AI AGENTS

Conversational model-fit advisor

In a chat, an agent takes your task requirements, evaluates relevant HuggingFace models against them.

CategoryAI Agents
Enginepaperclip
Difficultybeginner
Triggerchat
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerChat message with requirements
  • ActionSearch candidate modelsHugging FaceHugging Face
  • ActionRead top candidate cardsHugging FaceHugging Face
  • ActionWeigh candidates and pickOpenAI
  • OutputReply with recommendation in chat

What it does

Acts as an on-call model-selection advisor in chat. You describe the task and constraints; the agent researches matching HuggingFace models, reasons about fit, and answers with a concrete recommendation, the runner-up, and why.

When to use it

Use for fast, interactive decisions — an engineer asking "what should I use for multilingual summarization under 3B params and a permissive license?" and wanting a defensible answer in seconds, with follow-up questions welcome.

How it works

  1. 1A chat message with the task requirements triggers the agent.
  2. 2The agent searches HuggingFace for candidate models that match the stated task and constraints.
  3. 3It reads the cards of the top candidates to gather license, size, and evaluation details.
  4. 4An LLM weighs the candidates against the requirements and selects a primary recommendation plus an alternative.
  5. 5The agent replies in chat with the pick, its tradeoffs, and a short usage snippet, ready for follow-up questions.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

Run this workflow in your colony.

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