source: kdnuggets: 10 agentic ai frameworks you should know in 2026
level: technical
agentic ai frameworks have moved past simple llm wrappers. they now help manage state, memory, tool use, evaluations, and deployment. no single framework fits every project. some give explicit control over agent workflows, while others let you ship a working prototype with less code. this list covers ten frameworks worth knowing in 2026, based on research and hands-on experience.
langgraph models apps as graphs of states and transitions, making it good for long-running agents that need inspectability and control. crewai uses a role-based mental model where you define agents with roles and tasks, useful for quick multi-agent prototypes. the openai agents sdk offers a clean way to build tool-using agents with handoffs and tracing, and it supports other model providers. google's adk is a code-first toolkit for gemini and vertex ai users, with a local dev ui and full lifecycle support. pydanticai brings type safety and validated outputs to python agent development, focusing on reliable software engineering.
smolagents from hugging face lets models generate python code to call tools, but requires careful sandboxing. mastra is a typescript-first framework for full-stack teams, distinguishing between flexible agents and predictable workflows. microsoft agent framework targets enterprise .net and azure users with predictable software practices. strands agents takes a model-driven approach, letting the model reason about tool use, and fits aws environments. llamaindex workflows uses an event-driven model for document-heavy agents and rag systems. each framework has trade-offs, so the choice depends on your need for control, validation, observability, and tool access.
why it matters: picking the right agentic framework affects how reliably you can build, debug, and scale ai agents for production tasks.
source: kdnuggets: 10 agentic ai frameworks you should know in 2026