intelligence-layer

To build an AI agent with persistent memory and tool use, you can use a framework-agnostic intelligence layer that provides a BaseAgent with a reasoning loop, ToolExecutor for custom functions, and PersistentMemory for conversation history.

Retrieval-Augmented Generation (RAG) for AI agents works by utilizing components like a QueryRewriter to enhance knowledge retrieval, enabling agents to access external knowledge for complex reasoning and knowledge-intensive tasks like research and analysis.

Yes, you can integrate custom tools into an AI agent without framework lock-in by using a framework-agnostic intelligence layer that features a ToolExecutor, allowing seamless integration of custom functions for task decomposition and execution.

You can add external knowledge retrieval to a customer support agent by implementing RAG patterns within an intelligence layer, enabling the agent to access a knowledge base, look up order statuses via tools, and maintain coherent conversation history.

Yes, the intelligence layer supports task decomposition for complex reasoning by providing a BaseAgent with a dedicated reasoning loop, enabling the development of agents capable of executing complex multi-step tasks and analysis.