Tool Calling Agent
Agent that calls tools and uses a custom system prompt to guide the LLM in answering questions about YouTube video transcripts.
YouTube Video Chatbot
Interact with any YouTube video by chatting with its transcript. Instantly extract and query video content to get concise, AI-powered answers to your questions about the video.
Flows
How the AI Flow works
- User initiates chat.
- The workflow begins when a user opens the chat interface.
- Welcome message displayed.
- A welcome message guides the user to enter a YouTube video URL.
- Fetch YouTube transcript.
- The system retrieves the transcript from the provided YouTube URL.
- AI agent answers questions.
- An AI agent uses the transcript to answer user queries about the video content.
- Display answers in chat.
- The user receives concise, AI-generated responses directly in the chat interface.
Prompts used in this flow
Components used in this flow
Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.
Chat Opened Trigger
The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.
Message Widget
The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.
Chat Output
Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.
Chat History Component
The Chat History component in FlowHunt enables chatbots to remember previous messages, ensuring coherent conversations and improved customer experience while optimizing memory and token usage.
ChatInput
The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.
Tool Calling Agent
Explore the Tool Calling Agent in FlowHunt—an advanced workflow component that enables AI agents to intelligently select and use external tools to answer complex queries. Perfect for building smart AI solutions that require dynamic tool usage, iterative reasoning, and integration with multiple resources.
URL Retriever
Unlock web content in your workflows with the URL Retriever component. Effortlessly extract and process the text and metadata from any list of URLs—including web articles, documents, and more. Supports advanced options like OCR for images, selective metadata extraction, and customizable caching, making it ideal for building knowledge-rich AI flows and automations.
Flow description
Purpose and benefits
This workflow enables users to interactively chat with the transcript of any YouTube video. By simply providing a YouTube URL, users can ask questions and receive concise answers based on the video’s transcript. This system is designed to make long-form video content easily accessible and searchable through conversational AI.
Step-by-Step Workflow Description
1. Chat Initialization and User Guidance
- Chat Opened Trigger: The workflow is initiated when a user opens the chat. This triggers the process and prepares the interface for user interaction.
- Welcome Message: A message widget displays a friendly welcome:
"👋 Welcome to the Chat with a YouTube video tool! I’m here to help you turn long YouTube videos into concise answers🌐. Simply enter the URL of the YouTube video and wait for a bit. I’ll let you know when I’m ready to answer your questions. ✨📹" - Message Output: The welcome message is shown to the user in the chat output, guiding them to enter a YouTube video URL.
2. User Input Handling
- Chat Input: The system listens for user input, which typically includes a YouTube video URL and any follow-up questions.
- Chat History: All previous chat messages are stored in memory, allowing for context-aware responses and continuous multi-turn conversations.
3. Video Transcript Retrieval
- URL Retriever: When a YouTube URL is provided, the workflow uses a URL content retriever node to extract the transcript (or other available textual content) from the video. This node is configured to handle up to 30,000 tokens, enabling it to process long videos.
4. Agent-Powered Q&A
- Tool Calling Agent:
- The agent is instructed to act as a professional YouTube researcher and personal assistant.
- Upon receiving a user query, the agent uses the transcript (retrieved by the URL retriever) as its knowledge base.
- The system prompt ensures the agent provides concise, accurate answers and avoids making up information (“hallucination”) if the answer is not found in the transcript.
- The agent leverages chat history to maintain context across multiple questions.
- Answer Output: The agent’s response is output back to the user in the chat interface, closing the loop for each question.
Workflow Structure
| Step | Component | Purpose |
|---|---|---|
| 1. Chat Start | ChatOpenedTrigger, MessageWidget | Greet user and provide instructions |
| 2. User Input | ChatInput, ChatHistory | Receive user queries and remember conversation history |
| 3. Transcript Fetch | URLContent | Extract transcript from YouTube video |
| 4. Q&A Agent | ToolCallingAgent | Answer user questions using the transcript and chat context |
| 5. Output | ChatOutput | Display messages and answers to the user |
Benefits & Use Cases
- Scalability: This workflow allows anyone to interact with potentially unlimited YouTube videos without manual transcript reading.
- Automation: The process of extracting transcripts and answering questions is fully automated, saving hours of manual work.
- Enhanced Accessibility: Users can quickly get answers from lengthy educational, lecture, or documentary videos without watching the entire content.
- Knowledge Retention: Context-aware multi-turn chat preserves the flow of conversation, supporting more complex queries and follow-ups.
Example Use Cases
- Quickly summarize key points from a long interview or documentary.
- Ask for definitions, explanations, or clarifications about parts of a video.
- Extract lists, timelines, or other structured information from video content.
- Support research by enabling fast Q&A across multiple video sources.
Conclusion
This workflow brings powerful automation and AI-driven conversation to YouTube video content, making it a valuable tool for educators, researchers, students, and content consumers who want to extract value from video without manual effort. It can be easily scaled and generalized for various types of video content, maximizing productivity and accessibility.
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