Which AI agent framework should I use in 2026?

It depends on your use case. For coding-specific agent fleets (Claude Code, Codex, Gemini CLI), amux requires no framework code — you configure sessions and tasks via a web UI. For custom multi-agent pipelines in Python, LangGraph is the most mature framework with the best persistence and streaming support. For role-based workflows (researcher-writer-reviewer teams), CrewAI has the most intuitive API. For Microsoft Azure environments, AutoGen integrates natively. For production enterprise agents, Semantic Kernel has the strongest enterprise tooling.

What is the difference between LangGraph and CrewAI?

LangGraph models agents as a directed graph — nodes are steps, edges are transitions, and you have full control over state flow including cycles and branching. CrewAI abstracts this into roles and tasks — you define agents with roles (researcher, writer) and tasks, and CrewAI handles the orchestration. LangGraph is more flexible and powerful; CrewAI is easier to get started with for standard patterns.

Can I use LangGraph with Claude?

Yes. LangGraph is model-agnostic — it supports Claude via the LangChain Anthropic integration, as well as GPT-4, Gemini, and local models. The same graph definition works with any LLM provider by swapping the model object.

AI Agent Frameworks Compared 2026

Developer guide — architecture, language, multi-agent support, state management, and honest when-to-use. Last updated July 2026.

Framework	Language	Multi-agent	Local/Cloud	State management	Best for
amux	Python (no code required)	Yes — fleets	Local-first	SQLite + REST API	Coding agent fleets, overnight runs
LangGraph	Python / JS	Yes — graphs	Both	TypedDict, LangGraph Cloud	Custom stateful pipelines
CrewAI	Python	Yes — crews	Both	Per-task output passing	Role-based document workflows
AutoGen	Python	Yes — conversations	Both	Conversation history	Microsoft Azure, conversation agents
Semantic Kernel	C# / Python / Java	Yes — plugins	Both (Azure native)	Memory + kernel state	Enterprise, .NET shops
Bee Agent (IBM)	TypeScript	Yes	Both	Memory modules	IBM/TypeScript teams, complex memory
Agency Swarm	Python	Yes — agencies	Both	Thread-based	OpenAI Assistants API orchestration
MetaGPT	Python	Yes — software teams	Local-first	Shared message pool	Simulating software engineering teams

amux — Orchestrator, not a framework

amux differs from the others on this list: it is not a coding framework. You don't write Python to define agents. Instead, you configure tmux-based Claude Code, Codex, or Gemini CLI sessions via a web UI, queue tasks on a shared kanban board, and let agents claim and execute them. Self-healing watchdog, mobile monitoring, zero code to write for standard coding workflows.

Multi-agent: Fleet model — 10–50 coding agents sharing one kanban board. Atomic SQLite claiming prevents duplicate work. Get amux →

LangGraph

The most mature Python framework for stateful multi-agent systems. Agents are nodes in a directed graph; edges define the transitions. Supports cycles (for retry and reflection loops), conditional routing, and parallel fan-out. Streams token output. LangGraph Cloud provides hosted checkpointing and deployment.

State management: A TypedDict that every node reads and returns an update for. Persisted via SQLite (local) or PostgreSQL (LangGraph Cloud).

Multi-agent: Full support — subgraphs, handoffs, supervisor patterns. The most flexible option for complex workflows.

When to use: You need full control over flow, state, and transitions. You are building a custom pipeline that doesn't fit a higher-level abstraction. amux vs LangGraph →

CrewAI

Role-based multi-agent framework with an intuitive API. Define Agents with roles, goals, and backstories; define Tasks with expected outputs; wire them into a Crew. CrewAI handles sequencing, tool invocation, and output passing. Supports hierarchical crews with manager agents.

State management: Output of each task is passed to the next. No explicit state graph — agents communicate via task outputs.

Multi-agent: Yes — sequential or hierarchical crews. Manager agent can delegate to worker agents dynamically.

When to use: Document production, report generation, research-write-review pipelines where role separation is natural and you want a higher-level API than LangGraph.

AutoGen (Microsoft)

Conversation-based multi-agent framework from Microsoft Research. Agents communicate by sending messages to each other; you configure which agents talk to which and what triggers termination. Strong observability via AutoGen Studio. First-class Azure OpenAI integration.

State management: Conversation history is the primary state. Agents reference prior messages to maintain context.

Multi-agent: GroupChat for multiple agents; can include a HumanProxyAgent for human-in-the-loop approval.

When to use: Microsoft/Azure environments, research scenarios requiring conversation-based agent coordination, teams that want AutoGen Studio's visual debugger. amux vs AutoGen →

Semantic Kernel (Microsoft)

Enterprise-grade AI framework in C#, Python, and Java. Native Azure integration (Azure OpenAI, Cosmos DB, Azure Functions). Plugin-based architecture where tools are "skills" that the kernel invokes. Strong memory abstractions and enterprise governance features.

State management: Kernel memory (volatile, persistent, semantic search). Deep Azure Cosmos DB integration for enterprise persistence.

When to use: .NET/enterprise shops, Azure-native deployments, organizations that need enterprise governance and compliance tooling around AI agents.

Bee Agent Framework (IBM)

TypeScript-native agent framework from IBM Research. Modular memory system (token, summarize, sliding window), streaming output, tool integration via function calling. Well-suited for complex memory scenarios and TypeScript-first teams.

When to use: TypeScript-first teams who need sophisticated memory management and prefer not to use Python.

Agency Swarm

Python framework built on top of the OpenAI Assistants API. Agencies are groups of specialized agents that can communicate via threads. Designed specifically for OpenAI's persistent thread model. Less flexible than LangGraph but easier to build on top of OpenAI's hosted agent state.

When to use: Teams deeply committed to the OpenAI Assistants API who want a higher-level abstraction for multi-agent coordination.

MetaGPT

Research-oriented framework that simulates a software engineering team — Product Manager, Architect, Engineer, QA. Each role has specific responsibilities and generates artifacts (PRDs, architecture docs, code). More opinionated than LangGraph or CrewAI; less flexible but powerful for full software development simulation.

When to use: Research, demonstrations, or scenarios where you want to simulate a full software team from requirements to code. Less suited for production pipelines.