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Free AI Agent Framework Picker Online
Find the right AI agent framework for your project. Answer 12 questions about your team, language, control, hosting, observability, and QA needs to get a ranked recommendation across 11 stacks, from n8n and CrewAI to LangGraph. It is brought to you by TestMu AI (formerly LambdaTest), the team behind a unified software testing platform.
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Who will build and maintain the agent?
What are you building first?
Point-and-click automation with the largest app ecosystems. Fastest path for non-technical teams automating business workflows, billed per task or operation.
Best for: Non-technical teams automating SaaS workflows.
n8n (75% match): Technical teams that want visual workflows with code control.
Dify (69% match): Self-hosted visual building with built-in RAG.
What is an AI Agent Stack Picker?
An AI agent stack picker is a decision tool that recommends which framework or platform to build your AI agent with. It turns 12 questions about your team, language, control needs, hosting, quality practices, and budget into a ranked recommendation across 11 stacks, from no-code automation to code-first frameworks.
The comparison follows the criteria in LangChain's AI agent frameworks guide: prototyping speed, production reliability, observability, ecosystem integrations, and language fit. Prefer a no-code, describe-your-agent approach? The AI Agent Stack Selector recommends a full no-code stack from a plain-English brief. Once you pick a framework, design the workflow in the AI Agent Workflow Builder and export starter code for it.
How does the AI Agent Stack Picker work?
The picker uses a transparent scoring model that runs entirely in your browser. No data is uploaded. Here is how the recommendation is computed:
- Fit scores: each of the 11 stacks holds a 0 to 3 fit score for every answer option across all 12 questions.
- Live ranking: your answers are summed per stack and normalized to a match percentage, re-ranked on every change.
- Explained result: the top stack is shown with its type, language, hosting model, and a plain-English reason it fits.
- Runners-up on purpose: the second and third stacks stay visible because a near-tie means either would work for your answers.
- No universal winner: the right stack follows your workflow complexity, data sensitivity, approvals, and team skill, so the same tool recommends differently for different teams.
How to use the AI Agent Stack Picker?
Getting a recommendation takes about 60 seconds and requires no signup. Follow these steps:
- Answer the 12 questions: Pick one option per question from its dropdown. Questions appear two at a time across six slides, and answering the second question advances the carousel automatically.
- Watch the ranking update: The result panel below the questions re-ranks all 11 stacks with every answer, showing a match bar for each.
- Read the top recommendation: The best-fit stack is explained with what it is, why it matches your answers, and what it is best for, plus two runners-up.
- Compare the alternatives: Scan the full ranking to see how close the alternatives score, since a near-tie means either stack would work.
- Copy or download the report: Use the copy and download icons beside the result panel to export a Markdown summary of your answers and the full ranking.
Which AI agent frameworks does the stack picker compare?
The picker covers 11 stacks spanning no-code, hybrid, and code-first approaches. Each entry below is what the tool knows it is best at:
- Zapier or Make: point-and-click no-code automation with the largest SaaS app ecosystems.
- n8n: visual workflows with native AI agent nodes, code steps, and free self-hosting.
- Dify: open-source, self-hostable LLM app platform with strong built-in RAG.
- CrewAI: role-based multi-agent teams in Python, fast to prototype, with MCP and Ollama support.
- LangGraph: low-level graph orchestration with durable state and human-in-the-loop interrupts for production.
- OpenAI Agents SDK: minimal Python SDK for assistants and handoffs with built-in tracing.
- Claude Agent SDK: Anthropic's SDK for long-running agentic coding and research on Claude models.
- Microsoft Agent Framework: the unified successor to AutoGen and Semantic Kernel for Azure and .NET shops.
- Google ADK: batteries-included Python runtime with a debugging UI for Google Cloud teams.
- LlamaIndex Workflows: event-driven orchestration for document-heavy, data-intensive pipelines.
- Mastra: batteries-included TypeScript platform with memory, workflows, and a local studio.
An observability layer is part of the stack too. When your answers show real tracing or evaluation needs, the result also suggests LangSmith, which works across all major frameworks, or Langfuse, the open-source and self-hostable alternative, based on your hosting preference. The model provider matters as well: compare options in the AI Agent Comparison tool, and look up any unfamiliar terms in the AI Agent Glossary.
Difference between no-code, hybrid, and code-first agent stacks
Most stack decisions come down to which of three families fits your team and workflow. The picker weighs your answers across all three:
| Family | Examples | Strengths | Limits |
|---|---|---|---|
| No-code | Zapier, Make | Fastest setup, huge app libraries, no engineers needed. | Per-task pricing at scale, limited custom logic and permissions. |
| Hybrid | n8n, Dify | Visual building plus code steps, self-hosting, cheaper at volume. | Steeper learning curve than pure no-code tools. |
| Code-first | LangGraph, CrewAI, SDKs, Mastra | Full control, evaluations, approvals, production reliability. | Needs engineers to build and maintain the system. |
Use cases of the AI Agent Stack Picker
Use the picker whenever a new agent project starts or an existing stack starts to hurt. Common situations include:
- Starting a new agent project: get a defensible starting stack before writing code, and attach the report to the design doc.
- Settling team debates: answer the questions together in planning so the framework choice rests on requirements, not opinions.
- QA and test automation: teams adopting AI-native testing agents such as Kane AI by TestMu AI can apply the same criteria when choosing how to build custom agents around their pipeline.
- Outgrowing no-code: when a Zapier or Make workflow hits pricing or permission limits, rerun the picker with your new answers to find the upgrade path.
- Before a risk review: once the stack is chosen, score the planned agent with the AI Agent Risk Scorer and write its system prompt with the AI Agent Prompt Generator. To work the other way round, starting from a monthly spend and seeing which stack fits inside it, use the AI Agent Budget Planner.
Frequently Asked Questions (FAQs)
What is an AI agent stack?
An AI agent stack is the set of tools used to build and run an agent: the orchestration framework or no-code platform, the model provider, the hosting setup, plus memory, integrations, and observability. Choosing the framework is usually the first and most consequential decision in an agent project.
Do I need an AI agent framework, or can I just call the model API directly?
For a single agent that calls a few tools, a plain API loop is often enough and takes about an hour to build. A framework earns its place once you need state, checkpointing, retries, human approval, or multiple coordinated agents. Many teams start with raw API calls and adopt a framework as complexity grows.
What is the difference between an AI agent SDK and a framework?
An SDK is a set of low-level building blocks you assemble yourself, such as the OpenAI Agents SDK or Claude Agent SDK. A framework like LangGraph or CrewAI adds opinionated structure, orchestration, and state handling on top. Choose an SDK for bespoke control and a framework for faster, structured builds.
What is the difference between LangGraph and CrewAI?
CrewAI uses high-level, role-based abstractions that make multi-agent teams fast to prototype in Python. LangGraph gives low-level graph control over state, durable execution, and human-in-the-loop interrupts, which suits production systems. Teams often prototype in CrewAI and move to LangGraph as control and reliability needs grow.
Which AI agent framework is best for production?
LangGraph is a common choice for production agents because it offers durable execution, explicit state, checkpointing, and human approval gates, with LangSmith for tracing. Microsoft Agent Framework fits Azure and .NET shops, and Google ADK fits Gemini-first teams. Match the framework to your control, hosting, and compliance needs.
Do I need a multi-agent system, or is a single agent enough?
A single agent is faster to build, cheaper to run, and correct for most straightforward tasks. Move to multiple agents when one agent gets overloaded or the work splits into distinct roles such as researcher, writer, and reviewer. Multi-agent systems add coordination and latency overhead, so scale up only when the task demands it.
Should I use Python or TypeScript to build AI agents?
Python has the widest framework and machine-learning ecosystem, so most agent frameworks target it first, including CrewAI, LangGraph, and LlamaIndex. TypeScript keeps one language across your web stack and suits real-time or voice agents, where Mastra is the leading native option. Several frameworks, such as LangGraph, ship both.
Which AI agent framework is best for RAG or document-heavy agents?
LlamaIndex Workflows is the strongest fit when an agent mostly ingests, indexes, and queries documents or databases, because retrieval is its core. Dify is a good no-code option with built-in RAG pipelines. Choose these when your agent is largely a smart query layer over your own data.
When should I switch from a no-code tool like n8n or Zapier to a code framework?
Switch when the agent must reason, plan, or adapt on its own, when logic outgrows visual nodes, or when per-task pricing gets expensive at scale. No-code platforms handle most standard automations well. A common path is to prototype in n8n or Zapier, then rebuild the reasoning core in a code framework.
LangSmith or Langfuse: which observability tool should I add?
LangSmith is the tightest fit for LangChain and LangGraph stacks and is quick to start. Langfuse is open-source and framework-agnostic, making it the better self-hosted choice for data-residency or compliance needs. Both give step-level tracing and evaluations, and this picker suggests one based on your hosting preference.
Should I self-host my AI agent stack or use a managed platform?
Self-hosting looks cheaper on paper and keeps data in your environment, which matters for compliance. Managed platforms usually win once you price in maintenance and ops time, while self-hosting pays off at scale with a dedicated team. n8n and Dify support self-hosting, whereas the SDKs and Zapier are managed.
Can I combine more than one AI agent stack?
Yes, and production teams often do. A common pattern is n8n or Make for surrounding business automation with a code framework such as LangGraph handling the agent core, or a no-code prototype that later moves to code. Treat the recommendation as your primary stack, not your only tool.
Is my data uploaded when I use this tool?
No. All processing happens in your browser. Your answers, the computed ranking, and any report you copy or download never leave your device, and nothing is stored between visits. You can describe internal projects and constraints without sharing them with TestMu AI or any third party.
Is the AI Agent Stack Picker free to use?
Yes. The AI Agent Stack Picker is completely free, with no signup, no email gate, and no usage limits. Every feature, including the full 11-stack ranking and the Markdown report export, is available to everyone. It is maintained by TestMu AI as part of its free online tools collection.
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