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Building an Agentic Chatbot with Weave

Goal

Using our own products regularly helps us better empathize with and understand our users' needs. This repo provides a streamlined guide to experience how Weave works in a typical AI development workflow.

Go from zero to a production-deployed support bot with systematic evaluation, real-time monitoring, and continuous improvement.

Project

Build a support bot for Weights & Biases that can:

  • Answer questions about our product (from our docs)
  • Create and give updates on support tickets

Your Task

Get this bot ready for production. Going from 0 to demo is easy, but can you build an agent ready to face real customer questions? Discover:

  • Where Weave shines in the development process
  • What features are intuitive vs. confusing
  • What's missing or could be improved

Prerequisites

  • Python 3.12+ environment
  • GitHub to clone the repo
  • Terminal access to run commands
  • Modal account (free) for serverless deployment (sign up here)
    • Used to deploy your agent server (both development and production)
    • Free tier includes generous compute credits
  • Weights & Biases account (sign up free)
    • Your W&B API key is used for both Weave observability AND the LLM API (we use W&B Inference with DeepSeek)

Getting Started 🚀

1. Clone the repository

git clone https://github.com/wandb/agentic-support-bot-demo.git
cd agentic-support-bot-demo

2. Install dependencies

We use uv for fast, reliable Python package management.

# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install project dependencies (includes Marimo)
uv sync

3. Launch the Interactive Guide

uv run marimo run marimo-guide.py

The guide will open in your browser and walk you through the entire tutorial.


What You'll Learn

The interactive guide covers:

  1. Project Setup - Configure environment variables and workspace
  2. Basic Agent - Get a minimal agent running with Weave tracing
  3. Tools & MCP - Add capabilities and connect to documentation search
  4. Iteration - The core Weave workflow: observe → diagnose → fix → verify
  5. Evaluation - Build systematic evaluation with datasets and scorers
  6. Deployment - Deploy to Modal for production
  7. Monitoring & Guardrails - Add safety controls and quality tracking

Resources


License

See LICENSE file for details.

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A streamlined guide to experience how Weave works in a typical AI development workflow.

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