Decision in 20 seconds
Choose the agent framework by workflow shape: LangGraph for stateful control, CrewAI for role collaboration, OpenAI Agents SDK for OpenAI-native orchestration, Google ADK for Gemini/cloud deployment, Mastra for TypeScript product apps, and Microsoft Agent Framework for enterprise .NET/Python/Azure workflows.
Use this page when
- You are choosing an agent framework for a real builder project.
- You need to compare LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Mastra, Microsoft Agent Framework, and LlamaIndex Workflows.
- You want a framework shortlist based on workflow shape, not hype.
This page is not for
- A complete benchmark of every agent framework.
- One-off prompt scripts that do not need orchestration.
- Enterprise procurement scoring without hands-on workflow tests.
Key points
- Agent framework choice should start from task shape, not launch hype.
- The most important comparison dimensions are state, control flow, observability, deployment, ecosystem fit, and human approval.
- Most teams should run one representative workflow through two or three serious candidates before standardizing.
What changed recently
- Agent frameworks are shifting from prototype helpers into runtime, deployment, and observability decisions.
- OpenAI, Google, Microsoft, LangChain, CrewAI, and Mastra now represent different ecosystem bets, not interchangeable wrappers.
- Builder teams increasingly need framework selection tables rather than generic 'agent framework' explainers.
Explanation
The best AI agent framework for a builder is the one that matches the control surface they actually need. A team building a long-running, stateful workflow should not choose the same tool as a team prototyping a role-based research crew or a TypeScript-first product feature. Agent frameworks increasingly overlap in their marketing language, so the useful comparison is not 'which framework is best' in the abstract. The useful comparison is control flow, state, tooling, observability, deployment fit, and how much of the runtime loop your team wants to own.
LangGraph is strongest when the work needs explicit orchestration, durable state, persistence, human-in-the-loop steps, streaming, and debuggable transitions. Its value is that the agent loop is not a mystery. Builders can model a process as a graph and reason about where state moves next. That makes it a better fit for complex workflows, multi-step support systems, research loops, and internal tools where failure recovery matters. The cost is that it asks the team to think more carefully about the workflow shape.
CrewAI is strongest when the mental model is collaborative roles and task delegation. It is useful for teams that want to describe agents as researchers, writers, reviewers, analysts, or operators and then wire those roles into crews and flows. This can be productive for content, research, business workflows, and automations where the task split is intuitive. The risk is overbuilding a team of agents when a single workflow or tool call would be clearer. Crew-style systems still need crisp boundaries, source handling, and stop conditions.
OpenAI Agents SDK is most useful when the application is already close to OpenAI's tool and runtime ecosystem and the team wants lightweight orchestration with tools, guardrails, handoffs, and sessions. It is not automatically better than owning the loop directly with the Responses API. The key question is whether the SDK's primitives reduce boilerplate without hiding too much control. If your application needs a small number of agents, clear tool calls, and a provider-supported orchestration path, it belongs on the shortlist.
Google ADK is worth watching for teams building with Gemini, Google Cloud, or enterprise agent workflows. Its public positioning emphasizes building, debugging, and deploying reliable agents at scale, and it fits teams that expect multi-agent systems, cloud deployment, and enterprise operational surfaces to matter. The practical evaluation is whether ADK reduces the work of moving from local agent experiments to managed deployment and debugging. Teams outside the Google ecosystem can still learn from it, but should test integration cost honestly.
Mastra is especially relevant for TypeScript-first teams. It gives builders agents, workflows, memory, MCP-related integration paths, logging, tracing, evals, and product-app integration in a modern JS/TS stack. This matters because many AI product teams are not building standalone Python agent research systems; they are adding agentic behavior into Next.js, Node, React, or product backends. Mastra is often worth testing when the agent is part of an app surface rather than a separate automation lab.
Microsoft Agent Framework is important for .NET, Python, Azure, Semantic Kernel, AutoGen, and enterprise workflow contexts. It is positioned as a successor that combines single-agent and multi-agent patterns with state, telemetry, type safety, filters, and model support. Teams already in Microsoft or Azure environments should evaluate it early because the ecosystem fit may outweigh raw framework fashion. Teams outside that environment should still watch it because it will influence enterprise agent expectations.
LlamaIndex Workflows belongs in the comparison when the agent's job is strongly tied to retrieval, data indexing, structured knowledge, and RAG-like pipelines. Not every agent system starts with orchestration; many start with documents, data connectors, retrieval quality, and source-grounded reasoning. For those teams, a data-centric workflow tool may be more practical than a role-based multi-agent framework.
A practical first pass is to choose by task shape. Use LangGraph when the path is stateful and needs control. Use CrewAI when role collaboration is the main abstraction. Use OpenAI Agents SDK when the OpenAI runtime path fits and you want a compact orchestration layer. Use Google ADK when Gemini and cloud deployment are central. Use Mastra when the product is TypeScript-first. Use Microsoft Agent Framework when enterprise .NET/Python and Azure alignment matter. Use LlamaIndex Workflows when retrieval and data pipelines dominate.
The wrong way to choose is to build the same demo in five frameworks and pick the one that feels fastest on day one. The right way is to run one representative workflow through each serious candidate: define state, tool calls, failure handling, trace output, human approval, deployment target, and maintenance owner. A framework that makes the demo slower but the production workflow clearer may be the better choice.
Agent framework selection table
Use this table to map framework choice to task shape and team context.
| Framework | Best fit | Watch first | Avoid if |
|---|---|---|---|
| LangGraph | Stateful, long-running workflows that need control and recovery | Persistence, graph shape, human-in-the-loop, tracing | Your task is one simple tool call |
| CrewAI | Role-based collaboration and multi-agent business workflows | Crews, flows, memory, knowledge, observability | Roles are artificial and make debugging harder |
| OpenAI Agents SDK | OpenAI-native agent apps with handoffs, tools, guardrails, and sessions | SDK primitives, Responses API fit, tool execution | You need to fully own orchestration yourself |
| Google ADK | Gemini / Google Cloud agent lifecycle | Build, debug, deploy, multi-agent, cloud integration | You are not in the Google ecosystem and want minimal platform coupling |
| Mastra | TypeScript product apps and AI features | Agents, workflows, memory, MCP, logging, tracing, evals | Your team is Python-only and not product-app oriented |
| Microsoft Agent Framework | Enterprise .NET/Python/Azure agent workflows | State, telemetry, type safety, filters, AutoGen/Semantic Kernel lineage | You do not need Microsoft ecosystem fit |
| LlamaIndex Workflows | Retrieval-heavy and data-centric agent workflows | Indexes, retrieval, source grounding, workflow control | The problem is orchestration-first rather than data-first |
How to verify the answer
Start with official framework docs and repositories, then run one representative workflow through the serious candidates before standardizing.
Tools / Examples
Evidence timeline
Sources
- https://www.langchain.com/langgraph
- https://docs.crewai.com/
- https://developers.openai.com/api/docs/guides/agents
- https://adk.dev/
- https://mastra.ai/docs
- https://learn.microsoft.com/en-us/agent-framework/
FAQ
What is the best AI agent framework for most builders?
There is no universal best. Start from workflow shape. If state and control matter, test LangGraph. If TypeScript product integration matters, test Mastra. If OpenAI-native orchestration matters, test OpenAI Agents SDK.
Should I use multiple agent frameworks?
Usually not at first. Standardize around one primary runtime per project, then add specialized tools only when the workflow justifies the complexity.
When should I avoid an agent framework?
Avoid a framework when the task is one model call plus a small amount of application-owned logic. In that case, direct API calls and explicit code may be clearer.
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Last updated: 2026-06-29 · Policy: Editorial standards · Methodology