🤖🧪 botlab: a collection of AI bots, agents, and teams

A laboratory for experimenting with AI agents and automation tools. Built with Python and modern AI frameworks.

⭐ Features

• 🤖 AI Agents: Modern LLM-powered agents to automate nearly any job
• 🤝 Agent Teams: Teams of agents that collaborate to solve complex problems
• 🧩 Modular Architecture: Plug-and-play components for custom AI solutions
• ✨ Simplicity: Minimal dependencies and easy to understand code
• 📊 Extensive Logging: Built-in monitoring and debugging tools
• 📁 Simple File Management: Agent outputs stored in data/ directories

📚 Projects

This repository contains two main projects:

1. 🧠 Agents (smolagent framework)

The agents/ directory contains a collection of AI agents built with the smolagent framework. These agents can perform tasks like research, writing, and coordination.

# Setup the environment first
./setup_env.sh
source .venv/bin/activate

# Example: Run the researcher agent
python -m agents.researcher.example --query "What are the latest advancements in AI?"

View the full Agents documentation →

Key components:

• ResearcherAgent: Web search and information gathering
• WriterAgent & CriticAgent: Creative writing with feedback
• EditorAgent & FactCheckerAgent: Content editing with fact checking
• ManagerAgent: Coordinates multiple specialized agents
• QAQCAgent: Compares outputs and selects the best one
• AgentLoop: Orchestrates iterative workflows between multiple agents

2. 🐝 Swarms (OpenAI's swarm framework)

The swarms/ directory contains implementations based on OpenAI's swarm framework, allowing for the creation of collaborative agent systems.

# Setup the environment first (if not already done)
./setup_env.sh
source .venv/bin/activate

# Example: Run the writer-critic swarm
python -m swarms.writer-critic.writer-critic

View the full Swarms documentation →

Key features:

• Writer-Critic System: Collaborative writing with feedback loops
• Multi-agent collaboration: Agents working together on complex tasks
• Emergent behavior: Solutions that arise from agent interactions
• Scalable architecture: Add more agents to tackle larger problems

🛠️ Setup

The project uses a single virtual environment at the root directory for all components.

# Setup the environment
./setup_env.sh
source .venv/bin/activate

# Set API keys in the appropriate .env files
# For agents: add GEMINI_API_KEY to agents/.env
# For swarms: add OPENAI_API_KEY to swarms/.env

For detailed instructions, please refer to the INSTALL.md file.

📦 Using Botlab in Other Projects

You can easily use botlab agents in your own projects by adding it as a Git submodule:

# Add botlab as a submodule to your project
cd your-project
git submodule add https://github.com/yourusername/botlab.git
git commit -m "Add botlab as submodule"

# Setup the environment
cd botlab
./setup_env.sh

Then in your Python code:

import os
import sys
from dotenv import load_dotenv

# Add botlab to Python path
sys.path.append("./botlab")

# Import the agent you need
from botlab.agents.researcher.agents import ResearcherAgent

# Setup and use the agent
load_dotenv()
researcher = ResearcherAgent()
result = researcher.run_query("Your query here")

To update the submodule when botlab changes:

git submodule update --remote botlab
git commit -m "Update botlab submodule"

🤝 Contributing

1. Fork and clone the repository
   • git clone https://github.com/yourusername/botlab.git
2. Create a new branch
3. Submit a pull request

📜 License

GNU General Public License v3.0 - See LICENSE

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❤️ Thank you for using botlab! We hope this project helps you harness the awesome power of AI.