Tool, Memory Enhanced React RAG Agents

Tool-enhanced agents are versatile problem solvers integrating multiple tools, leveraging APIs, databases, and software to handle complex, multi-domain workflows. They combine reasoning, retrieval, and execution for seamless

Includes a RAG workflow: Has external knowledge provided through external database, API, or documentation with the addition of LLM-enhanced context. Grounded in current knowledge: Pulls in real-time or domain-specific knowledge. Reasoning and Action: Uses ReAct-style reasoning to break down tasks and dynamically retrieve information as needed. Low Hallucinations: Designed for scenarios where correctness and relevance are non-negotiable.

State management

Now let's talk about how our agent keeps track of everything it needs to do. Think of it like a smart to-do list system with three main parts.

First, we need a way to track what the agent plans to do. We'll use a simple list of steps written as text strings. This is like having a checklist of tasks the agent needs to complete.

Second, we want to remember what the agent has already done and what happened with each task. For this, we'll use a list of pairs (or tuples in programming terms). Each pair contains both the action taken and what resulted from that action.

Lastly, we need to store two more important pieces of information: the original question that was asked (the input) and the final answer once the agent finishes its work (the response).

This setup gives our agent everything it needs to work effectively!

The plan method prompts the LLM to create the initial execution plan and updates the working memory with the user query and the generated plan.