It shows case studies of the LangGraph agent.
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Updated
Mar 1, 2025 - Jupyter Notebook
It shows case studies of the LangGraph agent.
🤖 MateClaw — Java + Vue 3 AI Assistant with Multi-Agent Orchestration, MCP Protocol, Skills & Memory, and Multi-Channel Support. Built on Spring AI Alibaba.
It shows how to deploy and use an agent with LLM.
It shows a problem solver based on agentic workflow.
It shows an intelligent agent based on LangGraph for long form writing.
It is a chatbot based on LangChain.
Extending the capabilities of LLMs using Planning agents and using "knowledge providers"
It is a case study of an intelligent agent for Ocean.
A minimal, fully commented Python executive AI agent. For students, teachers and junior devs who want to understand agentic AI from the ground up.
A minimal, fully commented Python executive AI agent. For students, teachers and junior devs who want to understand agentic AI from the ground up.
Minimal, hackable AI agent built on the ReAct reasoning loop. No frameworks — just a transparent Thought → Action → Observation cycle you can read and extend in an afternoon.
A minimal, fully commented Python executive AI agent. For students, teachers and junior devs who want to understand agentic AI from the ground up.
One CLI to plan, execute, and review! AI agents with FSM-driven orchestration.
A modular, general-purpose agent built with LangGraph, MCP, and LangSmith - demonstrated via GitHub code analysis.
Plan execution layer for AI coding assistants. One command — isolate, classify, execute, verify, merge. Works with Claude Code, Cursor, Windsurf, Aider, and any LLM.
Add a description, image, and links to the plan-and-execute topic page so that developers can more easily learn about it.
To associate your repository with the plan-and-execute topic, visit your repo's landing page and select "manage topics."