A semi-structured agentic framework where agents discover what needs to be done and dynamically assemble workflows rather than relying on predefined step lists.
Detailed Introduction
Hephaestus is a semi-structured agentic framework that emphasizes letting agents discover work at runtime instead of predicting every step upfront. By modeling tasks as discoverable, composable units, the framework can dynamically generate workflows and coordinate multi-agent collaboration in complex, open-ended problem domains, enabling systems with exploratory and adaptive behavior.
Main Features
- Agents discover tasks and dynamically assemble workflows, reducing upfront modeling effort.
- Support for multi-agent collaboration and task distribution, enabling hybrid parallel and sequential execution strategies.
- Open-source implementation and examples for reuse and extension by the community.
- Engineering-oriented design with observability hooks for debugging and behavior analysis.
Use Cases
- Open-ended problem solving systems such as research assistants, automated investigation pipelines, or complex task orchestration.
- Business scenarios requiring agentic exploration and adaptive decision-making, like intelligent process automation.
- Rapid prototyping and proof-of-concept work to evaluate multi-agent collaboration on semi-structured tasks.
Technical Features
- Semi-structured workflow representation: discoverable task units as the core primitive for dynamic composition.
- Python implementation with examples and tooling for easy integration into existing Python stacks.
- Integrations with mainstream LLM providers to enhance agent understanding and decision-making.