This project is a Python library designed for building, testing, and deploying autonomous agents that execute complex workflows. It functions as a multi-agent orchestration framework, enabling the creation of systems where specialized agents communicate, delegate tasks, and integrate with external services to complete multi-step automated processes.
The framework distinguishes itself by combining deterministic code execution with adaptive language model reasoning. It utilizes structured graph-based logic and state-machine execution to maintain persistent context across multi-turn interactions, ensuring predictable state transitions throughout an automated process.
The toolkit supports the entire lifecycle of agentic applications, from defining individual agent roles and instructions to orchestrating complex, branching workflows. It includes built-in telemetry and testing tools to measure performance, accuracy, and reliability, facilitating iterative refinement of agent decision-making. These capabilities extend to production environments, allowing for the deployment of scalable systems that maintain consistent performance as task volume increases.
