Hey everyone! I was getting lost trying to find the right AI agents on HuggingFace (aren’t we all?), so I put together a basic alphanumeric classification system to organize them by complexity and use case.
Nothing fancy - just A1-A3 for agent types, B1-B4 for behavioral complexity, etc. Classified about 30 tools so far to test the concept.
Live demo: https://archiecur.github.io/huggingface-agent-classifier
Wondering if this kind of systematic organization would be useful for the community? Happy to expand the dataset if people find it helpful, or completely pivot if I’m solving the wrong problem.
Would love your thoughts!
Hey, this is really cool 
I think a classification system like this could be super helpful — HuggingFace can definitely feel overwhelming when you’re trying to find the right kind of agent for a specific use case. I like that your system focuses on both agent type and behavioral complexity; that’s a smart way to cut through the noise.
It might be interesting to see if you can also tag agents based on their intended audience or skill level (e.g., beginner-friendly vs. advanced), or even by integration ease (plug-and-play vs. requires custom dev). That could make the classification even more practical for different teams.
Overall, I’d say you’re solving a real problem here — I’d definitely use this as a quick reference when exploring new tools.
— Steve
Thank you Steve. I hadn’t thought about user level or plug and play.
Currently I’m gathering more data by continuing to validate the classification with more agents over the different axes. Let me see how I could work those suggestion in.
Again, thank you for taking a look and providing your thoughts.
Simplicity is key in a classification system for HuggingFace agents, as it enhances usability and adoption, especially for beginners like me, a first-year Computer Science student. A simple design reduces the learning curve, allowing users to focus on experimenting with AI models without unnecessary complexity. However, simplicity should not compromise functionality; the system must provide clear benefits, such as ranking agents based on performance or specific use cases (e.g., text generation or translation). I think it’s a great approach, but I’d love to know more about the classification metrics and how they integrate with the HuggingFace platform to maximize impact.
Thank you for your feedback. Those are good points. You are completely right.
I am working on adding exactly what you mentioned, a ‘function’ first level. It is a challenge. Currently there are 35 functions for HuggingFace Spaces- for instance code generation or text-to-speech. For perspective there are 600,000 Spaces There is no clear criteria for defining function, it maybe self selected by a user.
I have been experimenting with automating using README.md to try to get details that might be helpful. There also isn’t any standardization in README.md either. In HuggingFace both models and data sets have data cards. Spaces do not. It’s a journey but I’m not giving up.