A repository dedicated to AI agents, their architectures, implementations, and applications.
AI agents are autonomous or semi-autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. They combine various AI technologies such as:
- Natural Language Processing
- Computer Vision
- Reinforcement Learning
- Planning and Reasoning
- Tool Usage
- Act based on current percepts only
- Ignore history
- Implement condition-action rules
- Maintain internal state to track aspects of the world
- Update state based on percept history
- Choose actions based on current state
- Work towards specific goals
- Consider future actions and their outcomes
- Plan sequences of actions to achieve goals
- Maximize expected utility
- Consider preferences among different goal states
- Balance competing goals
- Improve performance over time through experience
- Adapt to changing environments
- Learn from feedback
- LangChain: Framework for developing applications powered by language models
- AutoGPT: Autonomous GPT-4 based agent that chains together LLM calls
- BabyAGI: Task-driven autonomous agent using LLMs
- AgentGPT: Browser-based autonomous AI agents
- JARVIS: Framework for flexible and modular agent creation
- Virtual assistants
- Autonomous vehicles
- Trading systems
- Game playing agents
- Content creation
- Research and data analysis
Contributions are welcome! Please feel free to submit a Pull Request.