About vs. Demo: This demo folder now uses a standardized launch page. Start with About Demo, then choose the run mode that fits your class.

AI/ML Workflows • Browser • 20-30 min

Copilot Kit

Based on **LLM function calling** and **multi-step reasoning** where AI agents can use tools and plan actions.

Open About Demo Read README View Course Path
Assistant: Hello! I'm an AI agent powered by CopilotKit. I can help you with calculations, answer questions, and run tools. Try asking me something!

CopilotKit Capabilities

  • Streaming: Real-time response generation
  • Tools: Function calling for calculations
  • Agents: Multi-step reasoning workflows
  • Human-in-the-loop: Intervene when needed

Try These Examples

  • "Calculate the Black-Scholes price for S=100, K=105, T=0.5, r=0.05, σ=0.2"
  • "What's the portfolio variance for 60% stocks (20% vol) and 40% bonds (5% vol)?"
  • "Explain risk parity and why it's better than equal weighting"

Learn More: https://github.com/copilotkit/copilotkit

Standard demo guide

Use this demo in a logical learning sequence

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

What this demo is about

Based on **LLM function calling** and **multi-step reasoning** where AI agents can use tools and plan actions.

Learning objectives

  • Explain the main ai/ml decision that Copilot Kit is designed to support.
  • Change input assumptions and predict how the output should respond before running the demo.
  • Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

  • Supported modes: Browser
  • Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

  • Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
  • Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

  • Use the main run or simulate action to compute the scenario after inputs are set.
  • Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

  • Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
  • Students should explain whether the output is descriptive, predictive, simulated, or recommended.
  • Look for the prompt, retrieved context, generated response, and user feedback loop
  • Observe how response quality changes with clearer instructions or better context

Available run modes

  • Browser: available for this demo.

How to proceed

  1. Choose the run mode that fits the class: Browser.
  2. Review the default assumptions before changing anything.
  3. Change one or two inputs, then use `Run the main action`.
  4. Read the output first, then compare any supporting metrics, charts, or AI text.
  5. Capture one insight, one limitation, and one action recommendation.