This project is an agentic workflow orchestrator designed for building and deploying autonomous systems that perform multi-step reasoning. It functions as a tool-augmented engine, enabling developers to chain model calls with external function execution to complete complex, user-defined tasks. By integrating large language models with persistent memory and stateful logic, the framework supports the creation of intelligent applications capable of independent operation.
The platform distinguishes itself through graph-based state orchestration, which allows developers to define logic steps and transitions as directed graphs. It provides a unified interface for accessing a wide range of specialized models, including those capable of multimodal processing, automated browser interaction, and deep research. These capabilities are further enhanced by reflection loops, where agents iteratively evaluate and refine their own outputs to improve accuracy before finalizing results.
Beyond core reasoning, the framework provides infrastructure for production-grade AI deployment. It supports the management of persistent state across execution steps and facilitates the use of containerized services to ensure consistent performance. The system also incorporates a multimodal embedding space to enable semantic search and retrieval across diverse data types, including text, images, and audio.
The repository provides a quickstart environment that allows developers to execute research agents directly from the command line for rapid testing and iteration.
