├── .github
└── workflows
│ └── jekyll-gh-pages.yml
├── README.md
└── images
├── AIAgentUseCase.jpg
├── Awesome AI Agent UseCases Industry Include _ Healthcare, Finance, Education, Customer Service, Retail, Transportation, Manufacturing, RealEstate, Agriculture, Energy, Entertainment, Legal, Human Resource, Hosp (1).jpg
├── Awesome AI Agent UseCases Industry Include _ Healthcare, Finance, Education, Customer Service, Retail, Transportation, Manufacturing, RealEstate, Agriculture, Energy, Entertainment, Legal, Human Resource, Hospital.jpg
├── industry_usecase.png
└── industry_usecase1.png
/.github/workflows/jekyll-gh-pages.yml:
--------------------------------------------------------------------------------
1 | # Sample workflow for building and deploying a Jekyll site to GitHub Pages
2 | name: Deploy Jekyll with GitHub Pages dependencies preinstalled
3 |
4 | on:
5 | # Runs on pushes targeting the default branch
6 | push:
7 | branches: ["main"]
8 |
9 | # Allows you to run this workflow manually from the Actions tab
10 | workflow_dispatch:
11 |
12 | # Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
13 | permissions:
14 | contents: read
15 | pages: write
16 | id-token: write
17 |
18 | # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
19 | # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
20 | concurrency:
21 | group: "pages"
22 | cancel-in-progress: false
23 |
24 | jobs:
25 | # Build job
26 | build:
27 | runs-on: ubuntu-latest
28 | steps:
29 | - name: Checkout
30 | uses: actions/checkout@v4
31 | - name: Setup Pages
32 | uses: actions/configure-pages@v5
33 | - name: Build with Jekyll
34 | uses: actions/jekyll-build-pages@v1
35 | with:
36 | source: ./
37 | destination: ./_site
38 | - name: Upload artifact
39 | uses: actions/upload-pages-artifact@v3
40 |
41 | # Deployment job
42 | deploy:
43 | environment:
44 | name: github-pages
45 | url: ${{ steps.deployment.outputs.page_url }}
46 | runs-on: ubuntu-latest
47 | needs: build
48 | steps:
49 | - name: Deploy to GitHub Pages
50 | id: deployment
51 | uses: actions/deploy-pages@v4
52 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # ? 500+ AI Agent Projects / UseCases
2 |
3 | [](https://github.com/ashishpatel26/500-AI-Agents-Projects)
4 |
5 | 
6 |
7 | 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! ??
8 |
9 | ---
10 |
11 | ## ? Table of Contents
12 |
13 | - [Introduction](#introduction)
14 | - [Industry Usecase](#-industry-usecase-mindmap)
15 | - [Use Case Table](#use-case-table)
16 | - [Framework Wise UseCase](#framework-wise-usecases)
17 | - [CrewAI UseCase](#framework-name-crewai)
18 | - [AutoGen UseCase](#framework-name-autogen)
19 | - [Agno UseCase](#framework-name-agno)
20 | - [Langgraph UseCase](#framework-name-langgraph)
21 | - [Contributing](#contributing)
22 | - [License](#license)
23 |
24 | ---
25 |
26 | ## ? Introduction
27 |
28 | 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:
29 |
30 | - A categorized list of industries where AI agents are making an impact.
31 | - Detailed use cases with links to open-source projects for implementation.
32 |
33 | Whether you're a developer, researcher, or business enthusiast, this repository is your go-to resource for AI agent inspiration and learning.
34 |
35 | ---
36 |
37 | ## ? Industry UseCase MindMap
38 |
39 | 
40 |
41 | ---
42 |
43 | ## ? Use Case Table
44 |
45 | | Use Case | Industry | Description | Code Github |
46 | | ------------------------------------------- | ---------------- | -------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
47 | | **HIA (Health Insights Agent)** | Healthcare | analyses medical reports and provide health insights. | [](https://github.com/harshhh28/hia.git) |
48 | | **AI Health Assistant** | Healthcare | Diagnoses and monitors diseases using patient data. | [](https://github.com/ahmadvh/AI-Agents-for-Medical-Diagnostics.git) |
49 | | **Automated Trading Bot** | Finance | Automates stock trading with real-time market analysis. | [](https://github.com/MingyuJ666/Stockagent.git) |
50 | | **Virtual AI Tutor** | Education | Provides personalized education tailored to users. | [](https://github.com/hqanhh/EduGPT.git) |
51 | | **24/7 AI Chatbot** | Customer Service | Handles customer queries around the clock. | [](https://github.com/NirDiamant/GenAI_Agents/blob/main/all_agents_tutorials/customer_support_agent_langgraph.ipynb) |
52 | | **Product Recommendation Agent** | Retail | Suggests products based on user preferences and history. | [](https://github.com/microsoft/RecAI) |
53 | | **Self-Driving Delivery Agent** | Transportation | Optimizes routes and autonomously delivers packages. | [](https://github.com/sled-group/driVLMe) |
54 | | **Factory Process Monitoring Agent** | Manufacturing | Monitors production lines and ensures quality control. | [](https://github.com/yuchenxia/llm4ias) |
55 | | **Property Pricing Agent** | Real Estate | Analyzes market trends to determine property prices. | [](https://github.com/AleksNeStu/ai-real-estate-assistant) |
56 | | **Smart Farming Assistant** | Agriculture | Provides insights on crop health and yield predictions. | [](https://github.com/mohammed97ashraf/LLM_Agri_Bot) |
57 | | **Energy Demand Forecasting Agent** | Energy | Predicts energy usage to optimize grid management. | [](https://github.com/yecchen/MIRAI) |
58 | | **Content Personalization Agent** | Entertainment | Recommends personalized media based on preferences. | [](https://github.com/crosleythomas/MirrorGPT) |
59 | | **Legal Document Review Assistant** | Legal | Automates document review and highlights key clauses. | [](https://github.com/firica/legalai) |
60 | | **Recruitment Recommendation Agent** | Human Resources | Suggests best-fit candidates for job openings. | [](https://github.com/sentient-engineering/jobber) |
61 | | **Virtual Travel Assistant** | Hospitality | Plans travel itineraries based on preferences. | [](https://github.com/nirbar1985/ai-travel-agent) |
62 | | **AI Game Companion Agent** | Gaming | Enhances player experience with real-time assistance. | [](https://github.com/onjas-buidl/LLM-agent-game) |
63 | | **Real-Time Threat Detection Agent** | Cybersecurity | Identifies potential threats and mitigates attacks. | [](https://github.com/NVISOsecurity/cyber-security-llm-agents) |
64 | | **E-commerce Personal Shopper Agent** | E-commerce | Helps customers find products they’ll love. | [](https://github.com/Hoanganhvu123/ShoppingGPT) |
65 | | **Logistics Optimization Agent** | Supply Chain | Plans efficient delivery routes and manages inventory. | [](https://github.com/microsoft/OptiGuide) |
66 | | **Vibe Hacking Agent** | Cybersecurity | Autonomous Multi-Agent Based Red Team Testing Service. | [](https://github.com/PurpleAILAB/Decepticon) |
67 | | **MediSuite-Ai-Agent** | Health insurance | A medical ai agent that helps automating the process of hospitals / insurance claiming workflow. | [](https://github.com/MahmoudRabea13/MediSuite-Ai-Agent) |
68 | | **Lina-Egyptian-Medical-Chatbot** | Health insurance | A medical ai agent that helps automating the process of hospitals / insurance claiming workflow. | [](https://github.com/MahmoudRabea13/MediSuite-Ai-Agent) |
69 |
70 | ## Framework wise Usecases
71 |
72 | ---
73 |
74 | ### **Framework Name**: **CrewAI**
75 |
76 | | Use Case | Industry | Description | GitHub |
77 | | -------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------- |
78 | | ? Email Auto Responder Flow | ?? Communication | Automates email responses based on predefined criteria to enhance communication efficiency. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/email_auto_responder_flow) |
79 | | ? Meeting Assistant Flow | ?? Productivity | Assists in organizing and managing meetings, including scheduling and agenda preparation. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/meeting_assistant_flow) |
80 | | ? Self Evaluation Loop Flow | ? Human Resources | Facilitates self-assessment processes within an organization, aiding in performance reviews. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/self_evaluation_loop_flow) |
81 | | ? Lead Score Flow | ? Sales | Evaluates and scores potential leads to prioritize outreach in sales strategies. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/lead-score-flow) |
82 | | ? Marketing Strategy Generator | ? Marketing | Develops marketing strategies by analyzing market trends and audience data. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/marketing_strategy) |
83 | | ? Job Posting Generator | ??? Recruitment | Creates job postings by analyzing job requirements, aiding in recruitment processes. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/job-posting) |
84 | | ? Recruitment Workflow | ??? Recruitment | Streamlines the recruitment process by automating various tasks involved in hiring. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/recruitment) |
85 | | ? Match Profile to Positions | ??? Recruitment | Matches candidate profiles to suitable job positions to enhance recruitment efficiency. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/match_profile_to_positions) |
86 | | ? Instagram Post Generator | ? Social Media | Generates and schedules Instagram posts automatically, streamlining social media management. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/instagram_post) |
87 | | ? Landing Page Generator | ? Web Development | Automates the creation of landing pages for websites, facilitating web development tasks. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/landing_page_generator) |
88 | | ? Game Builder Crew | ? Game Development | Assists in the development of games by automating certain aspects of game creation. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/game-builder-crew) |
89 | | ? Stock Analysis Tool | ? Finance | Provides tools for analyzing stock market data to assist in financial decision-making. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/stock_analysis) |
90 | | ?? Trip Planner | ?? Travel | Assists in planning trips by organizing itineraries and managing travel details. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/trip_planner) |
91 | | ? Surprise Trip Planner | ?? Travel | Plans surprise trips by selecting destinations and activities based on user preferences. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/surprise_trip) |
92 | | ? Write a Book with Flows | ?? Creative Writing | Assists authors in writing books by providing structured workflows and writing assistance. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/write_a_book_with_flows) |
93 | | ? Screenplay Writer | ?? Creative Writing | Aids in writing screenplays by offering templates and guidance for script development. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/screenplay_writer) |
94 | | ? Markdown Validator | ? Documentation | Validates Markdown files to ensure proper formatting and adherence to standards. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/markdown_validator) |
95 | | ? Meta Quest Knowledge | ? Knowledge Management | Manages and organizes knowledge related to Meta Quest, facilitating information retrieval. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/meta_quest_knowledge) |
96 | | ? NVIDIA Models Integration | ? AI Integration | Integrates NVIDIA AI models into workflows to enhance computational capabilities. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/nvidia_models) |
97 | | ?? Prep for a Meeting | ?? Productivity | Assists in preparing for meetings by organizing materials and setting agendas. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/prep-for-a-meeting) |
98 | | ??Starter Template | ?? Development | Provides a starter template for new projects to streamline the setup process. | [](https://github.com/crewAIInc/crewAI-examples/tree/main/starter_template) |
99 | | ?CrewAI + LangGraph Integration | ? AI Integration | Demonstrates integration between CrewAI and LangGraph for enhanced workflow automation. | |
100 |
101 | ### **Framework Name**: **Autogen**
102 |
103 | > **Code Generation, Execution, and Debugging**
104 |
105 | | Use Case | Industry | Description | Notebook |
106 | | --------------------------------------------------------------------------------------- | ----------------------- | --------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
107 | | ? Automated Task Solving with Code Generation, Execution & Debugging | ? Software Development | Demonstrates automated task-solving by generating, executing, and debugging code. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_auto_feedback_from_code_execution) |
108 | | ??? Automated Code Generation and Question Answering with Retrieval Augmented Agents | ? Software Development | Generates code and answers questions using retrieval-augmented methods. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_RetrieveChat) |
109 | | ? Automated Code Generation and Question Answering with Qdrant-based Retrieval | ? Software Development | Utilizes Qdrant for enhanced retrieval-augmented agent performance. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_RetrieveChat_qdrant) |
110 |
111 | > **Multi-Agent Collaboration (>3 Agents)**
112 |
113 | | Use Case | Industry | Description | Notebook |
114 | | :----------------------------------------------------------------------- | :-------------------------- | :------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
115 | | ? Automated Task Solving by Group Chat (3 members, 1 manager) | ? Collaboration | Demonstrates group task-solving via multi-agent collaboration. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_groupchat) |
116 | | ? Automated Data Visualization by Group Chat (3 members, 1 manager) | ? Data Analysis | Uses multi-agent collaboration to create data visualizations. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_groupchat_vis) |
117 | | ? Automated Complex Task Solving by Group Chat (6 members, 1 manager) | ? Collaboration | Solves complex tasks collaboratively with a larger group of agents. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_groupchat_research) |
118 | | ??? Automated Task Solving with Coding & Planning Agents | ?? Planning & Development | Combines coding and planning agents for solving tasks effectively. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_planning.ipynb) |
119 | | ? Automated Task Solving with Transition Paths Specified in a Graph | ? Collaboration | Uses predefined transition paths in a graph for solving tasks. | [](https://microsoft.github.io/autogen/docs/notebooks/agentchat_groupchat_finite_state_machine) |
120 | | ? Running a Group Chat as an Inner-Monologue via the SocietyOfMindAgent | ? Cognitive Sciences | Simulates inner-monologue for problem-solving using group chats. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_society_of_mind) |
121 | | ? Running a Group Chat with Custom Speaker Selection Function | ? Collaboration | Implements a custom function for speaker selection in group chats. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_groupchat_customized) |
122 |
123 | > **Sequential Multi-Agent Chats**
124 |
125 | | Use Case | Industry | Description | Notebook |
126 | | :--------------------------------------------------------------------------------- | :--------------------- | :------------------------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- |
127 | | ? Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent | ? Workflow Automation | Automates sequential task-solving with a single initiating agent. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_multi_task_chats) |
128 | | ? 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. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_multi_task_async_chats) |
129 | | ? Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents | ? Workflow Automation | Facilitates sequential task-solving with different agents initiating each chat. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchats_sequential_chats) |
130 |
131 | > **Nested Chats**
132 |
133 | | Use Case | Industry | Description | Notebook |
134 | | :----------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------- |
135 | | ? Solving Complex Tasks with Nested Chats | ? Problem Solving | Uses nested chats to solve hierarchical and complex problems. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nestedchat) |
136 | | ? Solving Complex Tasks with A Sequence of Nested Chats | ? Problem Solving | Demonstrates sequential task-solving using nested chats. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nested_sequential_chats) |
137 | | ? 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. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nestedchat_optiguide) |
138 | | ?? Conversational Chess with Nested Chats and Tool Use | ? Gaming | Explores the use of nested chats for playing conversational chess with integrated tools. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nested_chats_chess) |
139 |
140 | > **Application**
141 |
142 | | Use Case | Industry | Description | Notebook |
143 | | :------------------------------------------------------------------------------------------------- | :--------------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |
144 | | ? Automated Continual Learning from New Data | ? Machine Learning | Continuously learns from new data inputs for adaptive AI. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_stream.ipynb) |
145 | | ? 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. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nestedchat_optiguide) |
146 | | ? AutoAnny - A Discord bot built using AutoGen | ? Communication Tools | Showcases the development of a Discord bot using AutoGen for enhanced interaction. | [](https://github.com/microsoft/autogen/tree/main/samples/apps/auto-anny) |
147 |
148 | > **Tools**
149 |
150 | | Use Case | Industry | Description | Notebook |
151 | | :--------------------------------------------------------------------- | :----------------------------- | :------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
152 | | ? Web Search: Solve Tasks Requiring Web Info | ? Information Retrieval | Searches the web to gather information required for completing tasks. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_web_info.ipynb) |
153 | | ? Use Provided Tools as Functions | ?? Tool Integration | Demonstrates how to use pre-provided tools as callable functions in AutoGen. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_function_call_currency_calculator) |
154 | | ? Use Tools via Sync and Async Function Calling | ?? Tool Integration | Illustrates synchronous and asynchronous tool usage within AutoGen workflows. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_function_call_async) |
155 | | ? Task Solving with Langchain Provided Tools as Functions | ? Language Processing | Leverages Langchain tools for task-solving within AutoGen. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_langchain.ipynb) |
156 | | ? RAG: Group Chat with Retrieval Augmented Generation | ? Collaboration | Enables group chat with Retrieval Augmented Generation (RAG) to support information sharing. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_groupchat_RAG) |
157 | | ?? Function Inception: Update/Remove Functions During Conversations | ? Development Tools | Allows AutoGen agents to modify their functions dynamically during conversations. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_inception_function.ipynb) |
158 | | ? Agent Chat with Whisper | ?? Audio Processing | Demonstrates AI agent capabilities for transcription and translation using Whisper. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_video_transcript_translate_with_whisper) |
159 | | ? Constrained Responses via Guidance | ? Natural Language Processing | Shows how to use guidance to constrain responses generated by agents. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_guidance.ipynb) |
160 | | ? Browse the Web with Agents | ? Information Retrieval | Explains how to configure agents to browse and retrieve information from the web. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_surfer.ipynb) |
161 | | ? SQL: Natural Language Text to SQL Query Using Spider Benchmark | ? Database Management | Converts natural language inputs into SQL queries using the Spider benchmark. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_sql_spider.ipynb) |
162 | | ?? Web Scraping with Apify | ? Data Gathering | Illustrates web scraping techniques with Apify using AutoGen. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_webscraping_with_apify) |
163 | | ?? Web Crawling: Crawl Entire Domain with Spider API | ? Data Gathering | Explains how to crawl entire domains using the Spider API. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_webcrawling_with_spider) |
164 | | ? Write a Software App Task by Task with Specially Designed Functions | ? Software Development | Builds a software application step-by-step using designed functions. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_function_call_code_writing.ipynb) |
165 |
166 | > **Human Development**
167 |
168 | | Use Case | Industry | Description | Notebook |
169 | | :--------------------------------------------------------------- | :---------------------- | :------------------------------------------------------------------------------------------------ | :------------------------------------------------------------------------------------------------------------------------------------------------------------ |
170 | | ? Simple Example in ChatGPT Style | ? Conversational AI | Demonstrates a simple conversational example in the style of ChatGPT. | [](https://github.com/microsoft/autogen/blob/0.2/samples/simple_chat.py) |
171 | | ? Auto Code Generation, Execution, Debugging and Human Feedback | ? Software Development | Showcases code generation, execution, debugging with human feedback integrated into the workflow. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_human_feedback.ipynb) |
172 | | ? Automated Task Solving with GPT-4 + Multiple Human Users | ? Collaboration | Enables task solving with multiple human users collaborating with GPT-4. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_two_users.ipynb) |
173 | | ? Agent Chat with Async Human Inputs | ? Conversational AI | Supports asynchronous human input during agent conversations. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/Async_human_input.ipynb) |
174 |
175 | > **Agent Teaching and Learning**
176 |
177 | | Use Case | Industry | Description | Notebook |
178 | | :------------------------------------------------------------------- | :-------------------------- | :--------------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
179 | | ? Teach Agents New Skills & Reuse via Automated Chat | ? Education & Training | Demonstrates teaching new skills to agents and enabling their reuse in automated chats. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_teaching) |
180 | | ? 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. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_teachability) |
181 | | ? Teach OpenAI Assistants Through GPTAssistantAgent | ? AI Assistant Development | Illustrates how to enhance OpenAI assistants' capabilities using GPTAssistantAgent. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_teachable_oai_assistants.ipynb) |
182 | | ? Agent Optimizer: Train Agents in an Agentic Way | ?? Optimization | Explains how to train agents effectively in an agentic manner using the Agent Optimizer. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_agentoptimizer.ipynb) |
183 |
184 | > **Multi-Agent Chat with OpenAI Assistants in the loop**
185 |
186 | | Use Case | Industry | Description | Notebook |
187 | | :-------------------------------------------------------- | :----------------------- | :---------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
188 | | ? Hello-World Chat with OpenAI Assistant in AutoGen | ? Conversational AI | A basic example of chatting with OpenAI Assistant using AutoGen. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_oai_assistant_twoagents_basic.ipynb) |
189 | | ? Chat with OpenAI Assistant using Function Call | ? Development Tools | Illustrates how to use function calls with OpenAI Assistant in chats. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_oai_assistant_function_call.ipynb) |
190 | | ? Chat with OpenAI Assistant with Code Interpreter | ? Software Development | Demonstrates the use of OpenAI Assistant as a code interpreter in chats. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_oai_code_interpreter.ipynb) |
191 | | ? Chat with OpenAI Assistant with Retrieval Augmentation | ? Information Retrieval | Enables retrieval-augmented conversations with OpenAI Assistant. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_oai_assistant_retrieval.ipynb) |
192 | | ? OpenAI Assistant in a Group Chat | ? Collaboration | Shows how OpenAI Assistant can collaborate with other agents in a group chat. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_oai_assistant_groupchat.ipynb) |
193 | | ?? GPTAssistantAgent based Multi-Agent Tool Use | ? Development Tools | Explains how to use GPTAssistantAgent for multi-agent tool usage. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/gpt_assistant_agent_function_call.ipynb) |
194 |
195 | > **Non-OpenAI Models**
196 |
197 | | Use Case | Industry | Description | Notebook |
198 | | :------------------------------------------------ | :-------- | :---------------------------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
199 | | ?? Conversational Chess using Non-OpenAI Models | ? Gaming | Explores conversational chess implemented with non-OpenAI models. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_nested_chats_chess_altmodels) |
200 |
201 | > **Multimodal Agent**
202 |
203 | | Use Case | Industry | Description | Notebook |
204 | | :--------------------------------------------- | :------------------ | :-------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------- |
205 | | ? Multimodal Agent Chat with DALLE and GPT-4V | ?? Multimedia AI | Combines DALLE and GPT-4V for multimodal agent communication. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_dalle_and_gpt4v.ipynb) |
206 | | ?? Multimodal Agent Chat with Llava | ? Image Processing | Uses Llava for enabling multimodal agent conversations with image processing. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_lmm_llava.ipynb) |
207 | | ?? Multimodal Agent Chat with GPT-4V | ?? Multimedia AI | Leverages GPT-4V for visual and conversational interactions in multimodal agents. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_lmm_gpt-4v.ipynb) |
208 |
209 | > **Long Context Handling**
210 |
211 | | Use Case | Industry | Description | Notebook |
212 | | :--------------------------------------- | :--------------- | :--------------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------- |
213 | | ? Long Context Handling as A Capability | ? AI Capability | Demonstrates techniques for handling long context effectively within AI workflows. | [](https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_transform_messages) |
214 |
215 | > **Evaluation and Assessment**
216 |
217 | | Use Case | Industry | Description | Notebook |
218 | | :----------------------------------------------------------------------------------- | :------------------------ | :------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------- |
219 | | ? 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. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agenteval_cq_math.ipynb) |
220 |
221 | > **Automatic Agent Building**
222 |
223 | | Use Case | Industry | Description | Notebook |
224 | | :------------------------------------------------------------ | :---------------- | :------------------------------------------------------------------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------- |
225 | | ?? Automatically Build Multi-agent System with AgentBuilder | ? AI Development | Explains how to automatically build multi-agent systems using the AgentBuilder tool. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/autobuild_basic.ipynb) |
226 | | ? Automatically Build Multi-agent System from Agent Library | ? AI Development | Shows how to construct multi-agent systems by leveraging a pre-defined agent library. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/autobuild_agent_library.ipynb) |
227 |
228 | > **Observability**
229 |
230 | | Use Case | Industry | Description | Notebook |
231 | | :---------------------------------------------------------------- | :------------------------ | :----------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------ |
232 | | ? Track LLM Calls, Tool Usage, Actions and Errors using AgentOps | ? Monitoring & Analytics | Demonstrates how to monitor LLM interactions, tool usage, and errors using AgentOps. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_agentops.ipynb) |
233 |
234 | > **Enhanced Inferences**
235 |
236 | | Use Case | Industry | Description | Notebook |
237 | | :--------------------------------------------------------------------- | :----------------- | :----------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
238 | | ? API Unification | ? API Management | Explains how to unify API usage with documentation and code examples. | [](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference/#api-unification) |
239 | | ?? Utility Functions to Help Managing API Configurations Effectively | ? API Management | Demonstrates utility functions to manage API configurations more effectively. | [](https://microsoft.github.io/autogen/0.2/docs/topics/llm_configuration) |
240 | | ? Cost Calculation | ? Cost Management | Introduces methods for tracking token usage and estimating costs for LLM interactions. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_cost_token_tracking.ipynb) |
241 | | ? Optimize for Code Generation | ? Optimization | Highlights cost-effective optimization techniques for improving code generation with LLMs. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/oai_completion.ipynb) |
242 | | ? Optimize for Math | ? Optimization | Explains techniques to optimize LLM performance for solving mathematical problems. | [](https://github.com/microsoft/autogen/blob/0.2/notebook/oai_chatgpt_gpt4.ipynb) |
243 |
244 | ### **Framework Name**: **Agno**
245 |
246 | > **UseCase**
247 |
248 | | Use Case | Industry | Description | Notebook |
249 | | :--------------------------------- | :----------------------------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
250 | | ? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/agno_support_agent.py) |
251 | | ? YouTube Agent | ? Media & Content | An intelligent agent that analyzes YouTube videos by generating detailed summaries, timestamps, themes, and content breakdowns using AI tools. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/youtube_agent.py) |
252 | | ? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/thinking_finance_agent.py) |
253 | | ? Study Partner | ? Education | Assists users in learning by finding resources, answering questions, and creating study plans. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/study_partner.py) |
254 | | ?? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/shopping_partner.py) |
255 | | ? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/research_agent_exa.py) |
256 | | ? Research Agent | ?? Media & Journalism | A research agent that combines web search and professional journalistic writing. It performs deep investigations and produces NYT-style reports. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/research_agent.py) |
257 | | ? Recipe Creator | ?? Food & Culinary | An AI-powered recipe recommendation agent that provides personalized recipes based on ingredients, preferences, and time constraints. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/recipe_creator.py) |
258 | | ?? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/finance_agent.py) |
259 | | ? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/reasoning_finance_agent.py) |
260 | | ? Readme Generator Agent | ? Software Dev | Agent generates high-quality READMEs for GitHub repositories using repo metadata. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/readme_generator.py) |
261 | | ? Movie Recommendation Agent | ? Entertainment | An intelligent agent that gives personalized movie recommendations using Exa and GPT-4o, analyzing genres, themes, and latest ratings. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/movie_recommedation.py) |
262 | | ? Media Trend Analysis Agent | ? Media & News | Analyzes emerging trends, patterns, and influencers from digital platforms using AI-powered agents and scraping. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/media_trend_analysis_agent.py) |
263 | | ?? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/legal_consultant.py) |
264 | | ? 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. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/deep_knowledge.py) |
265 | | ? Book Recommendation Agent | ? Publishing & Media | An intelligent agent that provides personalized book suggestions using literary data, reader preferences, reviews, and release info. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/book_recommendation.py) |
266 | | ? MCP Airbnb Agent | ?? Hospitality | Create an AI Agent using MCP and Llama 4 to search Airbnb listings with filters like workspace & transport proximity. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/airbnb_mcp.py) |
267 | | ? Assist Agent | ? AI Framework | An AI agent using GPT-4o to answer questions about the Agno framework with hybrid search and embedded knowledge. | [](https://github.com/agno-agi/agno/blob/main/cookbook/examples/agents/agno_assist.py) |
268 |
269 | ### **Framework Name**: **Langgraph**
270 |
271 | > **UseCase**
272 |
273 | | Use Case | Industry | Description | Notebook |
274 | | :------------------------------------ | :---------------------------- | :----------------------------------------------------------- | :----------------------------------------------------------- |
275 | | ? Chatbot Simulation Evaluation | ? ? AI / Quality Assurance | Simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbot-simulation-evaluation/agent-simulation-evaluation.ipynb) |
276 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/chatbots/information-gather-prompting.ipynb) |
277 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/code_assistant/langgraph_code_assistant.ipynb) |
278 | | ??? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb) |
279 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/extraction/retries.ipynb) |
280 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/agent_supervisor.ipynb) |
281 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/hierarchical_agent_teams.ipynb) |
282 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/multi_agent/multi-agent-collaboration.ipynb) |
283 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/plan-and-execute/plan-and-execute.ipynb) |
284 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/sql-agent.ipynb) |
285 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflection/reflection.ipynb)|
286 | | ? 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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/reflexion/reflexion.ipynb)|
287 | | **LangGraph Agentic RAG** | | | |
288 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_adaptive_rag.ipynb) |
289 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_adaptive_rag_local.ipynb) |
290 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_agentic_rag.ipynb) |
291 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_agentic_rag_local.ipynb) |
292 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_crag.ipynb) |
293 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_crag_local.ipynb) |
294 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_self_rag.ipynb) |
295 | | ? **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. | [](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/rag/langgraph_self_rag_local.ipynb) |
296 |
297 |
298 |
299 |
300 |
301 | ---
302 |
303 | ## ? Contributing
304 |
305 | Contributions are welcome! ? Here's how you can help:
306 |
307 | 1. Fork the repository.
308 | 2. Add a new use case or improve an existing one.
309 | 3. Submit a pull request with your changes.
310 |
311 | Please follow our [Contributing Guidelines](CONTRIBUTING.md) for more details.
312 |
313 | ---
314 |
315 | ## ? License
316 |
317 | This repository is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
318 |
319 | ---
320 |
321 | ## ? Let's Build Together!
322 |
323 | Feel free to share this repository with your network and star ? it if you find it useful. Let’s collaborate to create the ultimate resource for AI agent use cases!
324 |
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