AI Agent 目录

analyses medical reports and provide health insights.

Diagnoses and monitors diseases using patient data.

Automates stock trading with real-time market analysis.

Provides personalized education tailored to users.

Handles customer queries around the clock.

Suggests products based on user preferences and history.

Optimizes routes and autonomously delivers packages.

Monitors production lines and ensures quality control.

Analyzes market trends to determine property prices.

Provides insights on crop health and yield predictions.

Predicts energy usage to optimize grid management.

Recommends personalized media based on preferences.

Automates document review and highlights key clauses.

Suggests best-fit candidates for job openings.

Plans travel itineraries based on preferences.

Enhances player experience with real-time assistance.

Identifies potential threats and mitigates attacks.

Helps customers find products they’ll love.

Plans efficient delivery routes and manages inventory.

Autonomous Multi-Agent Based Red Team Testing Service.

A medical ai agent that helps automating the process of hospitals / insurance claiming workflow.

A medical ai agent that helps automating the process of hospitals / insurance claiming workflow.

Automates email responses based on predefined criteria to enhance communication efficiency.

Assists in organizing and managing meetings, including scheduling and agenda preparation.

Facilitates self-assessment processes within an organization, aiding in performance reviews.

Evaluates and scores potential leads to prioritize outreach in sales strategies.

Develops marketing strategies by analyzing market trends and audience data.

Creates job postings by analyzing job requirements, aiding in recruitment processes.

Streamlines the recruitment process by automating various tasks involved in hiring.

Matches candidate profiles to suitable job positions to enhance recruitment efficiency.

Generates and schedules Instagram posts automatically, streamlining social media management.

Automates the creation of landing pages for websites, facilitating web development tasks.

Assists in the development of games by automating certain aspects of game creation.

Provides tools for analyzing stock market data to assist in financial decision-making.

Assists in planning trips by organizing itineraries and managing travel details.

Plans surprise trips by selecting destinations and activities based on user preferences.

Assists authors in writing books by providing structured workflows and writing assistance.

Aids in writing screenplays by offering templates and guidance for script development.

Validates Markdown files to ensure proper formatting and adherence to standards.

Manages and organizes knowledge related to Meta Quest, facilitating information retrieval.

Integrates NVIDIA AI models into workflows to enhance computational capabilities.

Assists in preparing for meetings by organizing materials and setting agendas.

Provides a starter template for new projects to streamline the setup process.

Demonstrates integration between CrewAI and LangGraph for enhanced workflow automation.

Demonstrates automated task-solving by generating, executing, and debugging code.

Generates code and answers questions using retrieval-augmented methods.

Utilizes Qdrant for enhanced retrieval-augmented agent performance.

Demonstrates group task-solving via multi-agent collaboration.

Uses multi-agent collaboration to create data visualizations.

Solves complex tasks collaboratively with a larger group of agents.

Combines coding and planning agents for solving tasks effectively.

Uses predefined transition paths in a graph for solving tasks.

Simulates inner-monologue for problem-solving using group chats.

Implements a custom function for speaker selection in group chats.

Automates sequential task-solving with a single initiating agent.

Handles asynchronous task-solving in a sequence of chats initiated by one agent.

Facilitates sequential task-solving with different agents initiating each chat.

Uses nested chats to solve hierarchical and complex problems.

Demonstrates sequential task-solving using nested chats.

Showcases how to solve supply chain optimization problems using nested chats, a coding agent, and a safeguard agent.

Explores the use of nested chats for playing conversational chess with integrated tools.

Continuously learns from new data inputs for adaptive AI.

Highlights a solution combining coding, tool use, and safeguarding for supply chain optimization.

Showcases the development of a Discord bot using AutoGen for enhanced interaction.

Searches the web to gather information required for completing tasks.

Demonstrates how to use pre-provided tools as callable functions in AutoGen.

Illustrates synchronous and asynchronous tool usage within AutoGen workflows.

Leverages Langchain tools for task-solving within AutoGen.

Enables group chat with Retrieval Augmented Generation (RAG) to support information sharing.

Allows AutoGen agents to modify their functions dynamically during conversations.

Demonstrates AI agent capabilities for transcription and translation using Whisper.

Shows how to use guidance to constrain responses generated by agents.

Explains how to configure agents to browse and retrieve information from the web.

Converts natural language inputs into SQL queries using the Spider benchmark.

Illustrates web scraping techniques with Apify using AutoGen.

Explains how to crawl entire domains using the Spider API.

Builds a software application step-by-step using designed functions.

Demonstrates a simple conversational example in the style of ChatGPT.

Showcases code generation, execution, debugging with human feedback integrated into the workflow.

Enables task solving with multiple human users collaborating with GPT-4.

Supports asynchronous human input during agent conversations.

Demonstrates teaching new skills to agents and enabling their reuse in automated chats.

Shows how to teach agents new facts, user preferences, and non-coding skills.

Illustrates how to enhance OpenAI assistants' capabilities using GPTAssistantAgent.

Explains how to train agents effectively in an agentic manner using the Agent Optimizer.

A basic example of chatting with OpenAI Assistant using AutoGen.

Illustrates how to use function calls with OpenAI Assistant in chats.

Demonstrates the use of OpenAI Assistant as a code interpreter in chats.

Enables retrieval-augmented conversations with OpenAI Assistant.

Shows how OpenAI Assistant can collaborate with other agents in a group chat.

Explains how to use GPTAssistantAgent for multi-agent tool usage.

Explores conversational chess implemented with non-OpenAI models.

Combines DALLE and GPT-4V for multimodal agent communication.

Uses Llava for enabling multimodal agent conversations with image processing.

Leverages GPT-4V for visual and conversational interactions in multimodal agents.

Demonstrates techniques for handling long context effectively within AI workflows.

Introduces AgentEval for evaluating and assessing the performance of LLM-based applications.

Explains how to automatically build multi-agent systems using the AgentBuilder tool.

Shows how to construct multi-agent systems by leveraging a pre-defined agent library.

Demonstrates how to monitor LLM interactions, tool usage, and errors using AgentOps.

Explains how to unify API usage with documentation and code examples.

Demonstrates utility functions to manage API configurations more effectively.

Introduces methods for tracking token usage and estimating costs for LLM interactions.

Highlights cost-effective optimization techniques for improving code generation with LLMs.

Explains techniques to optimize LLM performance for solving mathematical problems.

The Agno Support Agent helps developers with the Agno framework by providing real-time answers, explanations, and code examples.

An intelligent agent that analyzes YouTube videos by generating detailed summaries, timestamps, themes, and content breakdowns using AI tools.

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.

Assists users in learning by finding resources, answering questions, and creating study plans.

A product recommender agent that helps users find matching products based on preferences from trusted platforms like Amazon, Flipkart, etc.

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.

A research agent that combines web search and professional journalistic writing. It performs deep investigations and produces NYT-style reports.

An AI-powered recipe recommendation agent that provides personalized recipes based on ingredients, preferences, and time constraints.

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.

Uses a Claude-3.5 Sonnet-based agent to analyze stocks like NVDA using tools for reasoning and Yahoo Finance data.

Agent generates high-quality READMEs for GitHub repositories using repo metadata.

An intelligent agent that gives personalized movie recommendations using Exa and GPT-4o, analyzing genres, themes, and latest ratings.

Analyzes emerging trends, patterns, and influencers from digital platforms using AI-powered agents and scraping.

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.

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.

An intelligent agent that provides personalized book suggestions using literary data, reader preferences, reviews, and release info.

Create an AI Agent using MCP and Llama 4 to search Airbnb listings with filters like workspace & transport proximity.

An AI agent using GPT-4o to answer questions about the Agno framework with hybrid search and embedded knowledge.

Simulate user interactions to evaluate chatbot performance, ensuring robustness and reliability in real-world scenarios.

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.

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.

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.

This tutorial demonstrates how to implement retry mechanisms in LangGraph workflows, ensuring robust data extraction processes that can handle transient errors and improve reliability.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

This tutorial extends Agentic RAG to local environments, enabling the use of local models and data sources for retrieval and generation tasks.

Implement a Corrective RAG system that evaluates and refines retrieved documents before passing them to the generator, ensuring higher-quality outputs.

This tutorial focuses on building a Corrective RAG system using local resources, allowing for offline document evaluation and refinement processes.

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.

This tutorial demonstrates how to implement Self-RAG using local models and data sources, enabling offline reflection and retrieval processes.