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