Research and development laboratory for building autonomous AI agents with advanced reasoning capabilities. Explores multi-agent systems, tool use, planning algorithms, and emergent behaviors. Designed for AI researchers and developers creating sophisticated agent architectures. Includes frameworks for agent communication, task delegation, and collaborative problem-solving. Perfect for experimenting with next-generation AI systems and autonomous workflows.

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Table of Contents

• Table of Contents
• Agentic Lab
• Features
• Installation
• Quick Start
• Agent Types
• Project Structure
• Benefits
• How It Compares
• License

Agentic Lab

An advanced research laboratory for developing autonomous AI agents with sophisticated reasoning, planning, and collaboration capabilities for next-generation AI systems.

Features

• Multi-agent system architectures
• Advanced reasoning and planning algorithms
• Dynamic tool use and adaptation
• Agent-to-agent communication protocols
• Task delegation and workflow management
• Emergent behavior exploration
• Self-improving agent systems

Installation

1. Clone the repository:

git clone https://github.com/harehimself/agentic-lab.git
cd agentic-lab

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment:

cp config/agents.yaml.example config/agents.yaml
# Edit configuration files for your use case

Quick Start

1. Launch a basic autonomous agent:

python src/agents/basic_agent.py --task "analyze data and provide insights"

2. Start multi-agent collaboration:

python experiments/multi_agent_collaboration.py

3. Run agent benchmarks:

python tests/agent_benchmarks.py

Agent Types

The lab includes several specialized agent architectures:

• Research Agent: Information gathering and analysis
• Planning Agent: Strategy development and execution
• Tool Agent: Dynamic tool discovery and utilization
• Communication Agent: Inter-agent coordination
• Reflection Agent: Self-evaluation and improvement
• Swarm Agents: Collective intelligence systems

Project Structure

• src/agents/: Core agent implementations
• src/reasoning/: Reasoning and planning modules
• src/tools/: Tool integration and management
• src/communication/: Agent communication protocols
• experiments/: Research experiments and studies
• benchmarks/: Agent performance evaluation
• config/: Agent and system configurations

Benefits

• Cutting-edge research into autonomous AI systems
• Modular architecture for rapid agent development
• Comprehensive benchmarking and evaluation tools
• Advanced multi-agent coordination capabilities
• Foundation for building production-ready agent systems

How It Compares

• Research-focused approach to agent development
• Emphasis on autonomy and emergent behaviors
• Advanced multi-agent system capabilities
• Designed for complex, real-world problem solving
• Bridges academic research and practical implementation

License

MIT License © 2025 HareLabs