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Flowise: No-Code LLM Agent Builder
Build powerful AI agents and chatbots visually using Flowise – a no-code platform for deploying LLM workflows, RAG pipelines, and automation.
Course Duration: 10 Hours
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Flowise – No-Code LLM Agent Builder – Online Course
Flowise: No-Code LLM Agent Builder is a comprehensive, self-paced online course designed for beginners, product designers, developers, and AI enthusiasts looking to build and deploy intelligent agents without writing code. Using Flowise, an open-source visual programming platform built on top of LangChain, this course teaches you to create fully functional AI applications—from chatbots to knowledge assistants to RAG-based agents—through a simple drag-and-drop interface.
Course Introduction
The rise of large language models (LLMs) has unlocked the ability to automate, augment, and personalize workflows across industries. However, implementing LLM apps often requires complex code, infrastructure knowledge, and advanced development skills.
The rise of large language models (LLMs) has unlocked the ability to automate, augment, and personalize workflows across industries. However, implementing LLM apps often requires complex code, infrastructure knowledge, and advanced development skills.
Flowise solves this problem by providing a drag-and-drop visual environment for designing, configuring, and deploying LLM workflows using modular components and integrations. Whether you want to build a chatbot, automate customer support, summarize documents, or create custom agents with memory, Flowise enables you to do so without writing a single line of code.
What is Flowise?
Flowise is a no-code, open-source visual builder for LLM-based applications, built atop LangChain. It allows you to connect blocks (nodes) like prompt templates, models, vector stores, tools, memory, and APIs—visually forming a flowchart that acts as your AI agent. Flowise supports integration with OpenAI, Cohere, Hugging Face, Pinecone, Supabase, Chroma, and more.
Flowise is a no-code, open-source visual builder for LLM-based applications, built atop LangChain. It allows you to connect blocks (nodes) like prompt templates, models, vector stores, tools, memory, and APIs—visually forming a flowchart that acts as your AI agent. Flowise supports integration with OpenAI, Cohere, Hugging Face, Pinecone, Supabase, Chroma, and more.
How to Use This Course
This course is ideal for beginners and non-programmers looking to prototype or build production-ready AI workflows. To maximize your learning:
This course is ideal for beginners and non-programmers looking to prototype or build production-ready AI workflows. To maximize your learning:
- Start with simple flows, like chatbot creation, and move to advanced workflows involving memory and retrieval.
- Use real-world projects to deploy AI agents for documentation, support, and content generation.
- Explore plugin integrations such as Zapier, Slack, Notion, and vector databases.
- Customize nodes and test outputs using built-in debugging tools.
- Leverage export and deployment features to bring your agent into live apps.
By the end of this course, you’ll be able to confidently design your own LLM-based solutions using Flowise’s intuitive interface.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the core concepts of LLM-based workflows and LangChain architecture.
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Set up and configure Flowise locally or in the cloud.
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Build conversational agents using visual nodes and prompt templates.
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Implement memory and context persistence in chatbots.
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Connect Flowise to vector stores like Pinecone, Chroma, or Supabase.
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Create retrieval-augmented generation (RAG) flows for document Q&A.
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Integrate external tools and REST APIs into your workflows.
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Customize flows for customer support, legal drafting, research, and more.
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Deploy Flowise agents to web apps, CRMs, and SaaS tools.
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Debug, monitor, and export your agents for reuse and collaboration.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Flowise
- What is Flowise?
- Why use no-code LLM builders?
- Understanding LangChain and node-based workflows
Module 2: Installing and Accessing Flowise
- Local setup with Node.js and Docker
- Accessing the Flowise UI
- Overview of the interface and basic settings
Module 3: Building Your First Flow
- Adding and connecting nodes
- LLM input, prompt templates, and chat history
- Executing a simple Q&A chatbot
Module 4: Prompt Engineering in Flowise
- Creating reusable prompt templates
- Best practices for clarity and context
- Testing and refining responses
Module 5: Adding Memory and Context
- Memory node configuration
- Persistent vs transient memory
- Using Redis and local memory options
Module 6: Integrating Tools and APIs
- HTTP request nodes for external APIs
- Custom function creation
- Connecting with Zapier, Slack, Notion
Module 7: RAG (Retrieval-Augmented Generation) Workflows
- Document loaders and embedding nodes
- Vector database setup (Chroma, Pinecone, Supabase)
- Creating a document Q&A bot
Module 8: Advanced Agents and Workflows
- Multi-step logic and decision trees
- Using conditional nodes
- Building task automation flows
Module 9: Export, Deploy, and Monitor
- Exporting flows to JSON
- Hosting with Vercel, Render, or locally
- Monitoring and debugging flows
Modules 10–12: Real-World Projects
- Project 1: AI-powered FAQ Chatbot
- Project 2: Contract Reviewer Agent
- Project 3: Research Assistant with API calls + RAG
Module 13: Flowise Interview Questions & Answers
Certification Back to Top
Upon completing the Flowise: No-Code LLM Agent Builder course, learners will receive a Certificate of Completion from Uplatz, validating their ability to design and deploy AI workflows without coding. This certification demonstrates practical skills in building intelligent assistants, integrating tools, managing context, and deploying no-code LLM solutions using Flowise. The credential is especially valuable for product managers, AI solution designers, operations professionals, and aspiring no-code developers seeking roles in the AI and automation landscape.
Career & Jobs Back to Top
Flowise represents the future of democratized AI development, where professionals can create intelligent solutions without needing deep coding expertise. As organizations adopt LLM-based systems across support, HR, content, and operations, those who can prototype and deploy with tools like Flowise are increasingly in demand.
This course prepares you for roles such as:
- No-Code AI Builder
- Prompt Engineer
- AI Workflow Designer
- Automation Consultant
- Conversational AI Architect
- Product Manager (AI Tools)
Job opportunities span startups, SaaS providers, agencies, enterprises, and freelance gigs. With Flowise, you can turn ideas into AI-driven prototypes within minutes—making you a valuable asset in building fast, cost-effective AI solutions.
Interview Questions Back to Top
1. What is Flowise and how does it relate to LangChain?
Flowise is a visual, no-code interface built on top of LangChain that allows users to create LLM workflows by connecting nodes such as prompts, tools, and memory modules.
Flowise is a visual, no-code interface built on top of LangChain that allows users to create LLM workflows by connecting nodes such as prompts, tools, and memory modules.
2. What types of applications can be built using Flowise?
You can build chatbots, RAG-based Q&A systems, research agents, legal document analyzers, customer support tools, and more—all without code.
You can build chatbots, RAG-based Q&A systems, research agents, legal document analyzers, customer support tools, and more—all without code.
3. How does Flowise handle memory and context?
Flowise supports memory via dedicated nodes and can store chat history using Redis or internal memory. This allows agents to remember prior inputs and provide contextual replies.
Flowise supports memory via dedicated nodes and can store chat history using Redis or internal memory. This allows agents to remember prior inputs and provide contextual replies.
4. What vector stores does Flowise support?
Flowise integrates with Pinecone, Chroma, Supabase, Weaviate, and others for storing document embeddings used in RAG workflows.
Flowise integrates with Pinecone, Chroma, Supabase, Weaviate, and others for storing document embeddings used in RAG workflows.
5. How are prompts constructed in Flowise?
Prompts are defined through Prompt Template nodes that can use placeholders, variables, and dynamic inputs to create structured interactions with the LLM.
Prompts are defined through Prompt Template nodes that can use placeholders, variables, and dynamic inputs to create structured interactions with the LLM.
6. Can Flowise connect with external APIs?
Yes, using HTTP request nodes or custom tool nodes, you can make API calls to third-party platforms and incorporate responses into workflows.
Yes, using HTTP request nodes or custom tool nodes, you can make API calls to third-party platforms and incorporate responses into workflows.
7. How do you deploy a Flowise agent?
Agents can be deployed on local servers, cloud platforms like Vercel, or embedded in apps using Flowise's REST API or iframe embedding.
Agents can be deployed on local servers, cloud platforms like Vercel, or embedded in apps using Flowise's REST API or iframe embedding.
8. What makes Flowise different from other no-code AI tools?
Flowise gives full transparency, extensibility, and open-source control. It integrates deeply with LangChain, supports custom tooling, and is developer-friendly while remaining accessible to non-coders.
Flowise gives full transparency, extensibility, and open-source control. It integrates deeply with LangChain, supports custom tooling, and is developer-friendly while remaining accessible to non-coders.
9. What is a RAG pipeline and how is it built in Flowise?
A RAG pipeline combines retrieval from vector stores with LLM generation. In Flowise, this involves using a document loader, embedding node, vector DB, and LLM response generator.
A RAG pipeline combines retrieval from vector stores with LLM generation. In Flowise, this involves using a document loader, embedding node, vector DB, and LLM response generator.
10. How can you monitor and debug a Flowise workflow?
Flowise provides a visual trace of node execution, allowing you to inspect inputs, outputs, and logs at each step for debugging and optimization.
Flowise provides a visual trace of node execution, allowing you to inspect inputs, outputs, and logs at each step for debugging and optimization.
Course Quiz Back to Top
FAQs
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