deep-agents-core

Building AI agents with LangChain and LangGraph is simplified by using a framework that provides built-in middleware for task planning, context management, and sub-agent delegation. This approach handles complex multi-step resolutions automatically.

On-demand skill loading for LangGraph agents works by dynamically integrating specialized capabilities defined in SKILL.md files. This allows your AI agent to access specific tools and documentation only when required during a task.

Yes, you can use middleware to delegate tasks to sub-agents in LangChain. The framework includes an agent harness that automatically provides delegation capabilities, enabling a primary agent to assign specific tasks to specialized sub-agents.

The best way to maintain persistent memory across LangGraph agent sessions is to use a framework with built-in context management middleware. This ensures your AI agent retains context and memory seamlessly between different interactions.

Yes, this LangChain agent framework supports custom tools for multi-step customer support resolutions. You can develop an agent that plans resolutions, accesses documentation, and delegates tasks through a unified harness.