README.md

metadata

title: Agent 101
emoji: 🤖
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
python_version: '3.11'
pinned: false
license: apache-2.0
short_description: LLM side-by-side with and without tool access.

Agent 101

A tiny demo to show what "giving an LLM tools" actually does.

Both sides of the app call the same model via the HF Inference Providers API (default: meta-llama/Llama-3.1-8B-Instruct). The left side runs the model plain. The right side hands it a small toolkit (calculator, weather lookup, unit converter, course-notes search, CS/ML dictionary) and runs an agent loop.

Try a question the plain LLM can't answer — e.g. What is 4729 times 8314? or How much hotter is Delhi than Bangalore right now?. The plain model will guess or refuse; the agent will reach for the right tool, execute it, and answer from the real data.

Because inference is delegated to HF Inference Providers, this Space runs fine on free CPU Basic hardware — no GPU required.

Based on the Week 12 lab for CS 203 (Software Tools and Techniques for AI) at IIT Gandhinagar. The full Colab is at lecture-demos/week12/colab-notebooks/01-agents-from-scratch.ipynb in the course repo.

Running locally

pip install -r requirements.txt
export HF_TOKEN=hf_...
python app.py

To swap the backing model:

export MODEL_ID="Qwen/Qwen2.5-72B-Instruct"   # or any tool-capable chat model

Deploy to HF Spaces

git init
git remote add origin https://huggingface.co/spaces/Nipun/agent-101
git add .
git commit -m "Initial commit: Agent 101"
git push -u origin main

Then set the HF_TOKEN secret in the Space's Settings → Variables and Secrets. CPU Basic (free) is enough — no GPU needed.