AGENT DEV ACCELERATOR

OVERVIEW

This repository contains a collection of Jupyter notebooks and tools designed to accelerate the development of AI agents using LangChain, LangGraph, and other related libraries.

Important

The project demonstrates various techniques for building, managing, and enhancing AI agents with memory, retrieval-augmented generation, streaming, and tool-calling capabilities.

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FEATURES

• notebook/agent_memory.ipynb Demonstrates how to build a React-based agent with memory persistence using LangChain and LangGraph.

• notebook/rag_embedding.ipynb Implements RAG workflows with Pinecone vector stores and Ollama embeddings for efficient document retrieval and question answering.

• notebook/stream.ipynb Provides an example of streaming responses from a language model for real-time interaction.

• notebook/tool_calling.ipynb Shows how to integrate external tools into the agent's workflow for enhanced functionality.

Note

Please check the Wiki section of this GitHub repository for all related documentation, guides, and usage references. It contains detailed explanations, code examples, and development notes to help you get the most out of this project.

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AUTHOR

Giovane Hashinokuti Iwamoto - Computer Science - Brazil

I am always open to receiving constructive criticism and suggestions for improvement in my developed code. I believe that feedback is an essential part of the learning and growth process, and I am eager to learn from others and make my code the best it can be. Whether it's a minor tweak or a major overhaul, I am willing to consider all suggestions and implement the changes that will benefit my code and its users.