Deep Agents Core

Name: Deep Agents Core
Availability: InStock
Author: LangChain

Create and configure Deep Agents with LangChain and LangGraph.

1.1k|86|Updated Jan 22, 2026

Search Tags:#middleware #langchain #agent framework #langgraph #deep agents #skill loading

Ready to run

npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-core

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Deep Agents Core
Source: https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/deep-agents-core
Command: npx skills add https://github.com/langchain-ai/langchain-skills --skill deep-agents-core

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the creation and configuration of sophisticated AI agents by providing a robust framework that handles complex tasks, context management, and on-demand skill loading.

Core Features & Use Cases

Agent Creation: Easily instantiate agents with custom tools, models, and system prompts.
Middleware Integration: Leverage built-in middleware for planning, file management, delegation, and memory.
Skill Management: Dynamically load specialized capabilities based on task requirements.
Use Case: Develop an agent that can research a topic, write a report, and then generate code snippets based on the report's findings, all managed through a unified harness.

Quick Start

Create a basic deep agent with a custom tool and invoke it with a user message.

Frequently Asked Questions about Deep Agents Core

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build advanced AI agents with LangChain and LangGraph?▼

You can create AI agents with LangChain by leveraging the Deep Agents framework, which provides built-in middleware for planning, file management, delegation, and memory on top of LangGraph infrastructure.

How does an AI agent handle context management during multi-step task planning?▼

Context management for multi-step task planning is handled through integrated filesystem tools and persistent memory middleware, allowing the agent to maintain state and reference data across complex workflows and sessions.

Can I dynamically load specialized skills into a LangGraph agent based on task requirements?▼

Yes, you can dynamically load specialized capabilities into a LangGraph agent through on-demand skill loading middleware, allowing the agent to import and utilize specific tools only when the task requires them.

What is the best way to delegate subtasks to subagents within a LangChain workflow?▼

Delegate subtasks within a LangChain workflow by using the framework's built-in delegation middleware to spawn and manage subagents, allowing the primary agent to assign specialized tasks and aggregate results.

Does this agent framework support human-in-the-loop workflows?▼

Yes, the agent framework supports human-in-the-loop workflows by integrating approval steps and validation checks into the LangGraph execution flow, allowing users to review or redirect agent actions before final execution.