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It shows case studies of the LangGraph agent.

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.

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.

Autonomous coding loop for OpenAI Codex and Google Gemini CLIs. Drop planning docs into a folder, set an iteration count, and loop-agent generates a backlog and runs plan → implement → review cycles to write your code. Independent-process reviews, atomic backlog updates, auto git rollback on failure, and safe rate-limit handling.

One CLI to plan, execute, and review! AI agents with FSM-driven orchestration.

Plan execution layer for AI coding assistants. One command — isolate, classify, execute, verify, merge. Works with Claude Code, Cursor, Windsurf, Aider, and any LLM.

A modular, general-purpose agent built with LangGraph, MCP, and LangSmith - demonstrated via GitHub code analysis.

Build a minimal Python executive AI agent with fully commented code for learning, teaching, and junior developers

从零构建 AI Research Agent：5 阶段渐进式学习，掌握 Agent Loop、Tool Use、Memory、Plan-and-Execute、生产增强。Python + GLM-4.7 + SQLite

Build a minimal Python executive AI agent with full comments for students, teachers, and junior devs learning agentic AI from scratch

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