This project provides a framework for building AI agents that leverage state machines for structured decision-making and learning from experience. It targets developers building sophisticated LLM-powered applications, offering improved reliability and adaptability over traditional prompt-based agents.

How It Works

Stately Expert combines XState for robust state machine management with concepts from reinforcement learning. Agents transition through defined states based on observations, message history, and feedback. This structured approach allows for custom planning, learning from past decisions via rewards, and generating insights into state changes, leading to more informed and context-aware agent behavior.

Quick Start & Requirements

• Install via npm install --save @statelyai/agent or yarn add @statelyai/agent.
• Requires Node.js.
• Integrates with Vercel AI SDK for LLM providers (OpenAI, Anthropic, Google, Mistral, etc.).
• Documentation: stately.ai/docs/agents

Highlighted Details

• State-machine-driven decision making for predictable agent behavior.
• Learning from experience through observations, feedback, and rewards.
• Flexible LLM support via Vercel AI SDK.
• Custom planning abilities based on state logic.

Maintenance & Community

• Developed by Stately AI.
• Community links not explicitly mentioned in the README.

Licensing & Compatibility

• License type not specified in the README.
• Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project is marked with "TODO" for its Quick Start section, indicating it may be in early development. Specific details on learning algorithms, storage integration, and advanced policy implementations are not fully elaborated.