an open source 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.
React AI Agent with Long-Term Memory and Tool calling
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.
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 minimalistic approach to building AI agents
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! 🤖🚀
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.
A practice repository implementing examples from the official LangChain documentation
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! 🚀
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
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.
A lightweight, streaming-first ReAct (Reasoning + Acting) agent that works with any LangChain-compatible model. Focus on agent logic while LangChain handles the provider complexity.
🛰️ Agent Google Maps -
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