How to Choose an AI Agent Framework: LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, and Mastra
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The useful question is not which agent framework is universally best. The useful question is which framework matches the shape of the workflow you are actually building. LangGraph is strongest when state, control flow, persistence, human review, and recovery matter. CrewAI is useful when the task naturally maps to roles, crews, and collaborative flows. OpenAI Agents SDK fits teams that want a lightweight orchestration layer around OpenAI tools, handoffs, guardrails, and sessions. Google ADK belongs on the shortlist when Gemini, Google Cloud, debugging, deployment, and enterprise agent lifecycle matter. Mastra is especially relevant for TypeScript-first product teams that want agents, workflows, memory, MCP, logging, tracing, and evals inside an application stack.
The practical selection table
| Framework | Best fit | First thing to test |
|---|---|---|
| LangGraph | Stateful workflows, durable execution, human-in-the-loop systems | Can you model the task as clear nodes, state, transitions, and recovery points? |
| CrewAI | Role-based collaboration and multi-agent business workflows | Do the roles map to real responsibilities, or are they decorative? |
| OpenAI Agents SDK | OpenAI-native apps with tools, handoffs, guardrails, and sessions | Does the SDK reduce boilerplate without hiding too much runtime control? |
| Google ADK | Gemini / Google Cloud agent lifecycle | Does it make debugging and deployment easier for your cloud environment? |
| Mastra | TypeScript-first AI product features | Does it fit your app stack, logging, memory, workflow, and eval needs? |
| Microsoft Agent Framework | Azure, .NET, Python, and enterprise workflows | Does ecosystem fit outweigh framework fashion? |
| LlamaIndex Workflows | Retrieval-heavy and data-centric systems | Is the real problem grounded data flow rather than orchestration alone? |
This table is intentionally about workflow shape. If you choose by hype, every framework can look similar. If you choose by the kind of state, tools, approvals, traces, and deployment path you need, the differences become much easier to see.
When LangGraph is the right default
Choose LangGraph when the agent loop itself needs to be designed. A support workflow, research pipeline, coding loop, or internal operations agent often has branches, state, tool failures, human checkpoints, and recovery paths. In those cases, a graph is not just ceremony. It is how the team makes the hidden agent loop reviewable.
The trade-off is structure. LangGraph is not the lightest first demo. It asks you to name nodes, transitions, and state. That can feel slower at first, but it is often better when the workflow will live longer than a prototype.
When CrewAI is a better fit
CrewAI is useful when the task naturally has roles. A research workflow may need a researcher, analyst, reviewer, and writer. A business automation may need an operator, checker, and reporter. The key word is naturally. If the human version of the work would not use those roles, adding agents may only add debugging cost.
Before using CrewAI, ask whether each role has a different input, output, and acceptance standard. If all agents are reading the same prompt and producing similar text, the system probably wants a simpler workflow.
When OpenAI Agents SDK is enough
OpenAI Agents SDK is attractive when you are already building on OpenAI models and want a compact way to organize agents, tools, handoffs, guardrails, and sessions. It is often a good path for product features that need a few agents and clear tool use, without building a custom orchestration runtime from scratch.
It is not automatically better than writing direct API logic. If you need complete ownership over scheduling, persistence, retries, and tool execution, explicit application code may be clearer.
When Google ADK or Mastra belongs on the shortlist
Google ADK deserves attention when your team is close to Gemini, Google Cloud, or managed enterprise deployment. The useful test is not whether a local demo runs. The useful test is whether ADK improves the path from local development to debugging, deployment, monitoring, and team operations.
Mastra deserves attention when your agent is part of a TypeScript product. Many AI features live inside Next.js, Node, React, SaaS dashboards, and product backends. For those teams, a TypeScript-first framework with workflows, memory, logging, tracing, evals, and MCP integration may reduce more friction than a Python-first research stack.
Use a representative workflow before choosing. Define the state you need to preserve, the tools the agent must call, the human approval points, the trace output, the deployment target, and the person who will maintain the system later. A framework that makes the first demo fast but hides the runtime loop may become expensive in production. A framework that asks for more structure upfront may be better if it makes failures easier to understand.
For a practical shortlist: choose LangGraph for complex stateful orchestration; CrewAI for role-based collaboration; OpenAI Agents SDK for compact OpenAI-native agent apps; Google ADK for Gemini and managed deployment; Mastra for TypeScript product apps; Microsoft Agent Framework for Azure, .NET, Python, and enterprise workflows; and LlamaIndex Workflows when retrieval and data pipelines dominate the agent's job.
A 30-minute framework test
Pick one real workflow and run it through the serious candidates. Do not use a toy demo. The test should include one tool call, one failure path, one human approval point, one trace review, and one deployment question. Record how much glue code you wrote, whether the trace explains failures, whether state is clear, and whether a teammate could maintain the workflow later.
The winner is not the framework that feels most magical. The winner is the one that makes your workflow controllable, inspectable, and maintainable after the demo is over.