Frameworks and Platforms for Building AI Agents

What is an agent framework?

An AI agent is a program that relies on a language model (the “engine” that understands and writes text) to reach a goal over several steps: it plans, calls tools, looks at the result and decides what to do next. Those tools can be a web search, a spreadsheet, a database or an API, which is simply a doorway that lets one piece of software query another.

An agent framework provides the scaffolding for all this: the reasoning loop, memory, tool connections, error recovery and, often, a way to trace what the agent is doing. You could code everything yourself on top of a model’s API. It is a great way to learn, but you would soon be rewriting what others have already battle-tested. A framework saves you time in exchange for its conventions and technical choices. That trade-off is the whole decision.

The explorer above brings together code libraries and visual platforms. GitHub stars, license and latest release date come from GitHub and are refreshed every time the site is rebuilt each week.

The main families

Code libraries

These are built for developers, usually in Python or TypeScript.

• LangGraph models an agent as a graph of steps. It suits projects where you want tight control over the flow and explicit human approval points.
• CrewAI organizes several agents into a “crew,” each with a role and tasks.
• AutoGen, from Microsoft Research, focused on conversations between multiple agents; the project is now archived and no longer maintained. Microsoft points users to Microsoft Agent Framework, which also succeeds Semantic Kernel.
• LlamaIndex started out connecting models to your documents (retrieval-augmented generation, or RAG) and now offers agents too.
• Pydantic AI emphasizes structured, validated outputs, which matters when an agent feeds other software.
• Model makers’ agent SDKs (software development kits published by the companies that build the models) integrate tightly with their own models, sometimes at the cost of portability to other models.

No-code and automation platforms

These let you assemble an agent visually by connecting blocks.

• n8n, Make and Zapier come from task automation (“when an email arrives, do this”) and have added AI steps and agents.
• Dify was designed from day one for apps built on language models: prompt chains, knowledge bases, agents. Flowise, from the same family, is at end of life: its team froze development in July 2026.

Some of these platforms can be self-hosted, meaning installed on your own server; others are only available as online services. The line between families is blurry: several visual tools accept code blocks, and some libraries ship a graphical interface.

The criteria that really matter

• Language: pick the one you or your team already know. A brilliant framework in an unfamiliar language slows you down more than it helps.
• Maturity: the latest release date shows whether the project is maintained. Young projects move fast and may change how things work between versions.
• License: a permissive open source license such as MIT or Apache allows broad commercial use; other licenses add conditions, especially if you plan to resell the tool as a service. Read it before building a product on top.
• Community: GitHub stars signal popularity, not quality. Also check the docs, the examples and how quickly reported issues get answered.
• Hosting: can you run everything yourself, or do you depend on a third-party service? This becomes critical if you handle personal or confidential data.
• Observability: the ability to see what the agent does step by step (traces, logs, costs). Without it, figuring out why an agent went wrong is guesswork.

MCP, explained simply

MCP (Model Context Protocol) is an open standard, introduced by Anthropic, for connecting an agent to tools and data sources. Think of it as a universal plug: instead of writing a separate connector for every “agent + tool” pair, the tool exposes an MCP server and any compatible agent can use it.

The benefit is twofold: you reuse existing connectors, and you can switch frameworks without rewriting everything. Check each tool’s documentation for MCP support. Keep in mind that an MCP server gives the agent real access to your files or accounts: only install servers you trust and keep permissions tight.

Tips for your first project

1. Pick a narrow, repetitive task whose output you can judge: sorting emails, summarizing documents, drafting replies.
2. Start simple: one agent, a few tools. Multi-agent setups add complexity that only pays off once the basics are solid.
3. Estimate costs before you launch: every step uses tokens, the units of text that model providers bill for. The AI agent cost calculator helps you plan ahead.
4. Keep a human in the loop for irreversible actions such as sending, paying or deleting. The Can I trust an agent with this? tool helps you assess the risk.
5. Write a handful of test cases and rerun them after every change to make sure the agent does not regress.

To structure your learning, follow the Build track. To keep up with new research and tools, check the weekly radar, and keep the AI glossary handy whenever a term trips you up.