode latest

  multiagent-debugger debug 'why did the upload to S3 fail?' \
      --mode frequent --time-window-hours 12 \
      --code-path /Users/myname/myproject/src

  multiagent-debugger debug 'analyze recent errors' \
      --code-path /path/to/specific/file.py

This command analyzes your logs, extracts error patterns and code paths, and provides root cause analysis with actionable solutions and flowcharts.

⚙️ Configuration

Create a config.yaml file (or use the setup command):

# Paths to log files
log_paths:
  - "/var/log/myapp/app.log"
  - "/var/log/nginx/access.log"

# Path to source code directory or file for analysis (SECURITY FEATURE)
code_path: "/path/to/your/source/code"  # Restricts code analysis to this path only

# Log analysis options
analysis_mode: "frequent"   # frequent, latest, all
time_window_hours: 24      # analyze logs from last N hours
max_lines: 10000           # maximum log lines to analyze

# LLM configuration
llm:
  provider: openai  # or anthropic, google, ollama, etc.
  model_name: gpt-4
  temperature: 0.1
  #api_key: optional, can use environment variable

# Phoenix monitoring configuration (optional)
phoenix:
  enabled: true                                    # Enable/disable Phoenix monitoring
  host: "localhost"                               # Phoenix host
  port: 6006                                      # Phoenix dashboard port
  endpoint: "http://localhost:6006/v1/traces"     # OTLP endpoint for traces
  launch_phoenix: true                            # Launch Phoenix app locally
  headers: {}                                     # Additional headers for OTLP

Code Path Security

The code_path configuration is a security feature that restricts code analysis to a specific directory or file:

# Security: Only analyze code within this path
code_path: "/Users/myname/myproject/src"

How it works:

• When logs contain file paths (from stack traces, errors), the system validates them against code_path
• Files outside the configured path are rejected and not analyzed
• This prevents the system from analyzing sensitive system files or unrelated codebases
• Can be a directory (analyzes all source files within) or a specific file

Use cases:

• Multi-project environments: Restrict analysis to current project only
• Security: Prevent analysis of system files or sensitive directories
• Focus: Analyze only specific parts of large codebases

CLI override:

# Override config file code_path for this session
multiagent-debugger debug "question" --code-path /path/to/specific/project

Custom Providers

The system supports various LLM providers including OpenRouter, Anthropic, Google, and others. See Custom Providers Guide for detailed configuration instructions.

Environment Variables

Set the appropriate environment variable for your chosen provider:

• OpenAI: OPENAI_API_KEY
• Anthropic: ANTHROPIC_API_KEY
• Google: GOOGLE_API_KEY
• Azure: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT
• AWS: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION
• See documentation for other providers

🔍 How It Works

1. Question Analysis

• Extracts key information like API routes, timestamps, and error types
• Classifies the error type (API, Database, File, Network, etc.)
• Structures the query for other agents

2. Log Analysis

• Searches through specified log files using enhanced grep
• Filters relevant log entries by time and pattern
• Extracts stack traces and error patterns
• Dynamically extracts code paths (file paths, line numbers, function names)
• Validates code paths found in logs

3. Code Analysis

• Validates that extracted file paths are within the configured code_path (security)
• Locates relevant API handlers and endpoints
• Identifies dependencies and error handlers
• Maps the code structure and relationships
• Supports multiple programming languages (Python, JavaScript, Java, Go, Rust, etc.)
• Rejects analysis of files outside the configured code path

4. Root Cause Analysis

• Synthesizes information from all previous agents
• Determines the most likely cause with confidence levels
• Generates creative narratives and metaphors
• Creates visual flowcharts for documentation

5. Output Generation

• Structured JSON for programmatic access
• Human-readable text documents
• Visual flowcharts in Mermaid format
• Copyable flowchart code for easy sharing

📊 Phoenix Monitoring

The debugger includes built-in Phoenix monitoring for tracking agent execution, LLM usage, and performance metrics.

View Monitoring Status

multiagent-debugger phoenix

This shows your Phoenix configuration and provides instructions for accessing the dashboard.

Remote Server Access

When running the debugger on a remote server, use SSH port forwarding to access the Phoenix dashboard:

# On your local machine, create SSH tunnel
ssh -L 6006:localhost:6006 user@your-server

# Then visit in your local browser
http://localhost:6006

Configuration

Phoenix monitoring is configured in your config.yaml:

phoenix:
  enabled: true
  host: localhost
  port: 6006
  launch_phoenix: true

Features

• Real-time Monitoring: Track agent executions as they happen
• LLM Usage Tracking: Monitor token usage and costs across providers
• Performance Metrics: Analyze execution times and success rates
• Visual Traces: See the complete flow of agent interactions
• Automatic Launch: Starts automatically when you run debug commands

🛠️ Advanced Usage

List Available Providers

multiagent-debugger list-providers

List Models for a Provider

multiagent-debugger list-models openai

Debug with Custom Config

multiagent-debugger debug "Question?" --config path/to/config.yaml

Analyze Recent Errors Only

multiagent-debugger debug "What went wrong?" --mode latest --time-window-hours 2

Analyze Large Log Files

multiagent-debugger debug "Find patterns" --max-lines 50000

Restrict Code Analysis to Specific Path

# Only analyze code within /path/to/project directory
multiagent-debugger debug "What caused the error?" --code-path /path/to/project

# Analyze only a specific file
multiagent-debugger debug "Debug this file" --code-path /path/to/file.py

🧪 Development

# Create virtual environment
python package_builder.py venv

# Install development dependencies
python package_builder.py install

# Run tests
python package_builder.py test

# Build distribution
python package_builder.py dist

📋 Requirements

• Python: 3.8+
• Dependencies:
  • crewai>=0.28.0
  • pydantic>=2.0.0
  • And others (see requirements.txt)

📄 License

MIT License - see LICENSE for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

🆘 Support

• GitHub Issues: Report a bug
• Documentation: Read more

🎯 Use Cases

• API Debugging: Quickly identify why API endpoints are failing
• Production Issues: Analyze logs and code to find root causes
• Error Investigation: Understand complex error chains and dependencies
• Documentation: Generate visual flowcharts for error propagation
• Team Collaboration: Share analysis results in multiple formats
• Multi-language Projects: Support for Python, JavaScript, Java, Go, Rust, and more
• Time-based Analysis: Focus on recent errors or specific time periods
• Large Log Analysis: Handle massive log files with configurable limits