A curated collection of AI agent use cases across industries, showcasing practical applications and linking to open-source projects for implementation. Explore how AI agents are transforming industries like healthcare, finance, education, and more! 🤖✨
Artificial Intelligence (AI) agents are revolutionizing the way industries operate. From personalized learning to financial trading bots, AI agents bring efficiency, innovation, and scalability. This repository provides:
- A categorized list of industries where AI agents are making an impact.
- Detailed use cases with links to open-source projects for implementation.
Whether you're a developer, researcher, or business enthusiast, this repository is your go-to resource for AI agent inspiration and learning.
| Use Case | Industry | Description | Code Github |
|---|---|---|---|
| AME (Autonomous Marketing Engine) | Marketing | Fully Automated Marketing Department & Autonomous Marketing Engine (AME). |  |
| HIA (Health Insights Agent) | Healthcare | analyses medical reports and provide health insights. | |
| AI Health Assistant | Healthcare | Diagnoses and monitors diseases using patient data. | |
| Automated Trading Bot | Finance | Automates stock trading with real-time market analysis. | |
| Virtual AI Tutor | Education | Provides personalized education tailored to users. | |
| 24/7 AI Chatbot | Customer Service | Handles customer queries around the clock. | |
| Product Recommendation Agent | Retail | Suggests products based on user preferences and history. | |
| Self-Driving Delivery Agent | Transportation | Optimizes routes and autonomously delivers packages. | |
| Factory Process Monitoring Agent | Manufacturing | Monitors production lines and ensures quality control. | |
| Property Pricing Agent | Real Estate | Analyzes market trends to determine property prices. | |
| Smart Farming Assistant | Agriculture | Provides insights on crop health and yield predictions. | |
| Energy Demand Forecasting Agent | Energy | Predicts energy usage to optimize grid management. | |
| Content Personalization Agent | Entertainment | Recommends personalized media based on preferences. | |
| Legal Document Review Assistant | Legal | Automates document review and highlights key clauses. | |
| Recruitment Recommendation Agent | Human Resources | Suggests best-fit candidates for job openings. | |
| Virtual Travel Assistant | Hospitality | Plans travel itineraries based on preferences. | |
| AI Game Companion Agent | Gaming | Enhances player experience with real-time assistance. | |
| Real-Time Threat Detection Agent | Cybersecurity | Identifies potential threats and mitigates attacks. | |
| E-commerce Personal Shopper Agent | E-commerce | Helps customers find products they’ll love. | |
| Logistics Optimization Agent | Supply Chain | Plans efficient delivery routes and manages inventory. | |
| Vibe Hacking Agent | Cybersecurity | Autonomous Multi-Agent Based Red Team Testing Service. | |
| MediSuite-Ai-Agent | Health insurance | A medical ai agent that helps automating the process of hospitals / insurance claiming workflow. | |
| Lina-Egyptian-Medical-Chatbot | Health insurance | A medical ai agent that helps automating the process of hospitals / insurance claiming workflow. | |
| Use Case | Industry | Description | GitHub |
|---|---|---|---|
| 📧 Email Auto Responder Flow | 🗣️ Communication | Automates email responses based on predefined criteria to enhance communication efficiency. |  |
| 📝 Meeting Assistant Flow | 🛠️ Productivity | Assists in organizing and managing meetings, including scheduling and agenda preparation. | |
| 🔄 Self Evaluation Loop Flow | 👥 Human Resources | Facilitates self-assessment processes within an organization, aiding in performance reviews. | |
| 📈 Lead Score Flow | 💼 Sales | Evaluates and scores potential leads to prioritize outreach in sales strategies. | |
| 📊 Marketing Strategy Generator | 📢 Marketing | Develops marketing strategies by analyzing market trends and audience data. | |
| 📝 Job Posting Generator | 🧑💼 Recruitment | Creates job postings by analyzing job requirements, aiding in recruitment processes. | |
| 🔄 Recruitment Workflow | 🧑💼 Recruitment | Streamlines the recruitment process by automating various tasks involved in hiring. | |
| 🔍 Match Profile to Positions | 🧑💼 Recruitment | Matches candidate profiles to suitable job positions to enhance recruitment efficiency. | |
| 📸 Instagram Post Generator | 📱 Social Media | Generates and schedules Instagram posts automatically, streamlining social media management. | |
| 🌐 Landing Page Generator | 💻 Web Development | Automates the creation of landing pages for websites, facilitating web development tasks. | |
| 🎮 Game Builder Crew | 🎮 Game Development | Assists in the development of games by automating certain aspects of game creation. | |
| 💹 Stock Analysis Tool | 💰 Finance | Provides tools for analyzing stock market data to assist in financial decision-making. | |
| 🗺️ Trip Planner | Assists in planning trips by organizing itineraries and managing travel details. | |
|
| 🎁 Surprise Trip Planner | Plans surprise trips by selecting destinations and activities based on user preferences. | |
|
| 📚 Write a Book with Flows | ✍️ Creative Writing | Assists authors in writing books by providing structured workflows and writing assistance. | |
| 🎬 Screenplay Writer | ✍️ Creative Writing | Aids in writing screenplays by offering templates and guidance for script development. | |
| ✅ Markdown Validator | 📄 Documentation | Validates Markdown files to ensure proper formatting and adherence to standards. | |
| 🧠 Meta Quest Knowledge | 📚 Knowledge Management | Manages and organizes knowledge related to Meta Quest, facilitating information retrieval. | |
| 🤖 NVIDIA Models Integration | 🤖 AI Integration | Integrates NVIDIA AI models into workflows to enhance computational capabilities. | |
| 🗂️ Prep for a Meeting | 🛠️ Productivity | Assists in preparing for meetings by organizing materials and setting agendas. | |
| 🛠️Starter Template | 🛠️ Development | Provides a starter template for new projects to streamline the setup process. | |
| 🔗CrewAI + LangGraph Integration | 🤖 AI Integration | Demonstrates integration between CrewAI and LangGraph for enhanced workflow automation. |
Code Generation, Execution, and Debugging
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🤖 Automated Task Solving with Code Generation, Execution & Debugging | 💻 Software Development | Demonstrates automated task-solving by generating, executing, and debugging code. |  |
| 🧑💻 Automated Code Generation and Question Answering with Retrieval Augmented Agents | 💻 Software Development | Generates code and answers questions using retrieval-augmented methods. | |
| 🧠 Automated Code Generation and Question Answering with Qdrant-based Retrieval | 💻 Software Development | Utilizes Qdrant for enhanced retrieval-augmented agent performance. | |
Multi-Agent Collaboration (>3 Agents)
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🤝 Automated Task Solving by Group Chat (3 members, 1 manager) | 🤝 Collaboration | Demonstrates group task-solving via multi-agent collaboration. | |
| 📊 Automated Data Visualization by Group Chat (3 members, 1 manager) | 📊 Data Analysis | Uses multi-agent collaboration to create data visualizations. | |
| 🧩 Automated Complex Task Solving by Group Chat (6 members, 1 manager) | 🤝 Collaboration | Solves complex tasks collaboratively with a larger group of agents. | |
| 🧑💻 Automated Task Solving with Coding & Planning Agents | 🛠️ Planning & Development | Combines coding and planning agents for solving tasks effectively. | |
| 📐 Automated Task Solving with Transition Paths Specified in a Graph | 🤝 Collaboration | Uses predefined transition paths in a graph for solving tasks. | |
| 🧠 Running a Group Chat as an Inner-Monologue via the SocietyOfMindAgent | 🧠 Cognitive Sciences | Simulates inner-monologue for problem-solving using group chats. | |
| 🔧 Running a Group Chat with Custom Speaker Selection Function | 🤝 Collaboration | Implements a custom function for speaker selection in group chats. | |
Sequential Multi-Agent Chats
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🔄 Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent | 🔄 Workflow Automation | Automates sequential task-solving with a single initiating agent. | |
| ⏳ Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent | 🔄 Workflow Automation | Handles asynchronous task-solving in a sequence of chats initiated by one agent. | |
| 🤝 Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents | 🔄 Workflow Automation | Facilitates sequential task-solving with different agents initiating each chat. | |
Nested Chats
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🧠 Solving Complex Tasks with Nested Chats | 🧠 Problem Solving | Uses nested chats to solve hierarchical and complex problems. | |
| 🔄 Solving Complex Tasks with A Sequence of Nested Chats | 🧠 Problem Solving | Demonstrates sequential task-solving using nested chats. | |
| 🏭 OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats | 🏭 Supply Chain Optimization | Showcases how to solve supply chain optimization problems using nested chats, a coding agent, and a safeguard agent. | |
| ♟️ Conversational Chess with Nested Chats and Tool Use | 🎮 Gaming | Explores the use of nested chats for playing conversational chess with integrated tools. | |
Application
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🔄 Automated Continual Learning from New Data | 📊 Machine Learning | Continuously learns from new data inputs for adaptive AI. | |
| 🏭 OptiGuide - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization | 🏭 Supply Chain Optimization | Highlights a solution combining coding, tool use, and safeguarding for supply chain optimization. | |
| 🤖 AutoAnny - A Discord bot built using AutoGen | 💬 Communication Tools | Showcases the development of a Discord bot using AutoGen for enhanced interaction. | |
Tools
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🌐 Web Search: Solve Tasks Requiring Web Info | 🔍 Information Retrieval | Searches the web to gather information required for completing tasks. | |
| 🔧 Use Provided Tools as Functions | 🛠️ Tool Integration | Demonstrates how to use pre-provided tools as callable functions in AutoGen. | |
| 🔗 Use Tools via Sync and Async Function Calling | 🛠️ Tool Integration | Illustrates synchronous and asynchronous tool usage within AutoGen workflows. | |
| 🧩 Task Solving with Langchain Provided Tools as Functions | 🔍 Language Processing | Leverages Langchain tools for task-solving within AutoGen. | |
| 📚 RAG: Group Chat with Retrieval Augmented Generation | 🤝 Collaboration | Enables group chat with Retrieval Augmented Generation (RAG) to support information sharing. | |
| ⚙️ Function Inception: Update/Remove Functions During Conversations | 🔧 Development Tools | Allows AutoGen agents to modify their functions dynamically during conversations. | |
| 🔊 Agent Chat with Whisper | 🎙️ Audio Processing | Demonstrates AI agent capabilities for transcription and translation using Whisper. | |
| 📏 Constrained Responses via Guidance | 💡 Natural Language Processing | Shows how to use guidance to constrain responses generated by agents. | |
| 🌍 Browse the Web with Agents | 🌐 Information Retrieval | Explains how to configure agents to browse and retrieve information from the web. | |
| 📊 SQL: Natural Language Text to SQL Query Using Spider Benchmark | 💾 Database Management | Converts natural language inputs into SQL queries using the Spider benchmark. | |
| 🕸️ Web Scraping with Apify | 🌐 Data Gathering | Illustrates web scraping techniques with Apify using AutoGen. | |
| 🕷️ Web Crawling: Crawl Entire Domain with Spider API | 🌐 Data Gathering | Explains how to crawl entire domains using the Spider API. | |
| 💻 Write a Software App Task by Task with Specially Designed Functions | 💻 Software Development | Builds a software application step-by-step using designed functions. | |
Human Development
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 💬 Simple Example in ChatGPT Style | 🧠 Conversational AI | Demonstrates a simple conversational example in the style of ChatGPT. |  |
| 🤖 Auto Code Generation, Execution, Debugging and Human Feedback | 💻 Software Development | Showcases code generation, execution, debugging with human feedback integrated into the workflow. | |
| 👥 Automated Task Solving with GPT-4 + Multiple Human Users | 🤝 Collaboration | Enables task solving with multiple human users collaborating with GPT-4. | |
| 🔄 Agent Chat with Async Human Inputs | 🧠 Conversational AI | Supports asynchronous human input during agent conversations. | |
Agent Teaching and Learning
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 📘 Teach Agents New Skills & Reuse via Automated Chat | 🎓 Education & Training | Demonstrates teaching new skills to agents and enabling their reuse in automated chats. | |
| 🧠 Teach Agents New Facts, User Preferences and Skills Beyond Coding | 🎓 Education & Training | Shows how to teach agents new facts, user preferences, and non-coding skills. | |
| 🤖 Teach OpenAI Assistants Through GPTAssistantAgent | 💻 AI Assistant Development | Illustrates how to enhance OpenAI assistants' capabilities using GPTAssistantAgent. | |
| 🔄 Agent Optimizer: Train Agents in an Agentic Way | 🛠️ Optimization | Explains how to train agents effectively in an agentic manner using the Agent Optimizer. | |
Multi-Agent Chat with OpenAI Assistants in the loop
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🌟 Hello-World Chat with OpenAI Assistant in AutoGen | 🤖 Conversational AI | A basic example of chatting with OpenAI Assistant using AutoGen. | |
| 🔧 Chat with OpenAI Assistant using Function Call | 🔧 Development Tools | Illustrates how to use function calls with OpenAI Assistant in chats. | |
| 🧠 Chat with OpenAI Assistant with Code Interpreter | 💻 Software Development | Demonstrates the use of OpenAI Assistant as a code interpreter in chats. | |
| 🔍 Chat with OpenAI Assistant with Retrieval Augmentation | 📚 Information Retrieval | Enables retrieval-augmented conversations with OpenAI Assistant. | |
| 🤝 OpenAI Assistant in a Group Chat | 🤝 Collaboration | Shows how OpenAI Assistant can collaborate with other agents in a group chat. | |
| 🛠️ GPTAssistantAgent based Multi-Agent Tool Use | 🔧 Development Tools | Explains how to use GPTAssistantAgent for multi-agent tool usage. | |
Non-OpenAI Models
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| ♟️ Conversational Chess using Non-OpenAI Models | 🎮 Gaming | Explores conversational chess implemented with non-OpenAI models. | |
Multimodal Agent
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🎨 Multimodal Agent Chat with DALLE and GPT-4V | 🖼️ Multimedia AI | Combines DALLE and GPT-4V for multimodal agent communication. | |
| 🖌️ Multimodal Agent Chat with Llava | 📷 Image Processing | Uses Llava for enabling multimodal agent conversations with image processing. | |
| 🖼️ Multimodal Agent Chat with GPT-4V | 🖼️ Multimedia AI | Leverages GPT-4V for visual and conversational interactions in multimodal agents. | |
Long Context Handling
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 📜 Long Context Handling as A Capability | 🧠 AI Capability | Demonstrates techniques for handling long context effectively within AI workflows. | |
Evaluation and Assessment
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 📊 AgentEval: A Multi-Agent System for Assessing Utility of LLM-Powered Applications | 📈 Performance Evaluation | Introduces AgentEval for evaluating and assessing the performance of LLM-based applications. | |
Automatic Agent Building
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🏗️ Automatically Build Multi-agent System with AgentBuilder | 🤖 AI Development | Explains how to automatically build multi-agent systems using the AgentBuilder tool. | |
| 📚 Automatically Build Multi-agent System from Agent Library | 🤖 AI Development | Shows how to construct multi-agent systems by leveraging a pre-defined agent library. | |
Observability
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 📊 Track LLM Calls, Tool Usage, Actions and Errors using AgentOps | 📈 Monitoring & Analytics | Demonstrates how to monitor LLM interactions, tool usage, and errors using AgentOps. | |
Enhanced Inferences
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🔗 API Unification | 🔧 API Management | Explains how to unify API usage with documentation and code examples. |  |
| ⚙️ Utility Functions to Help Managing API Configurations Effectively | 🔧 API Management | Demonstrates utility functions to manage API configurations more effectively. | |
| 💰 Cost Calculation | 📈 Cost Management | Introduces methods for tracking token usage and estimating costs for LLM interactions. | |
| ⚡ Optimize for Code Generation | 📊 Optimization | Highlights cost-effective optimization techniques for improving code generation with LLMs. | |
| 📐 Optimize for Math | 📊 Optimization | Explains techniques to optimize LLM performance for solving mathematical problems. | |
UseCase
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🤖 Support Agent | 💻 Software Development / AI / Framework Support | The Agno Support Agent helps developers with the Agno framework by providing real-time answers, explanations, and code examples. |  |
| 🎥 YouTube Agent | 📺 Media & Content | An intelligent agent that analyzes YouTube videos by generating detailed summaries, timestamps, themes, and content breakdowns using AI tools. | |
| 📊 Finance Agent | 💼 Finance | An advanced AI-powered market analyst that delivers real-time stock market insights, analyst recommendations, financial deep-dives, and sector-specific trends. Supports prompts for detailed analysis of companies like AAPL, TSLA, NVDA, etc. | |
| 📚 Study Partner | 🎓 Education | Assists users in learning by finding resources, answering questions, and creating study plans. | |
| 🛍️ Shopping Partner Agent | 🏬 E-commerce | A product recommender agent that helps users find matching products based on preferences from trusted platforms like Amazon, Flipkart, etc. | |
| 🎓 Research Scholar Agent | 🧠 Education / Research | An AI-powered academic assistant that performs advanced academic searches, analyzes recent publications, synthesizes findings across disciplines, and writes well-structured academic reports with proper citations. | |
| 🧠 Research Agent | 🗞️ Media & Journalism | A research agent that combines web search and professional journalistic writing. It performs deep investigations and produces NYT-style reports. | |
| 🍳 Recipe Creator | 🍽️ Food & Culinary | An AI-powered recipe recommendation agent that provides personalized recipes based on ingredients, preferences, and time constraints. | |
| 🗞️ Finance Agent | 💼 Finance | A powerful financial analyst agent combining real-time stock data, analyst insights, company fundamentals, and market news. Ideal for analyzing companies like Apple, Tesla, NVIDIA, and sectors like semiconductors or automotive. | |
| 🧠 Financial Reasoning Agent | 📈 Finance | Uses a Claude-3.5 Sonnet-based agent to analyze stocks like NVDA using tools for reasoning and Yahoo Finance data. | |
| 🤖 Readme Generator Agent | 💻 Software Dev | Agent generates high-quality READMEs for GitHub repositories using repo metadata. | |
| 🎬 Movie Recommendation Agent | 🎥 Entertainment | An intelligent agent that gives personalized movie recommendations using Exa and GPT-4o, analyzing genres, themes, and latest ratings. | |
| 🔍 Media Trend Analysis Agent | 📰 Media & News | Analyzes emerging trends, patterns, and influencers from digital platforms using AI-powered agents and scraping. | |
| ⚖️ Legal Document Analysis Agent | 🏛️ Legal Tech | An AI agent that analyzes legal documents from PDF URLs and provides legal insights based on a knowledge base using vector embeddings and GPT-4o. | |
| 🤔 DeepKnowledge | 🧠 Research | This agent performs iterative searches through its knowledge base, breaking down complex queries into sub-questions and synthesizing comprehensive answers. It uses Agno docs for demonstration and is designed for deep reasoning and exploration. | |
| 📚 Book Recommendation Agent | 🧠 Publishing & Media | An intelligent agent that provides personalized book suggestions using literary data, reader preferences, reviews, and release info. | |
| 🏠 MCP Airbnb Agent | 🛎️ Hospitality | Create an AI Agent using MCP and Llama 4 to search Airbnb listings with filters like workspace & transport proximity. | |
| 🤖 Assist Agent | 🧠 AI Framework | An AI agent using GPT-4o to answer questions about the Agno framework with hybrid search and embedded knowledge. | |
UseCase
| Use Case | Industry | Description | Notebook |
|---|---|---|---|
| 🤖 Chatbot Simulation Evaluation | 💻 💬 AI / Quality Assurance | Simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios. | |
| 🧠 Information Gathering via Prompting | 🧠 AI / Research & Development | This tutorial demonstrates how to design a LangGraph workflow that utilizes prompting techniques to gather information effectively. It showcases how to structure prompts and manage the flow of information to build intelligent agents. | |
| 🧠 Code Assistant with LangGraph | 💻 Software Development | This tutorial demonstrates how to build a resilient code assistant using LangGraph. It guides you through creating a graph-based agent that can handle code generation, error checking, and iterative refinement, ensuring robust and accurate coding assistance. | |
| 🧑💼 Customer Support Agent | 🧑💼 Customer Support Agent | This tutorial demonstrates how to build a customer support agent using LangGraph. It guides you through creating a graph-based agent that can handle customer inquiries, providing automated support and enhancing user experience. | |
| 🔁 Extraction with Retries | 🧠 AI / Data Extraction | This tutorial demonstrates how to implement retry mechanisms in LangGraph workflows, ensuring robust data extraction processes that can handle transient errors and improve reliability. | |
| 🧠 Multi-Agent Workflow | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to build a multi-agent system using LangGraph's agent supervisor. It guides you through creating a supervisor agent that orchestrates multiple specialized agents, managing task delegation and communication flow. | |
| 🧠 Hierarchical Agent Teams | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to build a hierarchical agent system using LangGraph. It guides you through creating a top-level supervisor agent that delegates tasks to specialized sub-agents, enabling complex workflows with clear task delegation and communication. | |
| 🤝 Multi-Agent Collaboration | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to implement multi-agent collaboration using LangGraph. It guides you through creating multiple specialized agents that work together to accomplish a complex task, showcasing the power of agent collaboration in AI workflows. | |
| 🧠 Plan-and-Execute Agent | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to build a "Plan-and-Execute" style agent using LangGraph. It guides you through creating an agent that first generates a multi-step plan and then executes each step sequentially, revisiting and modifying the plan as necessary. This approach is inspired by the Plan-and-Solve paper and the Baby-AGI project, aiming to enhance long-term planning and task execution in AI workflows. | |
| 🧠 SQL Agent | 🧠 AI / Database Interaction | This tutorial demonstrates how to build an agent that can answer questions about a SQL database. The agent fetches available tables, determines relevance to the question, retrieves schemas, generates a query, checks for errors, executes it, and formulates a response. | |
| 🧠 Reflection Agent | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to build a reflection agent using LangGraph. It guides you through creating an agent that can critique and revise its own outputs, enhancing the quality and reliability of generated content. | |
| 🧠 Reflexion Agent | 🧠 AI / Workflow Orchestration | This tutorial demonstrates how to build a reflexion agent using LangGraph. It guides you through creating an agent that can reflect on its actions and outcomes, enabling iterative improvement and more accurate decision-making in complex workflows. | |
| LangGraph Agentic RAG | |||
| 🧠 Adaptive RAG | 🧠 AI / Information Retrieval | This tutorial demonstrates how to build an Adaptive RAG system using LangGraph. It guides you through creating a dynamic retrieval process that adjusts based on query complexity, enhancing the efficiency and accuracy of information retrieval. | |
| 🧠 Adaptive RAG (Local) | 🧠 AI / Information Retrieval | This tutorial focuses on implementing Adaptive RAG with local models, allowing for offline retrieval and generation, which is crucial for environments with limited internet access or privacy concerns. | |
| 🤖 Agentic RAG | 🤖 AI / Intelligent Agents | Learn to build an Agentic RAG system where an agent determines the best retrieval strategy before generating a response, improving the relevance and accuracy of answers. | |
| 🤖 Agentic RAG (Local) | 🤖 AI / Intelligent Agents | This tutorial extends Agentic RAG to local environments, enabling the use of local models and data sources for retrieval and generation tasks. | |
| 🧠 Corrective RAG (CRAG) | 🧠 AI / Information Retrieval | Implement a Corrective RAG system that evaluates and refines retrieved documents before passing them to the generator, ensuring higher-quality outputs. | |
| 🧠 Corrective RAG (Local) | 🧠 AI / Information Retrieval | This tutorial focuses on building a Corrective RAG system using local resources, allowing for offline document evaluation and refinement processes. | |
| 🧠 Self-RAG | 🧠 AI / Information Retrieval | Learn to implement Self-RAG, where the system reflects on its responses and retrieves additional information if necessary, enhancing the accuracy and relevance of generated content. | |
| 🧠 Self-RAG (Local) | 🧠 AI / Information Retrieval | This tutorial demonstrates how to implement Self-RAG using local models and data sources, enabling offline reflection and retrieval processes. | |
This repository created by collecting info from many sources (using AI Research models and manual labour). Credits: ashishpatel26, AiCMO, Langchain-ai Agno, Autogen, CrewAI
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