A software framework that provides the building blocks for creating, coordinating, and managing AI agents—including prompt management, tool integration, memory persistence, multi-agent coordination, error handling, and observability. The five that matter for production in 2026 are LangGraph (graph-based, durable state), CrewAI (role-based crews), the OpenAI Agents SDK (handoffs + hosted tools), Google ADK (Gemini/Vertex-native), and the Microsoft Agent Framework (the GA'd merger of AutoGen and Semantic Kernel — standalone AutoGen is now maintenance-only). These frameworks abstract common infrastructure patterns so developers focus on agent logic rather than plumbing.
Frequently asked questions
Which orchestration framework should I use?
Match the framework to the workload's shape. For complex, auditable, Human-in-the-Loop (HITL) stateful workflows: LangGraph—graph execution with Checkpointing (Agent State). For the fastest multi-agent prototype: CrewAI's crews. For low-friction GPT-centric agents: the OpenAI Agents SDK. For Azure/.NET or if you were on AutoGen/Semantic Kernel: the Microsoft Agent Framework. For Claude, single-agent, minimal overhead: the Claude Agent SDK. Start with the simplest framework that meets your needs.
Do I need a framework to build an AI agent?
No. Simple agents (single LLM call + tool use) can be built with direct API calls in 50-100 lines of code. Frameworks add value when you need: persistent memory across conversations, durable multi-step workflows with error recovery, coordination between multiple agents, or production observability. Adopt one when the plumbing it removes exceeds the abstraction it adds.