A modular framework for building autonomous agents.
Agent Company is designed to help developers build sophisticated multi-agent systems. The framework leverages Redis for messaging and provides a flexible API to incorporate custom tools and agents.
Key components include:
- Agent
- Driver
- Environment
- Protocol
The following table organizes the environment types, providing a structured view for clarity:
| Category | Subcategory | Description | Example |
|---|---|---|---|
| Observability | Fully Observable | The agent has complete information about the state of the environment at any time, requiring no history tracking. | Chess, where the board and moves are fully visible. |
| Partially Observable | The agent has incomplete information, needing to infer hidden states. | Driving, where road conditions beyond corners are unknown. | |
| Determinism | Deterministic | The next state is completely determined by the current state and action, with no randomness. | Chess, where each move has a definite outcome. |
| Stochastic | The next state involves randomness, not fully predictable by the agent. | Self-driving cars, affected by unpredictable driver behaviors. | |
| Interaction Type | Competitive | Agents compete to optimize their own objectives, often in zero-sum games. | Chess, where players aim to defeat each other. |
| Cooperative | Agents work together to achieve a common goal, coordinating actions. | Multiple self-driving cars cooperating to avoid collisions, as noted in AITUDE Understand Types of Environments. | |
| Number of Agents | Single-agent | Only one agent operates in the environment, focusing on its task. | A person navigating a maze alone. |
| Multi-agent | Multiple agents interact, potentially competing or cooperating. | A game of football with 11 players per team, as per Tpoint Tech Agent Environment. | |
| Environment Dynamics | Static | The environment remains unchanged over time, except by agent actions. | An empty house, where entering doesn't alter surroundings. |
| Dynamic | The environment changes independently of agent actions over time. | A roller coaster ride, constantly in motion, as described in Applied AI Course Types of Environment. | |
| State and Action Space | Discrete | The environment has a finite number of states or actions, countable and distinct. | Chess, with a finite set of possible moves per game. |
| Continuous | The environment has an infinite number of states or actions, not countable. | Self-driving cars, with continuous driving and parking actions. | |
| Task Structure | Episodic | The agent's actions are divided into independent episodes, each with no dependency on previous ones. | A pick-and-place robot inspecting parts on a conveyor belt, deciding per part, as per CSVeda AI Environment Types. |
| Sequential | The agent's actions are interdependent, with previous decisions affecting future ones. | Checkers, where each move impacts subsequent strategies. | |
| Environment Knowledge | Known | The agent knows the complete model, including how actions lead to state transitions. | Chess, with known rules and outcomes for moves. |
| Unknown | The agent does not know the model, requiring learning through interaction. | An agent in an unexplored environment, needing to discover dynamics, as noted in Slideshare Types of Environment in AI. |
- 🚀 Coming Soon: Remote MCP Server Support!
- 🔍 Integrated memory search using Redis
- 🤝 OpenAI-compatible API endpoints
- 📖 Finetuning, Cloud SFT & GRPO training support for base LLMs
Contributions are very welcome! To contribute:
- Fork the Repository:
- Create a Feature Branch:
git checkout -b feature/YourFeature
- Make Your Changes and Commit::
git checkout -b feature/YourFeature
- Push Your Branch:
git push origin feature/YourFeature
- Open a Pull Request:
Please follow our contribution guidelines and code style. For more details, refer to the CONTRIBUTING.md file.
Developed with ❤️ by @aloobhujiyan Thanks to the open-source community for encouraging me to start this. Agent Company is an evolving project—your feedback and contributions are crucial to its growth. Happy coding!