Modular Workshops

This repository contains hands-on tutorials for learning LangChain, LangGraph, and Deep Agents.

This is a condensed version of LangChain Academy, intended to be run in a session with a LangChain engineer. If you're interested in going into more depth, or working through tutorials on your own, check out LangChain Academy! LangChain Academy has helpful pre-recorded videos from our LangChain engineers.

Prerequisites

• Python 3.11+
• uv (recommended) or pip

Setup

# 1. Install dependencies
uv sync

# 2. Configure environment variables
cp .env.example .env
# Edit .env and fill in your keys

Key	Required for	Get one
OPENAI_API_KEY	Modules 1-4 (default model)	https://platform.openai.com
LANGSMITH_API_KEY	Modules 3 & 4 (recommended for all)	https://smith.langchain.com
LANGSMITH_API_KEY_GATEWAY / WORKSPACE_ID	Module 3 §1 (LangSmith Gateway policies)	same key as LANGSMITH_API_KEY; workspace ID from LangSmith Settings → Workspace
TAVILY_API_KEY	Modules 2 & 3 (web search tool)	https://tavily.com

# 3. Start Jupyter
uv run jupyter notebook

Open whichever module(s) your recipe calls for.

Switching Models

All modules import model from utils/models.py. Change one line there to swap providers — no notebook edits required.

# utils/models.py

# OpenAI (default)
model = init_chat_model("openai:gpt-4.1-mini")

# Anthropic
# model = init_chat_model("anthropic:claude-sonnet-4-5")

# Azure OpenAI
# from langchain_openai import AzureChatOpenAI
# model = AzureChatOpenAI(azure_deployment="gpt-4.1-mini", streaming=True)

# AWS Bedrock
# from langchain_aws import ChatBedrockConverse
# model = ChatBedrockConverse(provider="anthropic", model_id="...")

utils/models.py also ships a commented-out LangSmith Gateway block. Module 3 §1.4 walks through flipping the default to it so every model call (notebooks and the deployed agent) is routed through the gateway and subject to workspace policies.

Deploy + Govern (Module 3)

Module 3 first creates a workspace-level LangSmith Gateway policy (PII / secrets redaction), routes the model through the gateway, then deploys the agent at agents/deep_agent/ to LangSmith via the langgraph CLI (installed by uv sync). The deploy config is langgraph.json at the workshop root.

Because agents/deep_agent/agent.py imports model from utils.models, whichever block is active in utils/models.py at deploy time is what ships — flip on the gateway block and the deployed agent inherits it with no extra flags.

Your LANGSMITH_API_KEY must have deployment permissions (use a lsv2_sk_... service key). The gateway block reads LANGSMITH_API_KEY_GATEWAY (the same key under a non-reserved name, since langgraph deploy strips LANGSMITH_API_KEY during upload).

Project Structure

modular-workshops/
├── README.md                       (this file — recipes + setup)
├── pyproject.toml                  (shared dependencies)
├── .env.example
├── langgraph.json                  (registers agents/deep_agent for langgraph dev)
├── utils/
├── agents/
│   ├── research_agent.py           (shared agent factory — Module 2 references, Module 4 imports for eval)
│   └── deep_agent/                 (deployable + governed agent for Module 3)
│       ├── agent.py
│       ├── AGENTS.md
│       └── skills/
│           ├── linkedin-post/SKILL.md
│           └── twitter-post/SKILL.md
├── images/                         (diagrams used by the notebooks)
└── modules/
    ├── 01_langgraph.ipynb          (Module 1)
    ├── 02_deep_agents.ipynb        (Module 2)
    ├── 03_deploy_and_govern.ipynb  (Module 3)
    └── 04_langsmith.ipynb          (Module 4)

Common Issues

langgraph deploy fails with 403 / permission denied Your API key is a personal token. Generate a service key (lsv2_sk_...) in LangSmith settings.

Notebook can't find utils / agents Each module's setup cell prepends project_root (the workshop root) to sys.path. If you moved a notebook, update the Path().resolve().parent line to point at the workshop root.

For LangChain Internal Users

Please refer to this linked Notion document for instructions on setup and usage.