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.
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.
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.
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