deep-agents-core

The deep-agents-core skill provides a structured framework for building complex, multi-step AI agents with minimal implementation overhead. It is designed to streamline the creation of Deep Agents applications by offering a pre-configured harness that manages task planning, context, subagent delegation, long-term memory, and human-in-the-loop workflows. By abstracting the underlying complexity, developers can focus on configuring their agents rather than building each component from scratch, ensuring consistency and efficiency across projects that require advanced agent orchestration.

Key features include built-in middleware such as TodoListMiddleware for task breakdown, FilesystemMiddleware for context management with pluggable backends, SubAgentMiddleware for specialized agent delegation, MemoryMiddleware for persistent storage across threads, and HumanInTheLoopMiddleware for approval workflows. Additionally, SkillsMiddleware allows on-demand loading of specialized capabilities, making the framework highly extensible. The harness architecture automatically integrates these middleware components, so developers primarily define tools, subagents, skills, and configuration options without implementing the infrastructure themselves.

This skill is particularly useful for applications that involve multi-step problem solving, complex workflows, large or persistent context, and scenarios requiring specialized subagents. Target users include AI developers, data engineers, and teams building intelligent assistants or automation pipelines who need a scalable and maintainable agent framework. It is ideal for projects where task planning, context management, and persistent memory are critical, while simpler single-session tasks might still be better served by a more lightweight agent creation approach.