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 ...