AI is no longer just creating chatbots; now it can plan, use external tools, scrape data, and perform multi-step processes. Frameworks for creating AI agents are making this possible. AI agent frameworks are software suites for designing, prototyping, orchestrating, debugging, and deploying AI agents.
These frameworks mean that in 2026, you will be able to rapidly prototype new AI agents or create operational AI agents for complex workflows, multi-agent workflows, and enterprise-scale applications. Here are 10 popular frameworks and why to use them.
1. LangGraph
Created by LangChain, LangGraph is a framework for building stateful AI agents with granular control over execution. It incorporates a graph-based structure that manages new execution flows with conditional logic, persistence, human approval, and the ability to recover from interruption. It’s ideal for enterprise use and long-term agents.
2. CrewAI
CrewAI is a collaborative, role-based platform for managing multi-agent artificial intelligence. For multi-AI applications, coders can assign each agent its own role and then group them into teams to conduct research, analysis, content creation, or run a business. It has a very simple product design and user interface for fast prototyping.
3. OpenAI Agents SDK
The OpenAI Agents SDK allows you to construct agents that invoke functions, hand off tasks between agents, and utilize guardrails. It includes tracing, which allows you to observe agent behaviour and debug your application’s flow. A good choice for applications with tool use and multiple agents in collaboration.
4. Google Agent Development Kit (ADK)
The Google Agent Development Kit (ADK) can be used to develop, test, and deploy your AI agents- or multi-agent systems. The kit has a modular architecture, supports integrations with popular tools and coding languages (Python, TypeScript, Go, and Java), and may be useful for people building Gemini apps or using Google Cloud.
5. Microsoft Agent Framework
Microsoft Agent Framework offers the combined features of AutoGen and Semantic Kernel. It can implement single agents, multi-agent workflows, memory, tool integration, and human-in-the-loop execution. It supports Python and .NET, the latter being useful for organisations building AI in the Microsoft ecosystem and on Azure.
6. LlamaIndex
LlamaIndex is a great fit for document-focused or enterprise knowledge agents. Its indexing and retrieval features, as well as data connectors, allow developers to create search functions over internal data or information, as well as generate context sensitive responses. It’s a good choice for RAG and document analysis tools or research assistants.
7. Mastra
Mastra is a TypeScript framework for creating AI agents and applications. It supplies tools to developers for creating agents, workflows, and integrations in the JavaScript/TypeScript ecosystem. This is mainly for teams creating AI based mostly web apps without a separate Python agent layer.
8. Pydantic AI
Type-safe application development for AI with structured data validation. Pydantic AI empowers developers to author and verify expected output, compose tools, and develop applications where the interface to the language model is more explicit and predictable than standard software. It is particularly relevant for Python teams looking for predictable data validation and maintainability.
9. Haystack
Haystack, a deep-set built tool, is a modular framework designed for developing AI applications with pipelines, retrieval, and agent components. Document search and processing, search, and RAG are all features of the tool. Developers can leverage search with tool use to develop knowledge-intensive chatbots and business information systems.
10. Claude Agent SDK
The Claude Agent SDK from Anthropic offers a set of primitives to build agents based on Anthropic’s Claude models. It provides support for tool-based workflows and integrations that might be useful for coding, file workflows, and research-based use cases. Review the model provider requirements and tool permissions before selecting this option.
How Should Developers Choose an AI Agent Framework?
The best framework for you will depend on your use case, programming language, provider, and operational needs: complex, stateful workflows might use LangGraph; CrewAI is a good choice for role-oriented, collaborative workflows; OpenAI Agents SDK provides an easy-to-implement path to tool-using agents; Google ADK is best if you’re in the Google Cloud world; for document-centric workflows, check out Llama Index and Haystack.
For TypeScript teams, Mastra; for anyone who loves typed Python, Pydantic AI; and for enterprise solutions, Microsoft Agent Framework. You’ll want to consider documentation, observability, security controls, deployment options, licensing, and ongoing costs. You don’t necessarily need an agent framework- a single model invocation or a traditional ETL-style workflow may be simpler to run.
Conclusion
Frameworks for AI agents are emerging as essential components of applications that require more than text generation. They help developers leverage AI capabilities into functional software by reducing the complexity of tool integration, orchestrating workflows, managing context across multiple agents, and orchestrating multi-agent systems. There’s no single best option. The best framework depends on the scale, complexity, technical stack, reliability requirements, and costs of your project.
