AgentOps

Top Builders

Explore the top contributors showcasing the highest number of 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.

4RealEstate Flow

Real estate agents are losing listings to better marketers, not better agents. 73% of homeowners now say they're more likely to list with an agent who uses video marketing. But here is the thing, most agents face two bad options: pay €3,000+ a month for a content agency, or use generic AI tools that produce videos their audience scrolls past. Neither works. 4RealEstate Flow is the end-to-end AI content engine built for real estate agents. Four specialized AI agents run the entire content operation. The Content Planner continuously analyzes viral content across social media to build a weekly content calendar tailored to each agent's market. The Video Agent handles creative execution end-to-end script, edit, motion design, captions. The Listing Video Maker turns property photos and details into cinematic listing videos in minutes. And the Ads Manager dynamically allocates paid spend based on which content is actually converting. From the first idea to the last click. Autonomously. Every real estate agent, their own marketing team.

AgentPayOps Finance controls for autonomous agents

Autonomous agents will buy data, tools, compute, and services. Companies need a control plane that decides when those agents are allowed to spend money, blocks risky or duplicate purchases, escalates high-value actions, and records every decision for finance teams. AgentPayOps demonstrates that control layer through one vertical workflow: An invoice agent reviews a vendor invoice. The agent needs a paid vendor-risk report. The report endpoint returns 402 Payment Required. AgentPayOps evaluates vendor, category, amount, approval threshold, and duplicate-purchase rules. The system approves, blocks, or escalates the payment. Transactions and reasoning are shown in an audit dashboard.

ATLAS - Enterprise Multi-Agent Governance

ATLAS is an enterprise multi-agent system where every agentic decision is inspected, signed, and auditable. THE PROBLEM Goldman Sachs CIO said publicly: "We don't know what controls we need for agentic AI." Enterprise LLM agents make decisions affecting databases, APIs, financial records. There is no infrastructure that makes these decisions inspectable, auditable, and compliant. WHAT ATLAS DELIVERS - 29/29 scientific test suite PASS in under 1 second - All 5 sponsors integrated end-to-end (real API calls, not mocked): · Speechmatics for voice transcription · Featherless for open-source model routing (MiniMax-M2.5, DeepSeek-V3.2, Kimi-K2.5, Llama-3.3-70B) · Google Gemini 2.0 Flash for orchestration and synthesis · Vultr for infrastructure layer · Kraken for financial action layer - SOUF AI DPI inline governance: every prompt inspected in 0.079ms avg (well under 1ms ceiling) - Ed25519-signed audit chain with SHA-256 Merkle tamper-evidence - 8 signed records per full pipeline request, chain verified - Isaac Adams (Featherless judge): "confidence is what enterprise AI needs" — ATLAS is that confidence layer ARCHITECTURE 6-layer governed pipeline: Voice → Speechmatics → SOUF AI DPI gate → Gemini orchestrator → Featherless router → Tool executor (Search/Database/Kraken/Vultr) → Ed25519 audit trail → Gemini synthesis. REPRODUCIBILITY git clone https://github.com/SRKRZ23/atlas cd atlas && pip install -r requirements.txt python3 src/test_atlas.py → 29/29 PASS in under 1 second ECOSYSTEM ATLAS is the routing layer of a 4-product AI safety ecosystem: SOUF AI provides DPI, FORGE generates policies, CITADEL evaluates models, ATLAS calls them all. Same Ed25519 audit chain across four products. MIT licensed. Lobster Trap is the floor. ATLAS is the agent governance ceiling. Built solo by Sardor Razikov, Tashkent.

Dasalt 360 Enterprise AI

Dasalt 360 Ltd is a sophisticated, multimodal multi-agent enterprise system engineered specifically to navigate the intricate complexities of the West African IT hardware market. Operating from its headquarters in Jimeta, Adamawa State, Nigeria, the system serves as an autonomous "System of Record" that bridges the strategic gap between global hardware procurement in the United States, national wholesale hubs in Lagos, and local retail distribution in Yola North. The architecture is meticulously built upon Vultr’s Dedicated Cloud Compute infrastructure, ensuring secure, low-latency orchestration of business logic. The "Intelligence Layer" is powered by Vultr Serverless Inference, utilizing Llama 3.1 70B for high-precision financial reasoning and Llama 3.2 Vision for the optical identification of hardware assets. A primary innovation of this project is its ability to handle the extreme currency volatility of the Nigerian Naira (₦). The AI acts as an autonomous Chief Financial Officer (CFO), performing real-time landing cost calculations based on a fixed 1,400 NGN/USD exchange rate, while simultaneously integrating localized logistics overheads—specifically the 7,000 Naira per-unit secure transit fee from Lagos to Yola. Beyond text-based automation, the system demonstrates the "Future of Work" through a multimodal interface. It incorporates a bespoke Voice-to-Voice engine and Vision-to-Text capabilities, allowing CEO Christopher Krim and his team to manage inventory hands-free in the warehouse or via photographic evidence of arrivals. To maintain the highest corporate standards, the system is instructed to curate every response in elegant, sophisticated natural language, providing polished executive briefs that eliminate technical jargon. By centralizing planning, coordination, and execution on Vultr, Dasalt 360 Ltd exemplifies a new era of autonomous enterprise operations, ensuring business sustainability and profitability in a challenging economic environment.

MusKent Commerce OS

MusKent is a production-ready autonomous AI system designed to support real commerce operations across revenue, sales, automation, fulfillment, billing, and marketplace workflows. It moves beyond traditional copilots by combining reasoning agents, async execution, tool orchestration, and multimodal input into a unified operating system. At its core, MusKent uses agent-driven decision flows. It continuously evaluates business signals such as revenue performance, marketplace activity, sales trends, and operational state, then determines the next best action using AI. These agents operate within structured workflows, interact with internal tools and external APIs, and execute multi-step tasks while safely degrading to fallback systems when needed. The platform integrates multiple intelligence layers, including AI-powered reasoning for decision-making, specialized compute for ranking and scoring, and voice-based interaction through real-time and batch transcription. This enables a collaborative multi-agent system where different models and providers handle specific tasks like reasoning, analysis, and fallback execution. MusKent is designed for reliability and real-world usage. It supports asynchronous job processing for long-running operations, structured outputs for consistency, provider health awareness, and safe fallback mechanisms to maintain performance even under degraded conditions. From a systems perspective, MusKent delivers intelligent reasoning, agentic workflows, enterprise utility, and multimodal interaction in a single platform. The result is an AI-powered commerce operating system that can analyze, plan, and act across business operations while remaining resilient in production environments.