What the data shows

2026년 4월 기준으로 Buzzi.ai는 15개의 능력 축에서 10개의 멀티에이전트 프레임워크를 평가합니다 — 패턴, 상태, HITL, MCP/A2A, 관찰성, 배포 등. 토큰 오버헤드 계수는 ×1.0(LangGraph)에서 ×2.5(AutoGen)까지 범위이며 — 같은 워크로드에서 $0.04 작업과 $0.10 작업의 차이입니다.

작동 원리

10개의 빠른 질문.
응답으로 순위가 매겨진 후보 목록.

가입 없음, 스프레드시트 없음, 벤더 홍보 없음. 2분 이내에 방어 가능한 추천이 필요한 엔지니어링 리더, 응용 AI 팀, 아키텍트를 위해 만들어졌습니다.

  1. 1단계

    워크로드를 알려주세요.

    패턴, 상태, 지연 시간, HITL, MCP/A2A, 언어 스택 — 10개의 빠른 선택. 각 답변이 매트릭스를 좁힙니다.

  2. 2단계

    15개 축으로 평가합니다.

    응용 AI 팀의 편집적 점수, 분기마다 검증됨. 하드 제약은 자격을 박탈하고, 소프트 신호는 순위를 조정합니다.

  3. 3단계

    스캐폴드로 출시하세요.

    상위 3개 순위, 토큰 양 대비 작업당 비용 추정, 사용자 언어의 실행 가능한 스타터 스캐폴드.

10개 프레임워크 · 15개 축 · 유료 게재 없음

우리가 평가하는 모든 프레임워크.

토큰 오버헤드 계수는 프레임워크별 — LangGraph(×1.0)를 기준으로 합니다. AutoGen 같은 대화형 디자인은 ×2.5에 위치하고, 구조화된 그래프와 SDK는 ×1.0–×1.4 근처에 모입니다.

Lowest overhead

×1.0

LangGraph baseline

Highest overhead

×2.5

AutoGen worst case

측정 항목

15개 축, 0~10점으로 평가.

각 프레임워크는 각 축에서 정수 점수를 받습니다. 하드 요구사항(언어 스택, 배포)은 자격을 박탈하고, 소프트 신호는 순위를 조정합니다. 편집적, 투명하며 분기마다 업데이트됩니다.

오케스트레이션

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

스택 및 프로토콜

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

운영

  • Observability
  • Deployment flexibility
  • Production maturity
  • Learning curve

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

아키텍처 패턴

멀티에이전트 시스템이 취할 수 있는 4가지 형태.

워크로드는 일반적으로 하나에 매핑됩니다 — 그리고 선택하는 프레임워크는 먼저 그 축에서 강해야 합니다.

FAQ

가장 자주 묻는 질문.

토큰 오버헤드 수학, MCP 대 A2A, HITL, 언어 스택 제약 — 편집적 정직함으로 답변합니다.

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.

두 번째 의견

약속하기 전에 두 번째 의견을 원하시나요?

Buzzi.ai는 6주 만에 맞춤형 멀티에이전트 시스템을 제공합니다. 마법사 출력을 30분 범위 설정 통화로 가져오시면 도구가 놓친 것을 알려드립니다.

범위 설정 통화 예약방법론 읽기