Check this out. I just asked an agent to answer one of the world's greatest debates. Is Messi or Ronaldo the greatest soccer player of all time.This isn't an easy question to answer, and it definitely requires a good amount of research. The agent automatically spawned two parallel sub agents to look into each of their achievements. This meant searching the web over a dozen times, compiling a comprehensive report with cited sources.To be extra thorough, the agent then critiqued its own report and plugged any ...

Anthropic's Claude Code, OpenAI's Deep Researcher, and Manus's general purpose agent have demonstrated that agents can be amazingly effective on complex, long-running tasks. We call these Deep Agents because they have a few key differentiators from earlier forms of agents. In our new LangChain Academy course, Deep Agents with LangGraph, you'll learn their key characteristics and how to implement them in your own Deep Agent.So what makes these agents different. Under the hood, they use a simple ReAct tool-ca ...

Deep Agent Architecture - Deep agents utilize a planning tool to strategize task execution [4][5] - They employ a file system for organized context and information management, preventing context window overload [5] - Deep agents leverage specialized sub-agents to execute specific tasks, such as research or critique [5][6][18] - A detailed system prompt guides the overall operation of agents and sub-agents [6][10] Implementation and Usage - The process involves cloning the deep agents JS repository and the deep agents UI repository [7][13][14][19] - Configuration requires setting up environment variables, including API keys for web search (Tilli API key) and LLM provider (Anthropic API key), as well as specifying the Langraph server URL and agent ID [8][15][16][19] - The deep agent is instantiated using the `create deep agent` function, which requires a list of tools, instructions, and optionally, sub-agents [9][10] - The Langraph server is initiated using the command `npx langchain langraph cli dev` [8][13] - The deep agents UI runs on localhost 3000, allowing users to interact with the agent [16]

Deep agents are a form of agents that plan, reason, and act over longer time horizons. We built a dedicated UI for viewing and interacting with these agents that show its plan, the status of the file system that it uses, and any sub aents it kicks off. My name is Nick.I'm an engineer at Langchain, and today you'll learn how to set up this UI. Now, as a quick refresher, we can think of deep agents as a variant of the generic React tool calling architecture. Under the hood, deep agents still follow the same i ...

Deep Agent Characteristics - Deep agents utilize a planning tool to manage long-term tasks, enabling cohesive action over extended periods [3][5][9] - Sub-agents are employed to focus on specific areas, preserving context and allowing for specialized expertise, which can improve overall results [3][10][11][12][13][15] - A file system is used to offload context, preventing performance degradation of the LLM by storing and accessing information as needed [3][16][17][18] - Detailed system prompts, often hundreds or thousands of lines long, are crucial for guiding the agent's behavior and tool usage [3][21][22][23] Deep Agent Implementation - Deep agents operate using the same tool-calling loop as simpler agents, but are distinguished by their prompts and tools [3][4][5] - Planning tools can be simple, such as a "to-do write" tool that generates and modifies task lists within the model's context [7][8] - Sub-agents can have specialized expertise and different permissions, allowing for focused work and better results [13][14] - File systems allow agents to manage context by referencing files instead of directly including large observations in the LLM context [17][18] Deep Agent Benefits - Deep agents are capable of handling longer time horizon and more complex tasks compared to naive LLM implementations [4][5] - Sub-agents facilitate context preservation, preventing the main agent's context from being polluted by sub-tasks and vice versa [11][12] - Reusable sub-agents can be created and used across different agents, promoting efficiency and modularity [14]