How LangChain Deep Agents Actually Work (Execution Environment Deep Dive) | Part 1
Building Saas
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How LangChain Deep Agents Actually Work (Execution Environment Deep Dive) | Part 1
317 просмотров · 2 месяца назад
Building Saas
110 подписчиков
317 просмотров · 2 месяца назад
LangChain's Deep Agents SDK gives you a batteries-included agent harness — built-in tools, file systems, memory, subagents, and human-in-the-loop, all on top of LangGraph. In this video, we build a deep agent from scratch and go deep into the execution environment: tools, backends, permissions, and sandboxes.
🔧 What we cover:
What deep agents are and how they differ from a plain LangGraph agent
Setting up a project with uv, deepagents, and Tavily search
Building a custom tool and wiring it into a deep agent
StateBackend — temporary, thread-scoped file storage
FilesystemBackend — giving your agent access to real disk
CompositeBackend — routing file paths to different backends
StoreBackend — persistent, cross-thread memory (with a live demo!)
Permissions — locking down read/write access with declarative rules
Sandboxes — isolating agent code execution from your host system
⏱️ Timestamps:
00:00 – Intro: What are Deep Agents?
01:30 – Project setup (uv, deepagents, Tavily)
03:00 – Tools & built-in filesystem tools
03:40 – StateBackend
05:00 – FilesystemBackend
05:30 – StoreBackend
06:30 – CompositeBackend
06:50 – StoreBackend + persistence demo
09:45 – Permissions
11:35 – Sandboxes
13:00 – What's next
📚 Resources:
LangChain Deep Agents docs: https://docs.langchain.com/oss/python...
Tavily: https://tavily.com
LangGraph tutorial
• Adding Postgres Checkpointer to LangGraph ...
🔔 Coming up next in this series:
Context Engineering in Deep Agents (skills, memory, summarization)
Subagents & task delegation
Human-in-the-loop & steering
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