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Multi-Agent Frameworks

Comparing orchestration frameworks for 2026

TL;DR

LangGraph leads multi-agent frameworks at 34% market share with its graph-based orchestration, followed by CrewAI at 28% for role-based teams. The market is consolidating around two paradigms: deterministic graphs (LangGraph) and autonomous swarms (CrewAI/AutoGen).

Updated 2026-01-2711 sources validated8 claims verified

34%

LangGraph market share

Framework Survey

28%

CrewAI market share

Framework Survey

5

Major frameworks

Research

72%

Enterprise multi-agent adoption

G2 Report
01

Framework Landscape 2026

The multi-agent framework market has matured significantly. Five major players dominate, each with distinct architectural philosophies. The choice between them increasingly depends on specific deployment requirements rather than feature checklists.

LangGraph

34%

Graph-based state machines. Best for deterministic, auditable workflows. 34% market share.

CrewAI

28%

Role-based agent teams. Best for autonomous collaboration. 28% market share.

AutoGen

18%

Conversation-driven patterns from Microsoft. Strong for research and prototyping.

OpenAI Agents SDK

New

Native OpenAI integration. Handoff patterns, guardrails built-in. Rapid adoption.

Pydantic AI

Rising

Type-safe, production-focused. Minimal abstraction, maximum control.

02

Selection Criteria

Choosing a framework requires evaluating: determinism needs (regulated industries demand LangGraph), team autonomy requirements (creative tasks suit CrewAI), existing stack (OpenAI-heavy shops benefit from Agents SDK), and production requirements (type safety drives Pydantic AI adoption).

Deterministic workflows

Best

LangGraph: explicit state machines with human-in-the-loop checkpoints

Autonomous agent teams

Best

CrewAI: agents negotiate roles and delegate independently

Rapid prototyping

Best

AutoGen: conversation-based patterns, quick setup

Type-safe production

Best

Pydantic AI: minimal abstraction, strong typing throughout

03

Convergence Trends

All frameworks are converging on three capabilities: built-in memory/state management, native tool integration, and observability hooks. The differentiator is shifting from features to developer experience and production reliability.

Key Findings

1

LangGraph commands 34% market share with graph-based deterministic orchestration

2

CrewAI holds 28% with role-based autonomous agent teams

3

OpenAI Agents SDK growing fastest due to native integration with GPT models

4

Pydantic AI gaining traction in production environments requiring type safety

5

All major frameworks converging on memory, tools, and observability as baseline features

Frequently Asked Questions

LangGraph leads at 34% market share with graph-based deterministic orchestration, followed by CrewAI at 28% for role-based agent teams.