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What they all have
Most frameworks come with the same core building blocks: support for different models, tools, memory, and RAG.
Most open-source frameworks are more or less model agnostic. This means they’re built to support various providers. However, as mentioned earlier, each framework has its own structure for system prompts — and that structure may work better with some models than others.
That’s also why you ideally want access to the system prompt and the ability to tweak it if needed.
All agentic frameworks support tooling, since tools are essential for building systems that can act. They also make it easy to define your own custom tools through simple abstractions. Today, most frameworks support MCP, either officially or through community solutions.
Fun illustration on what they usually always have | Image by author
It’s important to understand that not all models are built for function calling, which is necessary for using tools. To figure out which models are best suited as the base LLM, you can check Hugging Face’s agent leaderboard.
To enable agents to retain short-term memory between LLM calls, all frameworks make use of state. State helps the LLM remember what was said in earlier steps or parts of the conversation.
Most frameworks also offer simple options to set up RAG with different databases to provide the agent with knowledge.
Finally, nearly all frameworks support asynchronous calls, structured outputs, streaming, and the ability to add observability.
From: Agentic AI: Comparing New Open-Source Frameworks | by Ida Silfverskiöld | Data Science Collective | Apr, 2025 | Medium.
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