deep-agents-core

Build multi-step LangChain agents by using a framework that integrates middleware for task planning and pluggable backends for context management. This approach simplifies complex tasks into manageable steps while spawning subagents for modular task delegation.

Add human-in-the-loop approval workflows to a deep agent by utilizing integrated middleware that supports sensitive operation oversight. This allows your complex multi-step agents to pause for human review before executing critical tasks.

Yes, you can use LangChain and langchain-google-genai to create subagents for task delegation. The framework supports spawning specialized subagents to handle specific tasks, enhancing modularity within your complex workflows.

Yes, you need Python to manage large file contexts with pluggable backends in deep agents. The framework requires Python and LangChain libraries to efficiently handle context management and persist memory across sessions.

Use middleware for task planning to simplify complex tasks into manageable steps within multi-step agents. TodoListMiddleware specifically breaks down workflows, enabling sequential analysis and long-term memory persistence across sessions.

Yes, deep-agents-core supports on-demand loading of specialized capabilities for tailored functionality. This feature allows your deep agents to dynamically load skills, ensuring efficient context management and task delegation.