KDnuggets named 7 frameworks for orchestrating AI agents: from LangGraph to Google ADK

KDnuggets released a selection of seven frameworks for orchestrating AI agents — from LangGraph and CrewAI to Google ADK and AutoGen. The material is useful for those building multi-agent systems and choosing a stack for production, debugging, and integration with external tools.

Why This Matters

Agent applications have quickly moved beyond simple chatbots. Now they are expected to plan steps, connect tools, call APIs, delegate tasks to other agents, and maintain context across iterations. This is why orchestration has become a separate engineering layer: without state management, checks, retries, and clear control, such systems begin to fail in the most expensive places — on long scenarios, when working with code, documents, and user data.

In its selection, KDnuggets emphasizes not the "trendiest" names, but different approaches to building agent systems. Some frameworks are better suited for graph and cyclic workflows, others — for a team model with roles, and still others — for enterprise deployments with observability and security. Separately highlighted are solutions oriented toward strict typing, multimodal work, deep integration with knowledge bases, and convenient integration standards between agents and tools.

Seven Frameworks

The first half of the list includes LangGraph, CrewAI, Pydantic AI, and Google Agent Development Kit. The authors call LangGraph a strong choice for stateful and multi-step systems: it has explicit state management, cycles, checkpointing, and human-in-the-loop. CrewAI, by contrast, relies on a simple role-based model where each agent has a goal, role, and area of responsibility. Pydantic AI stands out for type safety, built-in validation, MCP support, and durable execution. Google ADK is interesting for those building production agents within the Google Cloud and Vertex AI ecosystem, including multimodal scenarios.

The second part of the list includes AutoGen, Semantic Kernel, and LlamaIndex Agent Workflow. AutoGen remains strong where multiple agents need to have dialogue with each other, collaboratively write code, and work in different automation modes. Microsoft's Semantic Kernel is positioned as an enterprise-oriented layer with memory, planning approach, plugins, and built-in observability and compliance requirements. LlamaIndex was added not as a classic "agent" brand, but as a practical event-driven tool for scenarios where agents constantly pull data from documents and external repositories.

As an additional mention, KDnuggets names OpenAI Swarm — a lightweight, more educational than production-ready stack.

How to Choose a Stack

The good insight of the article is that there is no universal winner here. If a team is building an internal assistant for a company, security, execution control, and integration with corporate services are important. If the task is a research agent or coding assistant, cycles, debugging, state preservation, and convenience of coordinating multiple roles come to the forefront. If a project is tied to documents, RAG, and asynchronous processes, data-centric and event-driven approaches win.

Therefore, it makes more sense to choose such a stack not by noise on X or GitHub, but by workflow type, task length, and production requirements.

• For complex cycles and checkpointing, it makes sense to look at LangGraph.
• For role-based teams of agents and quick start, CrewAI is suitable.
• For strict typing, validation, and testability, Pydantic AI is strong.
• For an enterprise environment and Microsoft stack, Semantic Kernel is appropriate.
• For document-heavy and knowledge-intensive scenarios, LlamaIndex is worth checking.

A separate plus of the material is that it doesn't limit itself to a list of names, but suggests what practical projects can be built on each stack. Among the ideas are a research assistant, multi-agent market analysis, type-safe customer support agent, multimodal helper, and a pipeline for processing large document collections.

This format is useful not only for beginners, but also for teams already experimenting with agents and wanting to transition faster from demo to system architecture.

What This Means

The AI agent market is maturing quickly: teams are increasingly less likely to assemble everything manually and more often choose a framework for a specific scenario — code, search, documents, enterprise integrations, or multimodality. For Russian-speaking developers, this is a good benchmark: comparison should be not "who is louder," but who better maintains state, handles errors, and handles real production load. The next stage of the market is not new demos, but sustainable agent systems that can be properly maintained, tested, and scaled.