The article details a coding implementation to build a hierarchical planner AI agent using open-source language models (LLMs). It includes structured multi-agent reasoning and tool execution capabilities. The system consists of three main components: a planner agent that breaks down complex tasks into manageable steps, an executor agent capable of executing these steps through either language-based reasoning or dynamic Python code execution, and an aggregator agent responsible for combining intermediate results to produce a final refined output. The implementation leverages open-source LLMs, structured prompts, and tool-based execution methods to create a powerful and extensible autonomous agent framework.
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