Arc is a professional agentic framework designed for building complex AI agent systems with advanced orchestration, reinforcement learning, and multi-agent reasoning — all with minimal code.
It enables developers to create hierarchical, intelligent agent workflows using a coordinator–planner–supervisor–team architecture, providing clear modularity and scalability for production systems.
With under 20 lines of code, you can design and run sophisticated multi-agent workflows capable of reasoning, coordination, and adaptive tool use.
🧩 Core Highlights
- 🧠 Hierarchical Architecture — Built-in coordinator–planner–supervisor design for intelligent routing and multi-level agent collaboration.
- ⚙️ Multiple Workflow Patterns — Includes sequential, concurrent, swarm, and hierarchical orchestration patterns optimized for various tasks.
- 🎯 Reinforcement Learning Integration — Agents learn optimal tool usage over time using Q-learning and semantic state matching.
- 🧠 Advanced Reasoning Patterns — Supports self-consistency, reflexion, reasoning duo, and evaluation-based agent systems.
- 🌐 MCP Integration — Native Model Context Protocol support for connecting to external servers and real-world tools.
- 🛡️ Production Ready — Includes caching, error handling, validation, visualization, monitoring, and retry logic.
🚀 When to Use Arc
Use Arc when building:
- Multi-agent systems requiring hierarchical coordination
- Complex workflows involving specialized agents and tools
- Applications needing adaptive reasoning and continuous learning
- Enterprise-grade or research-scale agent orchestration
💡 Example Use Cases
- Research assistants combining multiple expert agents
- Adaptive tool orchestration with reinforcement learning
- Automated reasoning, planning, and evaluation pipelines
- Scalable agent teams for enterprise or academic systems