Build a Research Agent with Deep Agents

Deepagents is a simple, open source agent harness built by LangChain. It uses some common principle seen in popular agents such as Claude Code and Manus, including planning (prior to task execution), computer access (giving the able access to a shell and a filesystem), and sub-agent delegation (isolated task execution). We're introducing a new repo with a collection of quickstarts that demonstrate different agents can be easily configured on top of the deepagents harness.

Quickstarts repo:
https://github.com/langchain-ai/deepagents-quickstarts

Learn how to build Deep Agents on LangChain Academy:
https://academy.langchain.com/courses/deep-agents-with-langgraph/?utm_medium=social&utm_source=youtube&utm_campaign=q4-2025_youtube-academy-links_aw

deepagents repo:
https://github.com/langchain-ai/deepagents

deepagents docs:
https://bit.ly/480icl1

deepagents UI:
https://github.com/langchain-ai/deep-agents-ui

Chapters -- 
0:00 Introduction to DeepAgents
1:00 Agent Trajectory Overview
2:00 Built-in Tools in DeepAgents
3:00 Quick Start Setup Options
4:00 Task-Specific Tools: Search & Think
5:00 Task-Specific Instructions
6:00 Preventing Agent Spin Out
7:00 Custom Prompts & Instructions
8:00 Custom Subagents for Context Isolation
9:00 Workflow Instructions & Delegation Strategy
10:00 Initializing DeepAgent
11:00 Middleware Overview
12:00 Running the Agent in Notebook
13:00 File System Backends
14:00 Subagent Research Output
15:00 LangSmith Tracing
16:00 Deploying with LangGraph Server
17:00 Summary & Key Takeaways