The top 10 AI agent frameworks to know in 2026

AI agent frameworks manage the iterative process in which a model calls tools, interprets results, and produces a structured decision, illustrated through a TypeScript customer-support triage agent that looks up orders and applies escalation policies. The comparison rebuilds the same Claude Haiku 4.5 agent using a manual Anthropic SDK loop and five frameworks—Vercel AI SDK, Mastra, LangGraph.js, OpenAI Agents SDK, and Claude Agent SDK—testing common tickets, follow-up conversations, ambiguous requests, and strict step limits. All frameworks simplify tool orchestration, but they differ substantially in conversation memory, structured-output mechanisms, streaming integrations, step-limit semantics, and failure behavior. The AI SDK offers straightforward React streaming and typed tool parts but requires callers to manage history, while Mastra adds persistent memory and a registry but can return an empty result silently after reaching its limit. LangGraph.js provides graph-level control and checkpointed state but uses tool-based structured output that created issues with multiple decisions, whereas the OpenAI Agents SDK provides native structured output, managed sessions, and clear turn-limit errors even when used with Claude through an adapter. The Claude Agent SDK runs through Claude Code and MCP, requires more configuration and custom streaming work, and may be best suited to cases needing its built-in coding-agent capabilities rather than narrowly focused support workflows.

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