Cut through the noise — these are the most practical, proven tools for building AI agents fast.
If you want to build AI agents fast without reinventing the wheel, agent frameworks are the way to go. They handle the boilerplate code and come packed with built-in features like memory, execution, and tool integrations.
Frameworks help prototype quickly, scale multi-agent systems, and simplify development — especially in Python, the go-to language for AI. Here are seven widely-used options:
LangGraph (LangChain) builds agents as node-based graphs. It offers deep integrations and memory tools, but relies on the buggy LangChain base.
AutoGen (Microsoft) is a robust open-source tool with a low-code studio. It’s reliable but has a smaller community.
CrewAI lets agents roleplay in “crews” with defined roles, ideal for enterprises. It’s simple to start but lacks advanced tools.
SmolAgents (Hugging Face) is minimalist and powerful via code-as-action. It’s highly flexible, but limited in features and sometimes unpredictable.
OpenAI Agents SDK uses task handoffs between agents, with strong OpenAI support. It’s simple but less flexible than LangChain or LlamaIndex.
Agent Development Kit (Google) offers deep enterprise integrations, especially for Google Cloud, but has a steeper learning curve.
LlamaIndex evolved from data-focused RAG to support agents. It shines in external data use cases but isn’t as suited for pure agent workflows.
While the landscape keeps shifting, each framework serves similar core needs. Choose one and start building — they all offer a solid foundation.
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