Tutorial Shows How to Build Deep Agents with LangGraph and Web Search
Why does a tutorial on LangGraph matter now? Because building AI agents that go beyond a single prompt has become a concrete skill, not just a buzzword. The guide walks readers through assembling a “deep agent” that can query the web, parse results, and feed the information back into a language model.
While the code is straightforward, the underlying idea—using LangGraph to orchestrate multiple steps—shows how developers can stretch LLM performance without reinventing the wheel. Here’s the thing: the example agent is intentionally minimal, a sandbox for anyone eager to experiment. But the real value lies in the next steps.
The article points out that the agent can be refined, expanded, and tuned, turning a simple prototype into something more capable. That promise sets the stage for the author’s next words, which lay out concrete ways to improve the system and demonstrate how AI agents can push LLM capabilities a notch higher.
We built a simple deep agent, but you can challenge yourself and build something much better. Here are few things you can do to improve this agent: We have successfully built our Deep Agents and can now see how AI Agents can push LLM capabilities a notch higher, using LangGraph to handle the tasks. With built-in planning, sub-agents, and a virtual file system, they manage TODOs, context, and research workflows smoothly. Deep Agents are great but also remember that if a task is simpler and can be achieved by a simple agent or LLM then it's not recommended to use them.
Can a single tutorial change how we build AI? The guide walks readers through constructing a deep agent with LangGraph and web‑search tools, showing that the system can generate its own TODO list, decompose work, and even launch sub‑agents to complete steps. It demonstrates that, at least in a controlled example, the agent can think ahead and orchestrate tasks without human micromanagement.
It works for now. Yet the implementation remains a simple prototype; the authors acknowledge that more sophisticated designs are possible and invite readers to iterate. The claim that AI agents can push LLM capabilities a notch higher is illustrated, but whether this translates to broader applications is still unclear.
The tutorial’s code serves as a starting point, and the suggested improvements hint at a path toward more robust agents, though performance metrics are absent. In short, the piece offers a concrete example of LangGraph’s potential while leaving open questions about scalability, reliability, and real‑world utility.
Further Reading
- Building a Deep Research Agent with LangGraph And Exa - Siddharth Bharath
- Build Your First AI Agent in 2025 (Python + LangGraph) - Skywork AI
- LangChain's Deep Agents: A Guide With Demo Project - DataCamp
- Project: Deep Agents with LangGraph - LangChain Academy
- LangChain Agents in 2025 | Full Tutorial for v0.3 - YouTube
More in LLMs & Generative AI
Raycast showcases AI photo renaming in latest episode discussion
Raycast’s newest episode zeroes in on a surprisingly narrow problem: getting a computer to rename a...
Gen AI app sessions up fivefold, downloads jump 778% as ChatGPT leads traffic
Why does this matter now? While the tech is impressive, the real story is how quickly generative‑AI...
Sunday Robotics unveils Memo robot as Visual Intelligence secures USD 600M
Why does a new robot and a massive funding round matter right now? While the hype around autonomous...
Apply SRE discipline: SLOs, error budgets and golden signals for AI reliability
Enterprises are wrestling with a new kind of volatility: large‑language models that can drift,...
New RL framework lets LLM agents master multi-step reasoning in dynamic settings
It’s kind of surprising how often a language model trips up when a problem needs more than one...
Common Questions Answered
How does the tutorial use LangGraph to enable a deep agent to perform web searches?
The guide demonstrates wiring LangGraph nodes to issue web‑search queries, capture the results, and pass the parsed information back into a language model. This orchestration lets the agent retrieve up‑to‑date data without manual prompting, extending LLM capabilities.
What role do sub‑agents and a virtual file system play in the deep agent architecture described?
Sub‑agents handle discrete subtasks such as parsing search results or managing files, while the virtual file system stores intermediate outputs and context. Together they allow the deep agent to maintain state, coordinate complex workflows, and avoid reinventing core functionality.
In what ways can the deep agent generate and manage its own TODO list according to the article?
The prototype can automatically create a TODO list by decomposing a high‑level goal into smaller steps, then schedule those steps as separate graph nodes. It tracks progress through the virtual file system, updating the list as each sub‑task completes.
What limitations does the article acknowledge about the current deep agent implementation?
The authors note that the presented deep agent is a simple prototype, lacking robust error handling and scalability for real‑world deployment. They emphasize that while the concept works in a controlled example, further development is needed for production‑grade reliability.