AI agent framework
A library or toolkit for building AI agents — providing primitives for tool calling, planning, memory, and orchestration so you do not rebuild the agent loop from scratch.
Agent frameworks turn LLM APIs into agent runtimes. The dominant 2026 options: LangGraph for stateful graph-based agents, CrewAI for role-based multi-agent setups, AutoGen for research-oriented conversational agents, and Anthropic's Agent SDK + OpenAI's Agents SDK for vendor-native development.
Choosing one matters less than people think. The agent loop is now well-understood — observe, reason, act, repeat — and most frameworks implement the same core primitives. What varies is ergonomics, observability hooks, and ecosystem fit.
For most teams in 2026, the right move is to start with the SDK from the model vendor you use most (Anthropic, OpenAI, Google), add LangGraph or a custom orchestration layer when complexity demands it, and reach for CrewAI/AutoGen only for genuinely multi-agent designs.
Frequently asked
Should I use LangChain, LangGraph, CrewAI, or AutoGen?+
LangGraph for stateful agents with branching control flow. CrewAI for clear role-based multi-agent setups. AutoGen for research and conversational multi-agent. Skip LangChain proper in 2026 — use LangGraph or the vendor SDK directly.
Do I need a framework at all?+
For a single-agent, single-tool prototype: no. For anything with branching state, memory across turns, or multiple agents: yes. The framework saves weeks of plumbing.