What Next: LangGraph and Beyond
You made it to the end. Well done.
You can now talk to a model, shape its answers, give it memory and tools, and let it read your own documents.
This last lesson shows you where the road goes from here.
What you learned
Models: init_chat_model, invoke, stream, batch
Prompts and parsers: templates in, clean strings or objects out
Chains: the pipe that snaps pieces together
Memory: RunnableWithMessageHistory
Tools and agents: letting the model act
RAG: load, split, embed, store, retrieve, answer
Ollama, retries, fallbacks and LangSmith: making it free and solid
LangGraph: a flowchart for agents
A chain is a straight line. Step one, step two, step three, done.
Real agents need loops. Think, act, look at the result, think again. Sometimes they need to stop and ask a human.
LangGraph lets you draw that as a flowchart. Boxes are nodes. Arrows are edges. A shared state moves along the arrows.
State is the shared notebook. Each node reads it and writes back a piece. Edges say which node runs next.
Add a conditional edge and you get a loop. Add an interrupt and the graph pauses for a human to approve. That is how serious agents are built today.
Deploying: turn a chain into an API
A chain on your laptop is nice. A chain that a website can call is useful.
FastAPI is the simplest way. LangServe adds ready made routes on top of it.
Run it with uvicorn, and any app can send a question and get an answer back as JSON.
Evaluation: is it actually good?
Once you change a prompt, how do you know the answers got better and not worse?
LangSmith lets you save a dataset of questions and expected answers. Then it runs your chain over all of them and scores the results.
Make the dataset once. Run it after every change. Your future self will thank you.
Multi agent systems
One agent can do a lot. A team can do more.
A planner agent breaks the job into steps. A researcher fetches facts. A writer drafts. A checker reviews.
LangGraph is built for this. Each agent is a node, and the graph decides who speaks next.
Three projects to try
Homework helper: load PDFs of a textbook, build a RAG chain, and let a student ask questions with sources
Support bot: give an agent tools like check_order and refund, add memory, and wrap it in FastAPI
Code explainer: paste a file, split it by function, and ask the model to explain each part like a teacher
Pick one. Build it badly first. Then make it better. That is how everyone learns this.
Keep learning on FoxSchools
Stronger Python makes stronger LangChain. Our Python course covers everything used here.
LangChain also exists for JavaScript. Learn the language in our JavaScript course and TypeScript course.
Thank you for learning with us. Go build something, and be kind to your future users.
Remember: every big AI app is small pieces snapped together. You know the pieces now. The rest is practice.
Test yourself
Three quick questions made just for this lesson. Earn 10 XP per correct answer.