agent-pard0x/simple-react-agent

Simple ReAct Agent for Log Analysis

This directory contains a simple ReAct (Reasoning and Acting) agent implementation for log analysis and system administration tasks.

Overview

The simple ReAct agent follows a straightforward pattern:

1. Receives user input
2. Reasons about what tools to use
3. Acts by executing tools when needed
4. Responds with the final result

Features

• Single Agent: One agent handles all tasks
• Shell Access: Execute system commands safely
• Log Analysis: Specialized log analysis capabilities
• Interactive Chat: Stream responses with tool usage visibility
• Conversation History: Maintains context across interactions

Architecture

User Input → ReAct Agent → Tools (Shell + Log Analyzer) → Response

Files

• main.py: Main application with ReAct agent implementation
• log_analyzer.py: Specialized tool for analyzing log files
• loghub/: Symbolic link to log files directory

Tools Available

1. Shell Tool: Execute system commands

   • System monitoring (top, ps, df, etc.)
   • File operations
   • Network diagnostics

2. Log Analyzer Tool: Analyze log files with different modes:

   • error_patterns: Find and categorize error messages
   • frequency: Analyze frequency of different log patterns
   • timeline: Show chronological patterns of events
   • summary: Provide an overall summary of the log file

Usage

cd simple-react-agent
python main.py

Example Interactions

User: Analyze the Apache logs for error patterns
Agent: 🔧 Using tool: analyze_log_file
       Args: {'file_path': 'Apache/Apache_2k.log', 'analysis_type': 'error_patterns'}
       📋 Tool result: Found 15 error patterns in Apache logs...

User: Check disk usage on the system
Agent: 🔧 Using tool: shell
       Args: {'command': 'df -h'}
       📋 Tool result: Filesystem usage information...

Pros and Cons

✅ Pros

• Simple to understand: Single agent, clear flow
• Easy to debug: Linear execution path
• Quick setup: Minimal configuration required
• Resource efficient: Lower computational overhead
• Good for: Simple tasks, learning, rapid prototyping

❌ Cons

• Limited specialization: One agent handles everything
• No parallel processing: Sequential tool execution
• Scaling challenges: Complex tasks may overwhelm single agent
• Less sophisticated: No coordination between specialized experts

When to Use

Choose the simple ReAct agent when:

• You need a straightforward log analysis tool
• Your use cases are relatively simple
• You want to understand LangGraph basics
• Resource usage is a concern
• You prefer simplicity over sophistication

Requirements

pip install langchain-openai langgraph langchain-community
export OPENAI_API_KEY="your-api-key"