AI Agent Swarm Frameworks (2026) | Multi-Agent Orchestration, Handoffs & Swarms

Swarm-Native Frameworks

6 entries

Toolkits built around swarms of agents, handoffs and decentralized coordination.

01OpenAI SwarmEducational framework from OpenAI exploring lightweight multi-agent orchestration through agents and handoffs; the production successor is the OpenAI Agents SDK. 02SwarmsOpen-source multi-agent orchestration framework with prebuilt swarm architectures such as sequential, concurrent, hierarchical and mixture-of-agents workflows. 03LangGraph SwarmLangGraph library for building swarm-style multi-agent systems where agents dynamically hand control to one another. 04Agency SwarmFramework for building collaborative agencies of specialized agents with defined communication flows, built on the OpenAI Agents SDK. 05Ruflo (formerly Claude-Flow)Open-source agent harness for deploying multi-agent swarms and coordinating autonomous workflows, with memory and Claude Code integration. 06Strands AgentsAWS-backed open-source toolkit for production agents, including multi-agent patterns such as swarms, graphs and agents-as-tools.

Multi-Agent Orchestration Frameworks

12 entries

General frameworks for coordinating teams of agents with roles, handoffs and shared state.

07OpenAI Agents SDKLightweight framework for multi-agent workflows with agents, handoffs, guardrails and tracing. 08LangGraphGraph-based orchestration framework for stateful, controllable multi-agent workflows with persistence and human-in-the-loop. 09CrewAIFramework for orchestrating role-playing autonomous agents into crews, with an enterprise platform for deployment. 10Microsoft Agent FrameworkMicrosoft's framework for building and orchestrating agents and multi-agent workflows, bringing together ideas from AutoGen and Semantic Kernel. 11AutoGenMicrosoft Research framework for multi-agent conversations and event-driven agent systems. 12AG2Community-driven continuation of AutoGen, an open-source multi-agent framework with group chats and swarm-style handoffs. 13Google Agent Development Kit (ADK)Google's open-source framework for building, evaluating and deploying single and multi-agent systems. 14AgnoFramework and runtime for building multi-agent teams with memory, knowledge and tools. 15MetaGPTMulti-agent framework that assigns software-company roles to LLM agents to turn a requirement into code and documents. 16AgentScopeFramework for building, running and observing multi-agent applications with built-in tooling for tracing and deployment. 17LlamaIndexData framework with agent workflows for building multi-agent systems over private data. 18Claude Agent SDKAnthropic's SDK for building agents on the same harness as Claude Code, with subagents for delegating tasks.

Simulation, Research & Distributed Runtimes

4 entries

Platforms for large-scale agent societies, research systems and the compute behind them.

19CAMEL-AIOpen-source community and framework researching the scaling laws of agents and multi-agent role-playing systems. 20OASISOpen Agent Social Interaction Simulations, an open-source simulator that scales social-media style simulations to up to one million agents. 21Magentic-OneMicrosoft Research's generalist multi-agent system in which an orchestrator directs specialized agents to complete complex tasks. 22RayDistributed computing framework often used to scale agent workloads and LLM serving across clusters.

Frequently Asked Questions

What is an AI agent swarm?

An agent swarm is a group of AI agents that cooperate to complete a task, usually by handing work to one another or running in parallel, with little central control. Each agent has a focused role, tools and instructions.

How is a swarm different from a multi-agent framework?

Swarm-style systems emphasize decentralized handoffs, where any agent can pass control to another based on context. General multi-agent frameworks also support central supervisors, fixed pipelines and graphs. Many frameworks, such as LangGraph and AG2, support both.

What happened to OpenAI Swarm?

OpenAI Swarm was released as an educational, experimental framework. OpenAI now recommends its production-ready successor, the OpenAI Agents SDK, which keeps the agents-and-handoffs model.

When should I use multiple agents instead of one?

Use multiple agents when a task has distinct roles, needs parallel work, or has instructions and tools that would overwhelm one agent. Start with a single agent and add more only when it clearly improves quality, since multiple agents add cost and complexity.

How do I keep multi-agent systems safe?

Apply guardrails, least-privilege tool access, tracing and human approval for sensitive actions, and monitor agent-to-agent communication. See the AI Agent Governance and AI Agent Security pages for tools.