AI Agent Framework
A software library that provides the scaffolding for building AI agents—including tool use, memory management, planning loops, and orchestration. Frameworks abstract away low-level LLM interactions so developers can focus on business logic: the framework handles the observe-think-act loop while you define which tools the agent can call and what guardrails apply. Under the hood they reduce to three execution models — Graph-Based Orchestration (you draw the control flow, e.g. LangGraph), role-based crews (you define roles and a coordinator orchestrates, e.g. CrewAI), and handoff (agents pass control to each other, e.g. the OpenAI Agents SDK). The 2026 field consolidated around five production choices: LangGraph, CrewAI, the OpenAI Agents SDK, Google ADK, and the Microsoft Agent Framework (the GA'd merger of AutoGen and Semantic Kernel). Pick by the shape of your workload and your model/cloud commitments, not by popularity — and remember you may not need a framework at all for a single agent with a couple of tools.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Frequently asked questions
- Which AI agent framework should I choose in 2026?
- Match the framework's execution model to your workload. LangGraph for auditable, durable, Human-in-the-Loop (HITL) production workflows; CrewAI's role-based crews for the fastest prototype of a multi-agent team; the OpenAI Agents SDK for low-friction GPT-centric agents; Google ADK for Gemini/Vertex and multimodal; the Microsoft Agent Framework for Azure/.NET (and as the AutoGen successor). Because tools ride on MCP and agents interoperate over A2A, the choice is largely reversible.