deep-agents-core

To build AI agents with LangChain and LangGraph, you instantiate agents with custom tools, models, and system prompts, leveraging middleware for planning, context management, and memory to handle complex multi-step tasks.

Deep Agents architecture is a framework for sophisticated AI agent development using LangChain. You need its built-in middleware when your agent requires planning, context management, delegation, memory, or human-in-the-loop workflows.

Yes, you need LangChain and Deep Agents libraries installed in your environment to use this Skill for agent harness functionality, as it facilitates agent instantiation, middleware integration, and tool usage on top of these dependencies.

You integrate custom tools and system prompts into a LangGraph agent during agent instantiation, configuring the specific models and tools your AI assistant needs to manage files and interact with complex task workflows.

Yes, you can delegate work to specialized sub-agents by leveraging the built-in middleware for delegation and context management, allowing your primary AI assistant to manage multi-step tasks and distribute work effectively.

Skill loading for Deep Agents built on LangGraph involves configuring the agent harness to dynamically load specific capabilities, allowing the agent to manage complex tasks by integrating external tools and references as needed.