AgentOps AI technology page Top Builders

Explore the top contributors showcasing the highest number of AgentOps AI technology page app submissions within our community.

AgentOps

AgentOps is a comprehensive platform designed for monitoring, debugging, and optimizing AI agents in both development and production environments. It provides advanced tools such as session replays, metrics dashboards, and custom reporting, enabling developers to track the performance, cost, and interactions of their AI agents in real-time.

Some of the out-of-the-box integrations include:

CrewAI,
Autogen,
Langchain,
Cohere,
LiteLLM,
MultiOn.

This wide compatibility ensures seamless integration with a diverse range of AI systems and development environments.

General
Author	AgentOps, Inc.
Release Date	2023
Website	https://www.agentops.ai/
Documentation	https://docs.agentops.ai/v1/introduction
Technology Type	Monitoring Tool

Key Features

LLM Cost Management: Track and manage the costs associated with large language models (LLMs).
Session Replays: Replay agent sessions to analyze interactions and identify issues.
Custom Reporting: Generate tailored reports to meet specific analytical needs.
Recursive Thought Detection: Monitor recursive thinking patterns in agents to ensure optimal performance.
Time Travel Debugging: Debug and audit agent behaviors at any point in their operational timeline.
Compliance and Security: Built-in features to ensure that agents operate within security and compliance standards.

Start Building with AgentOps

AgentOps offers developers powerful tools to enhance the monitoring and management of AI agents. With easy integration through SDKs, it provides real-time insights into the performance and behavior of agents. Developers are encouraged to explore community-built use cases and applications to unlock the full potential of AgentOps.

👉 Start building with AgentOps

👉 Examples

Edit on Github

AgentOps AI technology page Hackathon projects

Discover innovative solutions crafted with AgentOps AI technology page, developed by our community members during our engaging hackathons.

CrewNews

Biases in news reporting can distort public perception, leading to a skewed understanding of important issues and events. When media outlets present information from a single perspective, they risk creating echo chambers where only certain viewpoints are amplified while others are silenced. This not only undermines the integrity of journalism but also affects how individuals form opinions and make decisions based on incomplete information. CrewNews addresses this critical issue by actively sourcing and presenting content from a diverse range of media providers from the United States, ensuring that multiple perspectives are represented in each report. By utilizing advanced AI technologies, CrewNews fosters a more balanced discourse, empowering users to hear all sides of the story for a given question or topic and come closer to the truth.

Luna AI - The Ultimate Personal Assistant

The Desktop Task Manager and Communication Assistant is an innovative application that revolutionizes how users interact with their digital workspace. By harnessing the power of LLaMA 3.1, a cutting-edge language model, and integrating it with crew.ai's sophisticated AI agents, this tool creates a seamless bridge between voice commands and complex task execution. At its core, the application offers an intuitive voice interface, allowing users to manage their daily tasks, schedule meetings, compose emails, and manipulate documents across various Google services with simple spoken instructions. The AI interprets these natural language inputs, extracting relevant information to trigger the appropriate task agents. What sets this assistant apart is its multi-agent architecture. Each Google service (Meet, Gmail, Docs, Calendar, Drive, and Sheets) has a dedicated AI agent, enabling specialized handling of service-specific tasks. This modular approach ensures efficient and accurate execution of user requests, whether it's creating a new spreadsheet, scheduling a video call, or organizing files in Google Drive. The LLaMA 3.1 model serves as the central intelligence, processing user prompts and coordinating between agents. It can understand context, infer user intent, and even automate complex multi-step processes. For instance, a single voice command could trigger the creation of a meeting invite, generate an agenda document, and send email notifications to participants. By automating routine tasks and providing a natural, conversational interface to powerful productivity tools, this Desktop Task Manager and Communication Assistant significantly enhances workflow efficiency. It's particularly valuable for professionals who juggle multiple projects, frequent meetings, and extensive digital communication, offering a smarter way to manage their digital workload.

HouseDNA for Fast Move

It is always hard to find the next home, especially when you move to a new city that you are unfamiliar with. Our project will use Autogen to help you find the right house/apt in a few sentences! And we also help you find logistics such as flight tickets/moving company to a new city! Our project leverages the Autogen and AgentOps framework to streamline the home-finding and moving process by providing users with tailored options for housing, airline tickets, and moving companies in just a few sentences. Designed to assist those relocating, especially low-income individuals or families, the platform simplifies complex logistics. It monetizes through Cost-Per-Click for listings and Cost-Per-Acquisition for completed bookings, while offering advertising opportunities. By alleviating the burden of searching for affordable, safe housing, the system helps users focus on packing and settling in.

HardwareAgents

Creating any hardware project requires a lot of time to source parts and even more time to make them work together. In the future, everyone will be an engineer, as long as they have ideas they want to bring to life. The market for creating new engineering tools isn’t just for engineers—it’s for all builders, it’s for everyone. AI agents can help close the gap between ideas and reality. Every engineer will gain more agency. The future of engineering tools isn’t about targeting only engineers, but anyone capable of generating ideas. These tools will be simple to use, expanding the market. Deep tech is just at the beginning. What does the future look like? Every corporation will have agents acting as communication proxies for what they are building. We’ll need to create new CAD tools and redefine standards so they become AI-native. One application will contain everything—components, power analysis, aerodynamics, performance, signal analysis, and more. The next step for this project is to fine-tune Llama on a wide range of datasheets. I’ve started this, but finding high-quality data takes time. If you fine-tune a model on bad data, it’s often better just to use prompt engineering instead.

Medina AI

In Uzbekistan 85% of deaths are caused because of non communicable disease. Why? Because we have only 15% of hospitals that are equipped with MRIs. We like professionals and equipment, especially in the countryside, and this process takes a lot of time, especially for people who live in rural areas because they don't have access to facilities and professionals in city areas. And this process is excessive bureaucracy and we have faulty diagnosis because we rely on human factors. Introducing CoMed, an AI powered platform to generate, manage and report Brain MRI observations. We are building one ecosystem for both doctors and their patients. We are moving forward to make MRI diagnosis easier, faster, and cheaper. And last but not least, we are NOT replacing doctors, however building tool for doctors to help their patients.

Multi-Agent Epidemic Simulation Tool

Introduction In an era where infectious diseases can rapidly escalate into global pandemics, the ability to make swift, data-driven policy decisions is more critical than ever. We present a groundbreaking multi-agent epidemic simulation tool designed to empower policymakers with actionable insights, enabling them to navigate the complexities of epidemic management effectively. The Problem Policymakers face significant challenges during epidemics: Complex Dynamics: Epidemics involve intricate interactions among individuals, communities, and policies, making outcomes difficult to predict. Uncertain Outcomes: Without robust predictive tools, policies may lead to unintended consequences, exacerbating the crisis. Data Overload: The abundance of fragmented data sources complicates the decision-making process. These issues can result in delayed responses, increased transmission rates, and greater societal and economic disruption. Our Solution Our simulation tool addresses these challenges by: Multi-Agent Modeling: Simulating individual behaviors and interactions to capture the nuanced spread of diseases. Policy Impact Analysis: Allowing users to model and compare the effects of various intervention strategies, such as lockdowns, vaccination campaigns, and travel restrictions. Real-Time Insights: Providing immediate feedback on potential outcomes, helping to adjust policies proactively. User-Friendly Interface: Designed for policymakers without requiring technical expertise in modeling or epidemiology.