What the data shows

Al aprile 2026, Buzzi.ai classifica 10 framework multi-agente su 15 assi di capacità — pattern, stato, HITL, MCP/A2A, osservabilità, deployment e altro. I moltiplicatori di overhead in token vanno da ×1.0 (LangGraph) a ×2.5 (AutoGen) — la differenza tra un task da $0,04 e uno da $0,10 con lo stesso carico.

Come funziona

Dieci domande rapide.
Una shortlist classificata in risposta.

Senza registrazione, senza fogli di calcolo, senza marketing dei vendor. Pensato per responsabili di engineering, team di IA applicata e architetti che hanno bisogno di una raccomandazione difendibile in meno di due minuti.

  1. Passo uno

    Descrivi il tuo carico di lavoro.

    Pattern, stato, latenza, HITL, MCP/A2A, stack linguistico — dieci scelte rapide. Ogni risposta restringe la matrice.

  2. Passo due

    Valutiamo 15 assi.

    Punteggi editoriali del nostro team di IA applicata, verificati trimestralmente. I vincoli rigidi squalificano; i segnali soft modificano il ranking.

  3. Passo tre

    Distribuisci con uno scaffold.

    Top 3 classificati, costo per task stimato sul tuo volume di token, e uno scaffold di partenza eseguibile nel tuo linguaggio.

10 framework · 15 assi · zero pay-per-placement

Ogni framework che classifichiamo.

I moltiplicatori di overhead in token sono specifici per framework — relativi a LangGraph a ×1,0. I design conversazionali come AutoGen sono a ×2,5; grafi strutturati e SDK si raggruppano vicino a ×1,0–×1,4.

Lowest overhead

×1.0

LangGraph baseline

Highest overhead

×2.5

AutoGen worst case

Cosa misuriamo

Quindici assi, valutati da 0 a 10.

Ogni framework riceve un punteggio intero su ogni asse. I requisiti rigidi (stack linguistico, deployment) squalificano; i segnali soft modificano il ranking. Editoriale, trasparente e aggiornato trimestralmente.

Orchestrazione

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

Stack e protocolli

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

Operazioni

  • Observability
  • Deployment flexibility
  • Production maturity
  • Learning curve

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

Pattern di architettura

Quattro forme che un sistema multi-agente può prendere.

Il tuo carico di lavoro di solito si mappa a uno — e il framework che scegli dovrebbe essere forte prima su quell'asse.

FAQ

Le domande più frequenti.

Matematica dell'overhead in token, MCP vs A2A, HITL, vincoli di stack linguistico — risposte con onestà editoriale.

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 seconda opinione

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Buzzi.ai consegna sistemi multi-agente personalizzati in 6 settimane. Porta l'output del wizard a una call di scoping di 30 minuti e ti diremo cosa ha mancato lo strumento.

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