Awesome AI Agents › Courses and Tutorials
This repository accompanies a paid video course on building AI agents with LangChain and LangGraph in Python. Rather than a library, it is a structured set of worked projects: each project lives on its own git branch or in a linked external repository, and commits are ordered so that learners can follow the build step by step and check out earlier states to see how a feature was assembled. The listed projects run from a hello world chain that introduces basic structure and LLM integration, through a modern search agent that uses the create_agent interface of LangChain version one with custom tools, Tavily search and structured outputs, an agents under the hood project that unpacks reasoning and acting patterns, a retrieval-augmented generation primer, a documentation helper assistant, a code interpreter that executes and analyses code, and on to reflection, reflexion and agentic RAG agents that critique and correct their own output. Additional linked repositories cover a social profile analyzer and a blog analyzer. The stated learning objectives are building agents from scratch, implementing tool calling and external API integration, creating RAG systems over vector databases such as Pinecone and FAISS, designing multi-step reasoning workflows, deploying agents, and handling error correction and self-improvement. The README states this is not a beginner course and expects familiarity with git, Python, environment variables, classes, testing and debugging, plus Python 3.10 or newer and a package manager such as uv, poetry or pipenv. Any LLM provider works, including local models served through Ollama as well as OpenAI, Anthropic and Gemini. It suits software engineers who want a project-driven path into agent frameworks.
https://github.com/emarco177/langchain-course
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