fullstack chat agent with authentication, request credits and payments built in
A simple ReAct agent that has access to LlamaIndex docs and to the internet to provide you with insights on LlamaIndex itself.
A pure Python implementation of ReAct agent without using any frameworks like LangChain. It follows the standard ReAct loop of Thought, Action, PAUSE, and Observation. The agent utilizes multiple tools, including Calculator, Wikipedia, Web Search, and Weather. A web UI is also provided using Streamlit.
🤖 Advanced AI agent system combining ReAct reasoning and Plan-Execute strategies with unified memory, reflection patterns, and browser automation tools. Built with LangGraph, LangChain, and Google Gemini.
An AI-powered investment analysis tool 📈 that leverages simple ReAct AI agent flow framework and financial analysis techniques to provide comprehensive portfolio insights. This intelligent agent helps investors make data-driven decisions by offering deep portfolio risk assessment, stock profiling, and personalized recommendations.
React AI Agent with Long-Term Memory and Tool calling
A minimalistic approach to building AI agents
LLM OSINT is a proof-of-concept method of using LLMs to gather information from the internet and then perform a task with this information.
A practice repository implementing examples from the official LangChain documentation
The Financial Analysis Crew is a Streamlit app that simplifies financial stock analysis. With the power of LLM-driven agents, users can seamlessly gather and analyze stock market data to generate comprehensive financial insights. Perfect for investors, analysts, and anyone interested in making data-driven financial decisions.
multi agent orchestrator
This repository contains a Python application using LangChain to create a multi-agent system for answering queries with Yahoo Finance News and Wikipedia
Innovative AI agent implementations using LangGraph—featuring ReAct, RAG (Corrective, Self, Agentic), chatbots, microagents, and more, with multi-AI agent systems on the horizon! 🤖🚀
ReAct (Reasoning and Acting) agent built from scratch in Python. No libraries, no abstractions, simple and straight to the point.
This project implements a travel chatbot powered by the RAG (Retrieve and Generate) chain, providing real-time information retrieval using various tools and the ability to fetch weather reports.
Effortlessly create functional documentation with AI and integrate directly with Jira. Generate, refine, and export User Stories to your Jira project in just a few clicks! 🚀
All projects done for LangChain - Develop LLM powered applications with LangChain Udemy Course
From-scratch implementation of a ReAct agent using LangChain, showcasing manual control over tool invocation, prompt design, and reasoning loop without relying on built-in abstractions.
A sample project to demonstrate how a langgraph ReAct agent can be wrapped with the A2A protocol
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