After this lesson, you will be able to: Build a fully featured low-code agent pipeline, ingest documents, answer questions, call APIs, and expose a polished interface.
Low-code capstone. Pick your platform, pick your problem, build it for real. Same pattern as the no-code capstone, pick → build → deploy → demo, but with the extra power of LangChain primitives underneath.
1. Same rule as no-code capstone: it must be YOURS. Real annoyance, real domain.
2. The bar is higher: low-code can handle RAG over hundreds of docs, multi-agent flows, and tool-rich pipelines. Pick a problem that uses at least 2 of those.
3. Examples that hit the bar:
- A research assistant over your school's entire course catalog (RAG + tools).
- A product analytics chatbot that queries your DB and explains results (tool calling + structured outputs).
- A multi-agent content team for your blog (research → write → review).
1. Sketch on paper first, nodes, edges, what each does. 5 minutes saves 2 hours.
2. Build the retrieval pipeline first (if RAG). Test it standalone before wiring to the LLM.
3. Add the LLM + prompt. Test single-turn.
4. Add memory. Test multi-turn.
5. Add tools. Test tool calling individually.
6. Add the deployment surface (widget, API, Slack).
7. Add observability, log every conversation to a Sheet or DB.
LangFlow. DataStax LangFlow Cloud or Docker on Railway. Flowise, `npx flowise` on a $5 VPS or Render. Dify. Dify Cloud or self-host on Railway. Relevance AI, already cloud-hosted; just enable the deployment surface.
Same bar as no-code capstone, automated, reliable, used for a week, recorded demo. Plus: at least one tool call working in production, RAG retrieving from your real docs, memory persisting across sessions.
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