metadata
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
Agents Course Final Project
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.
##About The Project
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 and both Audo and Video Understanding via Geminivideo-understanding)
- Chess agent leveraging my board_to_fen fork and a Stockfish API.
- Langfuse setup boilerplate, a working example. This is an absolute must.
- Pydantic settings for type safety, centralized, and encapsulated config.
- Basic parallel agent task execution, compatible with Gradio, no extra abstractions.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference