Langflow: Build AI Workflows & Agents Without Code
No code required. In one session you'll visually build a document Q&A chatbot, an AI agent that connects to live tools, and a multi-step workflow — drag, drop, run, done.
Yash ThakkerDay 1
A single intensive day covering Langflow end-to-end: how the graph-based runtime works, building RAG pipelines from scratch, connecting external tools and APIs, designing multi-agent flows, and preparing a workflow for production. Every session ends with a working flow you built yourself.
Next run: Sep 7, 2026
- ✓1 live session · 4 hours · Sep 7, 2026
- ✓1-year access to session recording
- ✓Take-home Langflow component templates
Secure payment via Stripe
Three working AI pipelines built during the session, ready to deploy
Langflow is a visual canvas where you build AI workflows and agents by connecting blocks — no code, no boilerplate, no waiting on a developer. You drag in a document loader, connect it to an LLM, add a memory block, and your AI assistant is running. This workshop covers the full picture: how to read and answer questions from your own documents, how to give your agent real tools like web search and APIs, and how to chain multiple agents together so they collaborate on a task. Whether you're a founder, marketer, operations manager, or PM — if you can use a whiteboard, you can build in Langflow. By the end you'll have three working AI workflows built during the session, ready to share or deploy the same day.
- 1 live session · 4 hours · Sep 7, 2026
- Build RAG pipelines and multi-agent workflows visually
Two focused days
1 sessions · 4 hours each · All sessions recorded
A single intensive day covering Langflow end-to-end: how the graph-based runtime works, building RAG pipelines from scratch, connecting external tools and APIs, designing multi-agent flows, and preparing a workflow for production. Every session ends with a working flow you built yourself.
Sessions
Session 1: Langflow Fundamentals — The Visual Runtime
Understand how Langflow works: nodes, edges, components, and the underlying LangChain execution graph. Set up your Langflow environment, tour the component library, and build your first end-to-end flow — a basic LLM chat with memory. Learn how Langflow maps to LangChain concepts so you can debug and extend anything you build.
Session 2: Building RAG Pipelines That Actually Retrieve
Build a retrieval-augmented generation pipeline from the ground up — document loading, chunking strategy, embedding model selection, vector store ingestion, and retrieval chain assembly. Learn why naive RAG fails and how to tune chunk size, overlap, and retrieval parameters to get the right context into every response. Finish with a working Q&A pipeline over your own documents.
Session 3: Connecting APIs, Tools, and External Data
Extend your flows beyond static documents. Connect live APIs as tool nodes, wire in web search, build conditional routing so agents decide which tool to call, and handle errors gracefully when external calls fail. Add memory so your agent tracks conversation state across turns. Leave with a flow that reads from the real world and responds intelligently.
Session 4: Multi-Agent Workflows and Production Patterns
Design supervisor-worker multi-agent architectures in Langflow — one orchestrator delegates to specialised agents, aggregates results, and returns a final output. Learn Langflow's deployment options: API export, Docker, and cloud hosting. Add logging, trace your flow's execution, and review the patterns that separate a reliable production workflow from a fragile demo.
Skills covered
You'll build
- →Build a RAG pipeline over your own documents with tuned retrieval
- →Create a tool-calling agent that connects to a live API
- →Design a multi-agent supervisor workflow and export it as a deployable API
Looks like something you need? Seats are limited.
Secure your spotWhat you'll build
Every session has hands-on work. Leave with real deliverables you can use the next morning.
RAG Document Pipeline
A complete retrieval-augmented generation flow over your own documents — ingestion, chunking, vector storage, and a retrieval chain with tuned parameters that returns accurate, grounded answers.
Tool-Calling Agent
A Langflow agent wired to real external APIs — with conditional tool selection, error handling, and conversation memory — ready to answer questions the LLM alone cannot.
Multi-Agent Supervisor Flow
A production-grade supervisor-worker architecture: one orchestrator routes tasks to specialised sub-agents, aggregates their outputs, and returns a unified response — exported as a deployable REST API.
Join professionals from 30+ countries who've taken this workshop.
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Frequently asked questions
Everything you need to know before enrolling.
No. Langflow is a visual canvas — you build by connecting blocks, not writing code. If you can use a tool like Notion or Figma, you can build in Langflow. No programming background required.
Ready to upgrade how you work with AI?
Join professionals from 30+ countries learning to use Claude as a genuine work partner. Seats are limited to keep sessions interactive.
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