Deep Agents Workshop — Interrupt 2026

A workshop on building a research agent from scratch with the Deep Agents framework. The repo ships both an interactive notebook that walks through the concepts step by step and a standalone agent wired up for LangSmith Studio.

What You'll Learn

• Creating a basic Deep Agent with built-in filesystem and planning tools
• Adding custom tools (web search via Tavily)
• Understanding backends: StateBackend, FilesystemBackend, StoreBackend, CompositeBackend
• Delegating work to subagents for context isolation
• Human-in-the-loop approval for sensitive operations
• Long-term memory with /memories/* routing across threads
• AGENTS.md for persistent agent identity (always loaded)
• Skills (SKILL.md) for on-demand capabilities via progressive disclosure

Setup

1. Clone the repo

git clone https://github.com/langchain-ai/interrupt26-deepagents.git
cd interrupt26-deepagents

2. Install dependencies

uv sync

3. Configure environment

cp .env.example .env

Fill in your API keys in .env. At minimum:

• ANTHROPIC_API_KEY (or swap providers — see utils/models.py)
• TAVILY_API_KEY — free at tavily.com

Optional but recommended:

• LANGSMITH_API_KEY + LANGSMITH_TRACING=true for full trace observability

Run it

A. Walk through the notebook

uv run jupyter notebook

Open deep_agent.ipynb and run the cells top to bottom. The 8 parts each take ~30s to a couple minutes to execute.

B. Run the agent in LangSmith Studio

The repo ships a production-shaped agent at agent/agent.py, wired up via langgraph.json. Start the local LangGraph API + Studio with one command:

uv run langgraph dev

You'll see something like:

- 🚀 API:        http://127.0.0.1:2024
- 🎨 Studio UI:  https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Open the Studio URL in a browser. The Deep Agent graph appears in the sidebar. From there you can:

• Chat with the agent and watch each tool call land in real time
• Inspect intermediate state, the virtual filesystem, and the agent's todo list
• Step through threads, fork them, and edit messages mid-conversation
• See /memories/* files persist across threads — langgraph dev provides the checkpointer + store automatically

When you're ready to deploy, langgraph.json is already shaped for LangSmith Deployments — you can use our langgraph-cli to deploy your agent directly from your terminal using uv run langgraph deploy

Model providers

The default model is Anthropic (claude-haiku-4-5). To switch, edit utils/models.py — commented-out sections are included for OpenAI, Azure OpenAI, AWS Bedrock, and Google Vertex AI (Gemini). For non-default providers, install the matching extra:

uv sync --extra azure     # Azure OpenAI
uv sync --extra bedrock   # AWS Bedrock
uv sync --extra vertex    # Google Vertex AI