kyegomez/swarms

What it solves

Swarms is a multi-agent orchestration framework designed to build scalable, production-ready systems where multiple AI agents collaborate to solve complex tasks. It removes the need to manually define every interaction by providing pre-built architectures for how agents should communicate and work together.

How it works

The framework treats an Agent (LLM + Tools + Memory) as the basic building block. Users can organize these agents into various Swarms using different orchestration patterns:

• Sequential: Agents work in a linear chain (A $\rightarrow$ B $\rightarrow$ C).
• Concurrent: Agents work on the same task simultaneously for high throughput.
• Graph: Agents are nodes in a Directed Acyclic Graph (DAG), allowing for complex dependencies and automatic parallelism.
• Mixture of Agents (MoA): Multiple expert agents run in parallel, and an aggregator agent synthesizes their results.
• Hierarchical: A director agent plans and distributes tasks to specialized worker agents.
• Dynamic Routing: Tools like AgentRearrange and SwarmRouter allow for flexible mapping of agent relationships or switching between swarm strategies via a single interface.

Additionally, the AutoSwarmBuilder can autonomously generate the necessary agents and prompts based on a high-level task description.

Who it’s for

It is intended for developers building enterprise-grade AI applications that require multi-step reasoning, complex research, or collaborative workflows that exceed the capabilities of a single LLM.

Highlights

• Diverse Architectures: Over 60 multi-agent structures including sequential, concurrent, hierarchical, and graph-based workflows.
• Autonomous Execution: Support for max_loops="auto", allowing agents to decide when a task is complete.
• Auto-Generation: Ability to automatically create specialized agents and detailed prompts using AutoSwarmBuilder.
• Interoperability: Backward compatibility with other agent frameworks and support for protocols like MCP and x402.