README
Agent Workshop
A hands-on FastAPI application that walks through the building blocks of AI agents — from stateless LLM calls to full agentic loops — step by step.
Features
| Module | What it demonstrates |
|---|---|
| Stateless LLM | Single, context-free call to the model (Anthropic or OpenAI) |
| Memory | Full conversation history passed on every request, with Claude compaction on supporting models |
| Tools | Model-driven tool use (calculator, datetime) — works on both providers |
| Web Search | Live grounding via Claude's built-in web_search server tool |
| RAG | Retrieve → Augment → Generate with in-memory employee data |
| Agent Mode | Compose Tools + Web Search + RAG into a single agentic loop |
| Planning | Zero-shot, few-shot, chain-of-thought, decomposition, ReAct |
| Agent in Action | Multi-step stock analysis agent (Plan → Execute → Synthesise → Verify), with live progress and risk-tolerance tuning |
| Frameworks | Overview + interactive decision guide comparing agent frameworks (LangGraph, Pydantic AI, LlamaIndex, and more) |
| Tokenizer | Interactive tokenizer playground — see how prose, code, JSON, multilingual (including RTL/Hebrew) and emoji text gets split into tokens across GPT, open models (DeepSeek, Qwen, Llama, Mistral, GLM) and Claude |
Workshop UX
- Provider & model switcher — pick Anthropic or OpenAI models per-request from a grouped dropdown; the choice threads through every endpoint (
/chat,/plan,/agent). - Missing API key warning — a
⚠️ No API keybadge appears next to the model picker whenever the selected provider's key isn't set in.env; calling it anyway returns a clear, friendly error instead of a raw SDK exception. - Cost & latency badges — every call shows its estimated USD cost (from per-model pricing) and round-trip latency.
- Peek under the hood — a collapsible panel showing the exact system/messages/tools payload sent to the model, including any injected RAG or web-search context.
- Context window meter — a live sidebar gauge of how much of the context window the current conversation is using.
- Export / Clear conversation — download the full chat transcript as Markdown at any point, or wipe the conversation and start fresh. Export keeps the whole session's history even if you toggle Memory mid-conversation.
- "What is an Agent?" / Control Loop pages — standalone interactive explainers for agent concepts, separate from the live chat demos.
- Reasoning effort selector — for models that support it, choose the effort/reasoning level per-request; it's threaded through every LLM call (stateless, memory, tools, agent, planning).
Screenshots
Workshop home — Stateless LLM step:
"What is an Agent?" — deterministic vs. non-deterministic execution:
Planning — Task Decomposition:
Tokenizer playground — right-to-left (Hebrew) prompt:
Tokenizer playground — English prompt:
Quick Start
1. Clone the repo
git clone https://github.com/YOUR_USERNAME/agent_workshop.git
cd agent_workshop
2. Configure your API key(s)
cp .env.example .env
# Edit .env and add your Anthropic and/or OpenAI API key(s)
You only need to set the key for the provider(s) you plan to use — the model picker in the UI shows a ⚠️ No API key warning next to any provider whose key isn't configured, and picking a model from that provider returns a clear error instead of a confusing one.
The .env file is listed in .gitignore and is never committed to the repository.
3. Start the server
./start.sh
The script will:
- Create a Python virtual environment (
.venv) if one does not exist - Install all dependencies from
requirements.txt - Start the FastAPI server at
http://127.0.0.1:8000 - Open the UI in your browser automatically
Share mode (ngrok)
To give participants a public URL:
./start.sh --share
Requires ngrok to be installed and authenticated.
Environment Variables
| Variable | Default | Description |
|---|---|---|
ANTHROPIC_API_KEY | (optional) | Your Anthropic API key — required to use Claude models |
ANTHROPIC_MODEL | claude-sonnet-5 | Default model used when no model is selected |
OPENAI_API_KEY | (optional) | Your OpenAI API key — required to use GPT models |
Choosing a Provider
Every module's model picker groups models by provider (Anthropic / OpenAI).
Switch providers at any time from the dropdown — the backend automatically
routes the request (including tool calls, memory, and structured JSON output)
to the right SDK. Anthropic-only features (context-management/compaction
betas, the built-in web_search server tool) transparently fall back to a
plain call when an OpenAI model is selected.
Check GET /config to see the model catalog and which providers currently
have a configured API key.
Sensitive Data Notice
- API keys are loaded exclusively from the
.envfile, which is gitignored. No secrets are ever committed to the repository. - The employee records in
rag.pyare entirely fictional demo data invented for the RAG workshop module. They do not represent real people.
Contributors
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40.5%
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README
Agent Workshop
A hands-on FastAPI application that walks through the building blocks of AI agents — from stateless LLM calls to full agentic loops — step by step.
Features
| Module | What it demonstrates |
|---|---|
| Stateless LLM | Single, context-free call to the model (Anthropic or OpenAI) |
| Memory | Full conversation history passed on every request, with Claude compaction on supporting models |
| Tools | Model-driven tool use (calculator, datetime) — works on both providers |
| Web Search | Live grounding via Claude's built-in web_search server tool |
| RAG | Retrieve → Augment → Generate with in-memory employee data |
| Agent Mode | Compose Tools + Web Search + RAG into a single agentic loop |
| Planning | Zero-shot, few-shot, chain-of-thought, decomposition, ReAct |
| Agent in Action | Multi-step stock analysis agent (Plan → Execute → Synthesise → Verify), with live progress and risk-tolerance tuning |
| Frameworks | Overview + interactive decision guide comparing agent frameworks (LangGraph, Pydantic AI, LlamaIndex, and more) |
| Tokenizer | Interactive tokenizer playground — see how prose, code, JSON, multilingual (including RTL/Hebrew) and emoji text gets split into tokens across GPT, open models (DeepSeek, Qwen, Llama, Mistral, GLM) and Claude |
Workshop UX
- Provider & model switcher — pick Anthropic or OpenAI models per-request from a grouped dropdown; the choice threads through every endpoint (
/chat,/plan,/agent). - Missing API key warning — a
⚠️ No API keybadge appears next to the model picker whenever the selected provider's key isn't set in.env; calling it anyway returns a clear, friendly error instead of a raw SDK exception. - Cost & latency badges — every call shows its estimated USD cost (from per-model pricing) and round-trip latency.
- Peek under the hood — a collapsible panel showing the exact system/messages/tools payload sent to the model, including any injected RAG or web-search context.
- Context window meter — a live sidebar gauge of how much of the context window the current conversation is using.
- Export / Clear conversation — download the full chat transcript as Markdown at any point, or wipe the conversation and start fresh. Export keeps the whole session's history even if you toggle Memory mid-conversation.
- "What is an Agent?" / Control Loop pages — standalone interactive explainers for agent concepts, separate from the live chat demos.
- Reasoning effort selector — for models that support it, choose the effort/reasoning level per-request; it's threaded through every LLM call (stateless, memory, tools, agent, planning).
Screenshots
Workshop home — Stateless LLM step:
"What is an Agent?" — deterministic vs. non-deterministic execution:
Planning — Task Decomposition:
Tokenizer playground — right-to-left (Hebrew) prompt:
Tokenizer playground — English prompt:
Quick Start
1. Clone the repo
git clone https://github.com/YOUR_USERNAME/agent_workshop.git
cd agent_workshop
2. Configure your API key(s)
cp .env.example .env
# Edit .env and add your Anthropic and/or OpenAI API key(s)
You only need to set the key for the provider(s) you plan to use — the model picker in the UI shows a ⚠️ No API key warning next to any provider whose key isn't configured, and picking a model from that provider returns a clear error instead of a confusing one.
The .env file is listed in .gitignore and is never committed to the repository.
3. Start the server
./start.sh
The script will:
- Create a Python virtual environment (
.venv) if one does not exist - Install all dependencies from
requirements.txt - Start the FastAPI server at
http://127.0.0.1:8000 - Open the UI in your browser automatically
Share mode (ngrok)
To give participants a public URL:
./start.sh --share
Requires ngrok to be installed and authenticated.
Environment Variables
| Variable | Default | Description |
|---|---|---|
ANTHROPIC_API_KEY | (optional) | Your Anthropic API key — required to use Claude models |
ANTHROPIC_MODEL | claude-sonnet-5 | Default model used when no model is selected |
OPENAI_API_KEY | (optional) | Your OpenAI API key — required to use GPT models |
Choosing a Provider
Every module's model picker groups models by provider (Anthropic / OpenAI).
Switch providers at any time from the dropdown — the backend automatically
routes the request (including tool calls, memory, and structured JSON output)
to the right SDK. Anthropic-only features (context-management/compaction
betas, the built-in web_search server tool) transparently fall back to a
plain call when an OpenAI model is selected.
Check GET /config to see the model catalog and which providers currently
have a configured API key.
Sensitive Data Notice
- API keys are loaded exclusively from the
.envfile, which is gitignored. No secrets are ever committed to the repository. - The employee records in
rag.pyare entirely fictional demo data invented for the RAG workshop module. They do not represent real people.
Similar repos
Contributors
Languages
HTML
40.5%
JavaScript
22.9%
CSS
19.6%
Python
15.8%
Shell
1.1%