What the data shows

Em abril de 2026, a Buzzi.ai classifica 10 frameworks multiagente em 15 eixos de capacidade — padrões, estado, HITL, MCP/A2A, observabilidade, deployment e mais. Os multiplicadores de overhead de tokens variam de ×1.0 (LangGraph) a ×2.5 (AutoGen) — a diferença entre uma tarefa de $0,04 e uma de $0,10 com a mesma carga.

Como funciona

Dez perguntas rápidas.
Uma lista priorizada como resposta.

Sem cadastro, sem planilhas, sem propaganda de fornecedor. Feito para líderes de engenharia, equipes de IA aplicada e arquitetos que precisam de uma recomendação defensável em menos de dois minutos.

  1. Passo um

    Conte-nos sua carga de trabalho.

    Padrão, estado, latência, HITL, MCP/A2A, pilha de linguagem — dez escolhas rápidas. Cada resposta restringe a matriz.

  2. Passo dois

    Avaliamos 15 eixos.

    Pontuações editoriais da nossa equipe de IA aplicada, verificadas trimestralmente. Restrições rígidas desqualificam; sinais suaves ajustam o ranking.

  3. Passo três

    Entregue com um scaffold.

    Top 3 ranqueados, custo por tarefa estimado contra seu volume de tokens, e um scaffold inicial executável na sua linguagem.

10 frameworks · 15 eixos · zero pagamento por colocação

Cada framework que classificamos.

Multiplicadores de overhead de tokens são específicos do framework — relativos ao LangGraph em ×1,0. Designs conversacionais como AutoGen ficam em ×2,5; grafos estruturados e SDKs se agrupam perto de ×1,0–×1,4.

Lowest overhead

×1.0

LangGraph baseline

Highest overhead

×2.5

AutoGen worst case

O que medimos

Quinze eixos, pontuados de 0 a 10.

Cada framework recebe uma pontuação inteira em cada eixo. Requisitos rígidos (pilha de linguagem, deployment) desqualificam; sinais suaves ajustam o ranking. Editorial, transparente e atualizado trimestralmente.

Orquestração

  • Sequential workflows
  • Parallel workflows
  • Hierarchical workflows
  • Adaptive workflows
  • State management
  • Human-in-the-loop

Pilha e protocolos

  • Python support
  • TypeScript support
  • .NET / Java support
  • MCP (Model Context Protocol)
  • A2A (Agent-to-Agent)

Operações

  • Observability
  • Deployment flexibility
  • Production maturity
  • Learning curve

15 axes total. Each axis is editorial, integer-scored 0–10, and verified quarterly against framework releases.

Padrões de arquitetura

Quatro formas que um sistema multiagente pode tomar.

Sua carga de trabalho geralmente mapeia para um — e o framework escolhido deve ser forte primeiro nesse eixo.

FAQ

As perguntas mais frequentes.

Matemática de overhead de tokens, MCP vs A2A, HITL, restrições de pilha de linguagem — respondidas com honestidade editorial.

Get instant answers from our AI agent

It ranks 10 multi-agent orchestration frameworks against your workload across 15 capability axes, estimates cost-per-task using each framework’s token-overhead multiplier, and generates a runnable starter scaffold in your language stack. Scores are editorial, transparent, and verified quarterly.
Up to 2.5x variance. AutoGen’s conversational overhead produces roughly 2.5x the tokens per task of LangGraph’s structured graph edges on equivalent workloads. The tool surfaces this multiplier per framework so you can see the cost delta before you commit.
base_task_tokens x framework_overhead_multiplier x (1 + (roles - 1) * 0.3) x (1.2 if HITL else 1.0). Default base is 15,000 tokens. Token rates come from our llm_models table. All assumptions are published on the methodology page and editable in the tool.
MCP (Model Context Protocol) is Anthropic’s open standard for connecting agents to tools and data servers. A2A (Agent-to-Agent) is Google’s open standard for agents from different vendors to discover and call each other. The two are complementary, not competing.
LangGraph scores highest at 10/10 thanks to first-class interrupt and resume primitives. AutoGen and Google ADK follow at 7 to 8. CrewAI, Semantic Kernel, and OpenAI Agents SDK ship basic approve-before or review-after hooks. Pydantic AI and Haystack are the weakest on HITL.
LangGraph and the OpenAI Agents SDK lead with structured tracing, replayable runs, and exportable audit logs. Semantic Kernel’s OpenTelemetry story is strong for .NET-first regulated shops. Haystack and Pydantic AI (via Logfire) are adequate for compliance-grade but not regulated-grade workloads.
LangGraph for production workloads that need auditable state and strong observability. CrewAI for fast prototypes and sequential crews where token cost is not critical. AutoGen (or AG2) for research-grade adaptive workflows where emergent agent behavior matters more than token efficiency.
Yes. .NET stacks narrow to Microsoft Semantic Kernel. Java stacks narrow to Semantic Kernel or Google ADK. Pure TypeScript with compliance-grade observability narrows to LangGraph.js, OpenAI Agents SDK, or Anthropic Claude SDK. Python runs every framework.
Every scaffold is a minimal 2-agent hello-world with pinned dependencies, a Dockerfile, and a README. A weekly CI job installs the latest stable framework version and runs the scaffold end-to-end. If a build fails, that scaffold download is disabled until it is fixed.
Scores are manually verified quarterly by a named Buzzi engineer, and version and release data are auto-refreshed monthly via GitHub release RSS. Every framework row on the methodology page shows its last_verified_at timestamp.
Yes — every ranked framework is an active, stable project with more than 10,000 GitHub stars and ongoing releases. Maturity scores on the capability matrix reflect real production battle-testing. The starter scaffolds ship with Docker images and sensible defaults.
No. Scores are editorial and never sold. Score changes require public justification on the open-source matrix repo. We publish the integrity triplet "no vendor pay-to-play, no guessed scores, no demo-ware" on every methodology page.
Your 10 wizard answers, optional email and company profile if you request a PDF or scaffold, UTM parameters, and aggregate events. Anonymous sessions never leave the browser until you submit. Full detail is on our privacy policy and the tool’s methodology page.
Indirectly. The observability axis and data-residency flag help you shortlist frameworks whose architecture aligns with these regimes. The tool does not replace legal review, DPIAs, or vendor questionnaires — but it narrows the candidate pool so those reviews target the right two or three frameworks.
LangGraph, Haystack, and AutoGen score 8 to 9 on maturity. LlamaIndex Agents and Semantic Kernel are solid 8s. CrewAI, OpenAI Agents SDK, and the Anthropic Claude SDK are productive at 7. Pydantic AI and Google ADK are the youngest at 6 — promising but evolving quickly.

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