Show HN: Hephaestus – Autonomous Multi-Agent Orchestration Framework
20 days ago
- #Dynamic Workflows
- #AI Agents
- #Hephaestus
- AI workflows can dynamically write their own instructions as agents discover tasks.
- Hephaestus coordinates multiple AI agents, monitors their trajectories, and builds workflows dynamically.
- Traditional agentic frameworks require predefined instructions for every scenario, limiting adaptability.
- Hephaestus introduces logical phase types (Analysis, Implementation, Validation) allowing agents to spawn tasks in any phase based on discoveries.
- Agents can create new tasks when they find optimizations, security issues, or better architectural patterns, enabling self-branching workflows.
- Example: A validation agent testing an auth system discovers a caching pattern and spawns investigation and implementation tasks.
- Workflows adapt in real-time based on discoveries, not predefined plans.
- Semi-structured approach balances structure (phase definitions, done criteria) with flexibility (dynamic task creation).
- Requirements include Python 3.10+, tmux, Git, Docker, Node.js, npm, and CLI AI tools like Claude Code.
- Setup validation script checks CLI tools, API keys, configuration, and services.
- Documentation and community support available via GitHub, issue tracker, and email.