kyegomez/swarms

What it solves

Swarms provides a scalable, production-ready framework for orchestrating multiple AI agents to work together. It eliminates the need to manually build complex interaction patterns by providing pre-built architectures for sequential, parallel, and hierarchical collaboration, allowing developers to move from single-agent bots to complex multi-agent systems.

How it works

At its core, the framework uses an Agent building block (LLM + Tools + Memory). These agents are then organized into various "swarms" or workflows:

• Sequential: Agents work in a linear chain where one's output is the next one's input.
• Concurrent: Multiple agents execute tasks simultaneously for high throughput.
• Graph-based: Agents are nodes in a Directed Acyclic Graph (DAG), ensuring dependencies are met while running independent branches in parallel.
• Hierarchical: A director agent plans and distributes tasks to specialized worker agents.
• MCP Integration: It supports the Model Context Protocol, allowing agents to connect to external tools via URLs and enabling agents to be served as MCP servers themselves via MCPDeployer.

Who it’s for

It is designed for developers and enterprises building complex AI applications that require multi-step research, collaborative decision-making, or high-throughput data processing pipelines.

Highlights

• Diverse Architectures: Includes over 60 multi-agent structures, including Mixture of Agents (MoA), GroupChat, and ForestSwarm.
• Autonomous Mode: Supports max_loops="auto", allowing agents to decide when a task is complete rather than following a fixed iteration cap.
• Interoperability: Compatible with MCP, x402, and other leading agent frameworks.
• Performance Optimization: Offers an optional swarms[fast] installation via RouteHub to reduce model call latency.