Since I am a novice developer, my learning direction is relatively unclear. I hope to get a relatively complete and clear learning roadmap. I would be very grateful if you could provide a learning plan.
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This guide is specifically for Applied Agentic Engineering with a focus on Langchain ecosystem.
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Start by understanding the following core component: Agents - Docs by LangChain
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Agents
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Models
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Tools
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Messages
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Short-Term Memory
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Structured Output
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Get a Basic Idea of Middewares: Overview - Docs by LangChain
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Understand the Very Basic of Langgrah: Quickstart - Docs by LangChain
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Once you have a clear Idea about Langchain and how Langchain v1 components are built on top of Langgraph (understand how they are linked), start building some stuff:
- langgraph/examples at main · langchain-ai/langgraph · GitHub (You can take inspiration from these examples)
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Also, learn about Langsmith: Tracing quickstart - Docs by LangChain and how you can integrate it in your workflow for better observability
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Finally, to deploy your flow, I would recommend you learn:
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https://docs.langchain.com/oss/python/langgraph/local-server (If you want to deploy your Agentic Flow using Langgraph template)
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FASTAPI (Learn basics about FastAPI since that is the go-to framework for deploying most of ML based apps)
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There are also a number of great courses available in LangChain Academy that provide a guided curriculum for getting started and launching agents: All Products - LangChain Academy
I wanted to learn LangGraph myself, so I built a beginner multi-agent investement system, it helped me understand fastapi utility and how to orchestrate different agents to retrieve data, debate facts and come up with an informed decison. you can absolutely clone it and add to it, break it into smaller isolated parts, or whatever might help you wrap your head around this increasingly demanded technology. feel free to play with it (don’t hesitate to put a star if you like it 
): GitHub - alielbaha/multi-agent · GitHub