Agent Academy

Agent Academy is a free, source-linked library of skills, prompts, MCP servers and bot templates, organised into courses by profession, for people who hand real work to AI agents: 1,467 live resources, each with its original link, license and safety tags.

Case

Customer Support Voice Agent

An OpenAI SDK powered customer support agent application that delivers voice-powered responses to questions about your knowledge base using OpenAI's GPT-4o and TTS capabilities. The system crawls through documentation websites with Firecrawl, processes the content into a searchable knowledge base with Qdrant, and provides both text and voice responses to user queries.

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https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/voice_ai_agents/customer_support_voice_agent

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🎙️ Customer Support Voice Agent

🎓 FREE Step-by-Step Tutorial

👉 Click here to follow our complete step-by-step tutorial and learn how to build this from scratch with detailed code walkthroughs, explanations, and best practices.

An OpenAI SDK powered customer support agent application that delivers voice-powered responses to questions about your knowledge base using OpenAI's GPT-4o and TTS capabilities. The system crawls through documentation websites with Firecrawl, processes the content into a searchable knowledge base with Qdrant, and provides both text and voice responses to user queries.

Features

  • Knowledge Base Creation
  • Crawls documentation websites using Firecrawl
  • Stores and indexes content using Qdrant vector database
  • Generates embeddings for semantic search capabilities using FastEmbed
  • AI Agent Team
  • Documentation Processor: Analyzes documentation content and generates clear, concise responses to user queries
  • TTS Agent: Converts text responses into natural-sounding speech with appropriate pacing and emphasis
  • Voice Customization: Supports multiple OpenAI TTS voices:
  • alloy, ash, ballad, coral, echo, fable, onyx, nova, sage, shimmer, verse
  • Interactive Interface
  • Clean Streamlit UI with sidebar configuration
  • Real-time documentation search and response generation
  • Built-in audio player with download capability
  • Progress indicators for system initialization and query processing

How to Run

  1. Setup Environment

```bash

# Clone the repository

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

cd awesome-llm-apps/voice_ai_agents/customer_support_voice_agent

# Install dependencies

pip install -r requirements.txt

```

  1. Configure API Keys
  1. Run the Application

```bash

streamlit run customer_support_voice_agent.py

```

  1. Use the Interface
  • Enter API credentials in the sidebar
  • Input the documentation URL you want to learn about
  • Select your preferred voice from the dropdown
  • Click "Initialize System" to process the documentation
  • Ask questions and receive both text and voice responses

Features in Detail

  • Knowledge Base Creation
  • Builds a searchable knowledge base from your documentation
  • Preserves document structure and metadata
  • Supports multiple page crawling (limited to 5 pages per default configuration)
  • Vector Search
  • Uses FastEmbed for generating embeddings
  • Semantic search capabilities for finding relevant content
  • Efficient document retrieval using Qdrant
  • Voice Generation
  • High-quality text-to-speech using OpenAI's TTS models
  • Multiple voice options for customization
  • Natural speech patterns with proper pacing and emphasis

Related

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A structured output agent demonstrating Pydantic schema-based responses for customer support ticket creation.

Sourcegithub.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_framework_crash_course/openai_sdk_crash_course/2_structured_output_agent/2_1_support_ticket_agent

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Sourcegithub.com/Shubhamsaboo/awesome-llm-apps/tree/main/ai_agent_framework_crash_course/google_adk_crash_course/3_structured_output_agent/3_1_customer_support_ticket_agent

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CaseCustomer service

ai_customer_support_agent

This Streamlit app implements an AI-powered customer support agent for synthetic data generated using GPT-4o. The agent uses OpenAI's GPT-4o model and maintains a memory of past interactions using the Mem0 library with Qdrant as the vector store.

Sourcegithub.com/Shubhamsaboo/awesome-llm-apps/tree/main/advanced_ai_agents/single_agent_apps/ai_customer_support_agent

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TutorialCustomer service

Using tool_choice=required for customer service (OpenAI cookbook)

Using Tool Required for Customer Service The ChatCompletion endpoint now includes the ability to specify whether a tool must be called every time, by adding tool_choice='required' as a parameter. This adds an element of determinism to how you build your wrapping application, as you can count on a to

Sourcegithub.com/openai/openai-cookbook/blob/main/examples/Using_tool_required_for_customer_service.ipynb

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Customer service agent with tool use (Claude cookbook)

Creating a Customer Service Agent with Client-Side Tools In this recipe, we'll demonstrate how to create a customer service chatbot using Claude 3 plus client-side tools. The chatbot will be able to look up customer information, retrieve order details, and cancel orders on behalf of the customer. We

Sourcegithub.com/anthropics/claude-cookbooks/blob/main/tool_use/customer_service_agent.ipynb

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Awesome listCustomer service

500 AI Agents Projects (includes customer service use cases)

Curated collection of AI agent use cases across industries (incl. Customer Service, E-commerce) with links to open-source implementations, e.g. a LangGraph customer-support agent tutorial.

Sourcegithub.com/ashishpatel26/500-AI-Agents-Projects

No credentialsDoes not sendMIT