Lo que muestran los datos

A abril de 2026, Buzzi.ai clasifica 10 frameworks multiagente en 15 ejes de capacidad — patrones, estado, HITL, MCP/A2A, observabilidad, despliegue y más. Los multiplicadores de sobrecarga de tokens van de ×1.0 (LangGraph) a ×2.5 (AutoGen) — la diferencia entre una tarea de $0,04 y una de $0,10 con la misma carga.

Cómo funciona

Diez preguntas rápidas.
Una lista priorizada como respuesta.

Sin registro, sin hojas de cálculo, sin promoción de proveedores. Pensado para responsables de ingeniería, equipos de IA aplicada y arquitectos que necesitan una recomendación defendible en menos de dos minutos.

  1. Paso uno

    Cuéntanos tu carga de trabajo.

    Patrón, estado, latencia, HITL, MCP/A2A, pila de lenguaje — diez decisiones rápidas. Cada respuesta acota la matriz.

  2. Paso dos

    Evaluamos 15 ejes.

    Puntuaciones editoriales de nuestro equipo de IA aplicada, verificadas trimestralmente. Las restricciones duras descalifican; las señales suaves ajustan el ranking.

  3. Paso tres

    Despliega con un esqueleto.

    Top 3 clasificados, coste por tarea estimado contra tu volumen de tokens, y un esqueleto inicial ejecutable en tu lenguaje.

10 frameworks · 15 ejes · cero pago por colocación

Cada framework que clasificamos.

Los multiplicadores de sobrecarga de tokens son específicos del framework — relativos a LangGraph en ×1,0. Los diseños conversacionales como AutoGen están en ×2,5; los grafos estructurados y SDK se agrupan cerca de ×1,0–×1,4.

Lowest overhead

×1.0

LangGraph baseline

Highest overhead

×2.5

AutoGen worst case

Lo que medimos

Quince ejes, puntuados de 0 a 10.

Cada framework recibe una puntuación entera en cada eje. Los requisitos duros (pila de lenguaje, despliegue) descalifican; las señales suaves ajustan el ranking. Editorial, transparente y actualizado trimestralmente.

Orquestación

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

Pila y protocolos

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

Operaciones

  • Observability
  • Deployment flexibility
  • Production maturity
  • Learning curve

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

Patrones de arquitectura

Cuatro formas que puede tomar un sistema multiagente.

Tu carga de trabajo normalmente se asigna a uno — y el framework que elijas debe destacar primero en ese eje.

FAQ

Las preguntas más comunes.

Matemáticas de sobrecarga de tokens, MCP vs A2A, HITL, restricciones de pila de lenguaje — respondidas con honestidad 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.

Una segunda opinión

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