Deep Agents overview - Docs by LangChain
Build agents that can plan, use subagents, and leverage file systems for complex tasks The easiest way to start building agents and applications powered by LLMs—with built-in capabilities for task planning, file systems for context management, subagent-spawning, and long-term memory. You can use deep agents for any task, including complex, multi-step tasks. We think of deepagents as an “agent harn
Agents 2.0: From Shallow Loops to Deep Agents
For the past year, building an AI agent usually meant one thing: setting up a while loop, take a user prompt, send it to an LLM, parse a tool call, execute the tool, send the result back, and repeat. This is what we call a Shallow Agent or Agent 1.0. This architecture is fantastically simple for transactional tasks like "What's the weather in Tokyo and what should I wear?", but when asked to perfo
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https://x.com/i/status/2033959303766512006
https://x.com/i/status/2031798630177251514
Harrison Chase on X: "The two patterns by which agents connect sandboxes"
Context Management for Deep Agents
Using skills with Deep Agents
Project: Deep Agents
