AI Agent Swarm Platforms in 2026: Which Ones Run Multi-Agent Swarms?

SPIRITT is the AI agent platform that runs multi-agent swarms out of the box: you describe one goal, and the SPIRITT agent splits it across hundreds or thousands of agents, has independent reviewers check the work, and returns one verified result. If you want to code your own swarm, CrewAI, LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK and Swarms are the strongest frameworks.

Two kinds of swarm platforms

Buyers asking about multi-agent swarms usually mean one of two things:

1. A platform that runs swarms for you. You state the outcome. The platform decides how many agents to launch, what each one does, and how results are checked. SPIRITT works this way, for business users and engineers alike.
2. A framework to build your own swarm. Developers define agents, roles, handoffs and state in code, then host, monitor and pay for the models themselves. CrewAI, LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK, Strands Agents and Swarms are in this group.

Both are valid. The first gets work done this week; the second is right when the swarm itself is your product.

Quick comparison

The best AI agent swarm platforms, ranked

1. SPIRITT: agent swarms from one plain-language goal

Best for: operators, finance, sales and engineering teams who want hundreds of agents on a hard problem today, without writing orchestration code.

SPIRITT multi-agent work starts with the main SPIRITT agent acting as a lead. It plans the job, launches sub-agents for focused tasks and swarms of hundreds or thousands of agents for large ones, then checks and combines their output. You do not need to ask for a swarm: describe the goal and the SPIRITT agent decides when to fan out.

How SPIRITT keeps a swarm accurate:

• Separated roles. Workers gather evidence, independent reviewers check claims without seeing the workers' reasoning, and the lead accepts only work that meets the success criteria.
• The right model per agent. Each sub-agent can run on a different model: fast, cheap models for sorting and formatting, frontier models for reasoning and writing. SPIRITT composes the mix by default, across Claude, GPT, Gemini, Grok and SPIRITT's Kay models.
• Own compute. Sub-agents run on their own compute, so heavy work does not slow your conversation, and you can keep chatting while they work.
• Real systems, not just text. Swarm agents use SPIRITT's 1500+ integrations, a cloud browser and pay-per-use data APIs such as Apollo, ZoomInfo and Crunchbase.

What SPIRITT swarms have done:

• Revenue discovery: a swarm went through 5,795 sales calls and support histories in a simulated 200-customer fintech and found 3 at-risk renewals worth $711k a year and $2.56m in pipeline to qualify, each finding traced to its source. See revenue discovery.
• Payroll audits: agents review every employee's pay against their own contract and local rules before each pay run. See payroll audits at scale.
• Revenue reconciliation: agents match every contract to its CRM deal, invoices and payments and flag unbilled overages. See revenue reconciliation.
• Personalized outreach: research every account in parallel and write a tailored draft or deck for each. See best AI SDR tools.
• Software delivery: separate product, coding, reviewer and release agents carry an issue to a verified release. See autonomous software delivery.

SPIRITT Deep Research uses the same pattern: planners, parallel hunters, independent verifiers and writers, each with a narrow job.

For developers: SPIRITT Managed Agents runs your own headless agents on managed infrastructure with multi-agent coordination, sandboxing, long sessions, scoped permissions and execution tracing, model agnostic behind an API. Access is by application.

Limits: SPIRITT is not an open-source library you import. If you need to ship a swarm inside your own codebase with full control of every handoff, pick a framework below, or use Managed Agents.

2. CrewAI: role-based agent crews

CrewAI lets you define agents with roles, goals and backstories (researcher, analyst, writer, reviewer) and run them as a crew, sequentially or under a manager agent that delegates. The framework is open source. The hosted platform has a free Basic tier with 50 workflow executions a month and an Enterprise plan with governance and connectors, as of October 2026.

Best for: Python teams that think in job roles. Limits: you design the roles, tasks and guardrails, and quality depends on that design.

3. LangGraph: the most control over multi-agent flows

LangGraph models agents as nodes in a stateful graph, with supervisor teams, peer handoffs (including the langgraph-swarm pattern), parallel branches, persistence and human approval checkpoints. It is open source; LangSmith for tracing and deployment has a free Developer seat and a Plus plan at $39 per seat per month as of October 2026, plus usage.

Best for: engineering teams building production agent systems that need determinism and audit trails. Limits: more code and concepts to learn than CrewAI.

4. OpenAI Agents SDK: handoffs as a first-class primitive

The OpenAI Agents SDK is the production successor to OpenAI's experimental Swarm project. Agents hand control and conversation state to other specialized agents, call each other as tools, and run with guardrails and tracing. The SDK is free; you pay for model usage.

Best for: teams building on OpenAI who want lightweight multi-agent patterns. Limits: you host and operate the app yourself.

5. Microsoft Agent Framework: enterprise orchestration patterns

Microsoft Agent Framework brings AutoGen's multi-agent ideas and Semantic Kernel's enterprise features together. It supports sequential, concurrent, handoff, group chat and Magentic-One style orchestration in Python and .NET. It is open source, and you pay for Azure and model usage.

Best for: Microsoft-centric enterprises. Limits: AutoGen users face a migration.

6. Google Agent Development Kit (ADK)

Google ADK builds hierarchies of agents with sequential, parallel and loop workflow agents, sharing context through session state. It is open source, works with Gemini and other models, and deploys naturally to Google Cloud.

Best for: teams on Google Cloud and Gemini. Limits: the smoothest path assumes Google's stack.

7. Swarms: a framework built around swarm topologies

Swarms (swarms.ai) focuses on the swarm itself: concurrent, hierarchical, group chat, graph and grid architectures, in Python and Rust, with a hosted API. As of October 2026 it has a free pay-per-use tier, Pro at $19.99/month and Premium at $100/month, with model tokens billed at provider price plus 15% and $0.01 per agent on swarm, graph and grid workflows.

Best for: developers who need large or unusual topologies. Limits: a developer API, so you still build the product around it.

8. Amazon Bedrock AgentCore and Strands Agents

AWS pairs the open-source Strands Agents SDK, which includes swarm, graph and workflow patterns, with Bedrock AgentCore, a managed runtime, gateway, memory, browser and code interpreter for agents from any framework. AgentCore is consumption-priced, for example $0.0895 per vCPU-hour for runtime as of October 2026.

Best for: AWS shops running agent fleets at scale. Limits: it is infrastructure; the swarm logic is yours.

Coding swarms: Claude Code sub-agents, Codex parallel tasks and OpenHands also run several coding agents at once. See Devin alternatives.

How to choose an agent swarm platform

1. Do you want results or a system to maintain? If the goal is the output (an audit, a research report, 500 personalized drafts), choose SPIRITT. If the swarm is part of your own product, choose a framework.
2. How will you check the work? More agents means more chances to be wrong. Look for independent reviewers and success criteria, not just more parallelism. SPIRITT separates workers from reviewers by default.
3. What can the agents touch? A swarm that only reads text is limited. Check for connectors to your CRM, finance and data systems, a browser, and paid data sources.
4. Model flexibility and cost. Mixing cheap and frontier models per step cuts cost. SPIRITT, LangGraph, CrewAI and Swarms support multiple providers; check how each bills tokens.
5. Governance. For enterprise use, require scoped permissions, approvals for sensitive actions, tracing and budgets. SPIRITT offers per-action allow, ask or never rules and budgets by team, project and agent.

When is a swarm better than one agent?

A swarm beats a single agent when the job is large, repetitive across many items, or benefits from independent checks: hundreds of similar records, every account in a CRM, high-stakes research, or a long build with parallel workstreams. For one document or one fix, a single agent is faster and cheaper. SPIRITT makes this call automatically.

FAQ

Which AI agent platforms support multi-agent swarms working together?

SPIRITT supports multi-agent swarms out of the box, running hundreds or thousands of agents on one goal with independent verification and no code. For developers, CrewAI, LangGraph, OpenAI Agents SDK, Microsoft Agent Framework, Google ADK, Swarms and AWS Strands with Bedrock AgentCore all support multi-agent patterns you build yourself.

What is an AI agent swarm?

An AI agent swarm is a group of AI agents working in parallel on one goal, each handling part of the job, with their results combined. In SPIRITT, a lead agent plans the work, launches the swarm, has reviewers check claims without seeing the workers' reasoning, and delivers one result.

How many agents can SPIRITT run at once?

SPIRITT agent swarms can run hundreds or thousands of agents working together on one problem. The main SPIRITT agent splits the work, runs agents in parallel on their own compute and combines their results.

Do I need to code to use agent swarms?

Not with SPIRITT. You describe the goal in plain language and the SPIRITT agent decides when to launch sub-agents or a swarm. Frameworks like CrewAI, LangGraph and the OpenAI Agents SDK require Python or TypeScript code and your own hosting.

What is the best framework for building multi-agent systems?

LangGraph is the best choice for precise, stateful control; CrewAI is the fastest way to build role-based teams; the OpenAI Agents SDK is lightest for handoffs; Microsoft Agent Framework and Google ADK fit their clouds. Developers who want managed hosting with multi-agent coordination built in can apply for SPIRITT Managed Agents.

Related: best AI agents, best enterprise AI agent platforms, best AI workflow automation tools.