Build and run agents you can see, understand and trust.

Demystify AI agents by building them yourself. Local LLMs, no black boxes, real understanding of function calling, memory, and ReAct patterns.

Lightweight Python SDK for LLMs with unified API across 9 providers. Built-in ReAct & Plan-Execute agents, streaming, native tool calling, context injection, structured outputs, and observability.

fullstack chat agent with authentication, request credits and payments built in

A minimalistic approach to building AI agents

A complete LangGraph multi-agent system demo using SQL tools, Tavily search, MCP Toolbox, and OpenRouter models — with reproducible notebooks and a full supervisor-led agent workflow.

🤖 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.

A simple ReAct agent that has access to LlamaIndex docs and to the internet to provide you with insights on LlamaIndex itself.

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

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 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.

基于大模型 (LLM) ，智能体(Agent)与 RAG 技术的智能口岸物流助手。通过 Agent 自动调度查询工具与检索法规知识库，为港口用户提供通关异常诊断与决策支持。

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

一个简单的AI Agent，可以通用处理各种问题

ReAct (Reasoning and Acting) agent built from scratch in Python. No libraries, no abstractions, simple and straight to the point.

A sample project to demonstrate how a langgraph ReAct agent can be wrapped with the A2A protocol

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.