The Most Widely Used Multi-Agent Systems

Snapshot: September 4, 2026. Adoption figures were collected live from PyPI Stats, GitHub, and vendor sites on that date; download counts include CI and mirror traffic, so treat them as relative signals rather than head counts of developers.

The short answer

In 2026 the multi-agent field has consolidated around a handful of production frameworks. By every adoption signal (package downloads, GitHub activity, enterprise case studies), the systems people actually use today are:

  1. LangGraph (LangChain) — the de-facto standard orchestrator for stateful, graph-based multi-agent systems
  2. AWS Strands Agents — Amazon's model-driven SDK, the fastest riser of the year
  3. OpenAI Agents SDK — minimal handoff-based multi-agent workflows (successor to Swarm)
  4. Claude Agent SDK (Anthropic) — Claude Code's agent loop as a library, with first-class subagents
  5. CrewAI — role-based "crews" of agents; the strongest independent player in the enterprise
  6. Google ADK — Google's Agent Development Kit, now in five languages with graph workflows in 2.0
  7. Microsoft Agent Framework — the unified successor to AutoGen and Semantic Kernel (1.0 GA April 2026)

Two older names you may know have been retired: AutoGen (60.8k stars) is in maintenance mode, and OpenAI Swarm was an educational project replaced by the Agents SDK. Both point their users at their successors.

Adoption at a glance: PyPI downloads per month

Python package downloads, last 30 days (millions) — pypistats.org, Sep 4 2026 langgraph strands-agents (AWS) openai-agents claude-agent-sdk crewai google-adk pydantic-ai llama-index semantic-kernel agent-framework (MS) autogen-agentchat smolagents (HF) 62.6 36.6 32.4 31.3 25.8 19.1 10.7 6.3 2.1 1.2 0.9 0.6
Bar lengths proportional to monthly downloads. LangGraph alone moves more volume than the next two combined. CrewAI's organic (mirror-free) rate is roughly 2.3M/month based on its weekly figure of 572k.

The leaders in detail

1. LangGraph (LangChain ecosystem)

Downloads/month62.6MGitHub stars~134k (langchain monorepo)

LangGraph is a low-level orchestration runtime that models multi-agent systems as stateful graphs with loops, persistence (checkpointing), and human-in-the-loop control. It hit a stable 1.x line and pairs with LangChain (1,000+ integrations), Deep Agents (a harness for long-running research/coding agents), and LangSmith for tracing and evaluation. It is the framework most production teams name first, and the one competitors benchmark against.

2. AWS Strands Agents

Downloads/month36.6MGitHub stars7.1k+ (young repo, split across SDKs)

Open-source SDK "built from production systems inside Amazon", in Python and TypeScript. Model- and cloud-agnostic, with a model-driven design (the LLM plans; you supply tools, hooks, guardrails, and steering policies) and built-in multi-agent patterns such as Agent-as-Tool and Swarm. Deploys to Bedrock AgentCore, Lambda, Fargate, and EKS. Named production users include Smartsheet, Swisscom, Verisk, Eightcap, Zafran, Jit, and Tavily. Its download volume is amplified by AWS's own CI ecosystem, but the customer list is real.

3. OpenAI Agents SDK

Downloads/month32.4MGitHub stars29.2k (openai-agents-python)

A deliberately small framework for multi-agent workflows: agents, tools, handoffs (delegation between agents), guardrails, and built-in tracing. It replaced the educational Swarm project (21.9k stars, now frozen). Works best on OpenAI models but supports others via LiteLLM. Teams typically add Temporal or DBOS for durable, long-running execution.

4. Claude Agent SDK (Anthropic)

Downloads/month31.3MLanguagesPython, TypeScript

The agent loop that powers Claude Code, packaged as a library. Subagents are a first-class primitive ("spawn specialized agents for focused subtasks"), alongside hooks, MCP, permissions, sessions, skills, and plugins. This is arguably the most-used multi-agent system in day-to-day practice, because every Claude Code user who runs parallel subagents is running a multi-agent system, whether they call it that or not. Anthropic also offers hosted Managed Agents.

5. CrewAI

Downloads/month25.8M (≈2.3M organic)GitHub stars~49k

The leading independent framework, built on a role-based mental model: each agent has a persona, goal, and tools, and a "crew" divides a task among them. Self-contained (no LangChain dependency), MIT-licensed, with MCP and A2A support. The company behind it now sells an enterprise "agent build & runtime" platform and claims usage by 65% of the Fortune 500, with case studies from Docusign, General Assembly, Gelato, Piracanjuba, and Konecta. Community-reported rough edges: non-OpenAI provider reliability and async execution.

6. Google ADK (Agent Development Kit)

Downloads/month19.1MGitHub stars~19k (adk-python)

Google's opinionated, batteries-included framework, now shipping in Python, TypeScript, Go, Java, and Kotlin; ADK 2.0 added graph workflows. Comes with a CLI (adk web, adk run, adk api_server), a browser debugging UI, built-in sessions/memory, evals, and native MCP, A2A, and OpenAPI tool support. Strongest for teams deploying on Vertex AI Agent Engine, Cloud Run, or GKE.

7. Microsoft Agent Framework

Downloads/month1.2M (agent-framework)GitHub stars13.3k, very active

The unified successor to AutoGen (60.8k stars, maintenance mode since its 1.0 GA) and Semantic Kernel (2.1M downloads/month, security-fixes-only). It merges AutoGen's conversational multi-agent patterns (sequential, concurrent, handoff, group chat, Magentic-One) with Semantic Kernel's enterprise features, ships Python and .NET runtimes simultaneously, and integrates Azure AI Foundry guardrails, OpenTelemetry, MCP, and A2A. Raw downloads are still small because it is young and most Microsoft-stack teams are mid-migration; the direction of travel is unambiguous, since Microsoft has pointed both predecessor communities at it.

Notable second tier

SystemSignalWhat it is
Pydantic AI10.7M dl/moType-safe Python agent framework from the Pydantic team; growing fast as the "FastAPI of agents".
LlamaIndex Workflows6.3M dl/mo (llama-index)Event-driven multi-agent orchestration for document-heavy pipelines.
Haystack (deepset)26.4k starsPipeline-based orchestration, strong in RAG-centric production systems.
Mastra~23k starsTypeScript-first agent framework (from the Gatsby team): workflows, memory, Studio.
MetaGPT70.2k stars"AI software company" simulation; hugely starred, research-flavored, lighter production use.
CAMEL17.7k starsAcademic multi-agent framework focused on agent-society scaling laws.
TradingAgents103k starsMulti-agent LLM trading framework; the most-starred "multi-agent" repo on GitHub, popular with retail quants.
smolagents (Hugging Face)571k dl/moMinimal code-acting agents; popular for learning and lightweight tasks.
AG2312k dl/moCommunity fork continuing the original AutoGen line.

Deprecated or superseded — avoid for new projects

Which one should you pick?

If you…Use
Need precise control over stateful, cyclic multi-agent graphs with persistence and human-in-the-loopLangGraph
Run on AWS / Bedrock and want enterprise guardrails with minimal codeStrands Agents
Want the simplest possible handoff-style delegation on OpenAI modelsOpenAI Agents SDK
Are building coding/research agents, or want Claude Code's loop with subagents in your own appClaude Agent SDK
Think in terms of role-based teams and want the fastest prototype (or a no-code enterprise platform)CrewAI
Are GCP-native or need Go/Java/Kotlin SDKsGoogle ADK
Are on Azure/.NET or migrating from AutoGen/Semantic KernelMicrosoft Agent Framework
Want type-safe, Pythonic agents without heavy abstractionsPydantic AI
Build TypeScript/Next.js productsMastra (or Strands TS / ADK TS)
One trend worth naming: the biggest real-world multi-agent usage in 2026 is not bespoke orchestration graphs; it is coding agents fanning out subagents (Claude Code, Codex, and similar harnesses running parallel workers across repos). The frameworks above are converging on the same interoperability rails, MCP for tools and A2A for agent-to-agent communication, so multi-agent systems built on different stacks can increasingly talk to each other.

Sources (visited September 4, 2026)