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---
title: Final Assignment
emoji: 🕵🏻‍♂️
colorFrom: indigo
colorTo: indigo
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_expiration_minutes: 480
license: mit
---
<br />
<div align="center">
  <a href="https://huggingface.co/learn/agents-course/unit4/introduction">
    <img src="images/unit-4.jpg" alt="Hugging Face Agents Course" width="800">
  </a>

  <h3 align="center">Agents Course Final Project</h3>

  <p align="center" style="width:50%">
    Final hands-on assignment for the Hugging Face Agents course. In this project I built a multi-agent solution, evaluated it against questions from the General AI Assistants (GAIA) benchmark (level one only), and got creative with some agent and tool improvements. 
</div>

##About The Project

![[HF Space Screen Shot][./images/submit_answers.jpg]](https://huggingface.co/spaces/civerson916/Final_Assignment_Template)

Achieving 30 points for the certification was relatively easy with the template provided and a powerful enough LLM. However, evaluation revealed the unique types of implementation challenges with AI agents. Some of which include...

* Cost
* Response Times
* Reliability

Beyond what looks like a smolagents guided tour, you can find the following in this repo... 

* Research agent armed with Google search via [Serper](https://serper.dev/) and both Audo and Video Understanding via [Gemini](https://ai.google.dev/gemini-api/docs/)video-understanding)
* Chess agent leveraging my [board_to_fen](https://github.com/civerson/board_to_fen) fork and a Stockfish API.
* [Langfuse](https://langfuse.com/) setup boilerplate, a working example. This is an absolute must.
* [Pydantic](https://docs.pydantic.dev/latest/) settings for type safety, centralized, and encapsulated config.
* Basic parallel agent task execution, compatible with [Gradio](https://www.gradio.app/), no extra abstractions.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference