Building Deep Agents Tutorial With Langchain- Part 1

Why It Matters

Deep agents transform generative AI from single‑shot responders into autonomous problem‑solvers, unlocking higher‑value applications for businesses that need complex, context‑aware insights.

Key Takeaways

• Shallow agents lack planning and struggle with complex queries
• Deep agents incorporate a planning layer before tool execution
• Deep agents enable query decomposition into sub‑tasks for richer results
• Context retention improves as deep agents loop through multiple reasoning steps
• Companies like OpenAI, Anthropic, and Mistral deploy deep research agents

Summary

The video introduces deep agents, a next‑generation AI architecture that moves beyond the simple request‑response loop of traditional, or "shallow," agents. Krish Naik explains that shallow agents rely on a single LLM decision to either generate an answer or call an external tool, offering limited context retention and no explicit planning, which makes them unsuitable for multifaceted queries.

He contrasts this with deep agents, which embed a dedicated planning component that first decomposes a user request into sub‑tasks, selects appropriate tools, and iteratively refines its reasoning. This multi‑step loop enables richer context handling, dynamic tool orchestration, and the ability to tackle complex, interdisciplinary questions such as real‑time AI news tied to economics or physics.

Examples cited include the deep research agents powering ChatGPT, Claude, and Mistral AI, as well as Naik’s upcoming product Xenodox, which leverages the same architecture. He demonstrates preliminary code snippets to illustrate how developers can instantiate a planning module and integrate it with LangChain’s tool‑calling framework.

The shift to deep agents signals a broader industry trend toward more autonomous, adaptable AI systems that can perform sophisticated information synthesis. Enterprises that adopt this paradigm can expect faster time‑to‑insight, reduced reliance on manual prompt engineering, and a competitive edge in delivering AI‑driven services that require nuanced, multi‑step reasoning.

Original Description

code link: https://drive.google.com/file/d/1SVjvgqvKfF-FPAIqpEZdKMQLhe13NLFD/view?usp=sharing

Build agents that can plan, use subagents, and leverage file systems for complex tasks

deepagents is a standalone library for building agents that can tackle complex, multi-step tasks. Built on LangGraph and inspired by applications like Claude Code, Deep Research, and Manus, deep agents come with planning capabilities, file systems for context management, and the ability to spawn subagents.

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When to use deep agents

Use deep agents when you need agents that can:

Handle complex, multi-step tasks that require planning and decomposition

Manage large amounts of context through file system tools

Delegate work to specialized subagents for context isolation

Persist memory across conversations and threads