AgentOps

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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.

Band Decision Desk

Band Decision Desk shows what enterprise multi-agent coordination looks like when a wrong answer is expensive. Three agents run as independent Band participants and collaborate over the Band channel, not inside a single prompt. Regime reads live market state from public Kraken OHLC data (EMA trend, ATR volatility, a 0–100 quick-score). Strategy turns that read into a concrete proposal — buy, sell or hold, with a position size. Risk is an independent auditor: it trust-checks the data source and its freshness, then applies hard risk gates. If a proposal breaks a gate (position over cap, or high volatility with a non-conservative profile) Risk does not merely warn — it issues a VETO with a reason. Strategy receives the veto, resizes the position, and re-submits, until Risk returns APPROVED with a SHA-256 audit hash anchoring the exact inputs and the decision. This is real agent-to-agent coordination: dynamic recruitment (Strategy adds Risk to the room), a non-linear veto loop, escalation when a source is untrusted, and a tamper-evident decision trail. The same pattern maps directly onto regulated enterprise workflows — credit approvals, trade compliance, insurance underwriting — where one agent must be able to overrule another and every decision must be explainable after the fact. Built with the Band SDK (SimpleAdapter), Python and live public market data. The repository includes a one-command self-test that demonstrates approve, veto-and-resize, and escalate end-to-end.

OpenMind Nexus: Cognitive Risk Intelligence

OpenMind Nexus is a multi-agent cognitive risk investigation platform that helps organizations identify, analyze, and respond to information integrity incidents. Modern organizations face increasing risks from disinformation, manipulated narratives, synthetic media, and persuasive content designed to influence decision-making. Investigating these threats is often manual, fragmented, and difficult to scale. OpenMind Nexus transforms this process into a collaborative workflow powered by Band. When suspicious content is submitted, specialized AI agents collaborate through a shared investigation room: • Content Intake Agent – Structures content and creates investigation artifacts. • Cognitive Bias Agent – Detects manipulation patterns, cognitive biases, and persuasion techniques. • Verification Agent – Evaluates credibility, unsupported claims, and evidence quality. • Echo Chamber Agent – Measures amplification and polarization risks. • Explainability Agent – Combines findings into a transparent reasoning trace and recommendation. Instead of relying on a single AI response, agents contribute independent evidence into a shared context. Recommendations emerge from multiple evidence streams. The platform supports three outcomes: • ARCHIVE – Low-risk content. • MONITOR – Ambiguous content requiring observation. • ESCALATE – High-risk incidents requiring Trust & Safety, Compliance, or Security review. Band serves as the collaboration layer, enabling agents to exchange structured context, maintain investigation state, and participate in auditable workflows. By separating reasoning from transport, OpenMind Nexus creates a scalable architecture for enterprise investigations. By combining bias detection, credibility analysis, polarization assessment, explainability, and human oversight, OpenMind Nexus demonstrates how organizations can move beyond single-agent AI systems and adopt transparent, collaborative AI investigations.

🦷 Dental Surgical Simulator

The simulator presents a stylized, professional interface featuring a 3D viewport of a human dental arch. Users select from various surgical missions, each requiring a specific sequence of surgical steps. Success depends on selecting the correct surgical instrument and interacting with the precise target tooth at the right time. Procedural Anatomy: Anatomically accurate tooth crowns and root clusters generated via math-based geometry. Multi-Mode Visualization: X-Ray Mode: Toggles transparency of soft tissues (gums, palate) to reveal bone and roots. Wireframe Mode: Provides structural visualization of dental anatomy. View Switching: Toggle between Full Arch, Upper Jaw, and Lower Jaw views. Dynamic Theming: Seamless transition between a professional high-contrast Dark Mode and a clean Light Mode. Interactive HUD: Real-time feedback through toasts, tooltips, and a crosshair for precise targeting. Advanced Missions: Includes complex procedures like Dental Implants, Root Canals, and Bone Grafting.

RevenueOS

RevenueOS — AI-Native GTM Workspace a RevenueOS is an AI outbound platform built on one belief: companies don't have a lead problem, they have an attention problem. Every prospect is already broadcasting buying intent on the open web — hiring sprees, funding rounds, product launches, pricing changes, new executives — but revenue teams can't read the whole internet, so the best opportunities go ignored. RevenueOS listens, prioritizes, and acts end to end. A rep enters a company (or describes an ICP in plain English), and the platform researches it live, detects and scores buying signals, ranks accounts by fit, intent, timing, and risk, writes hyper-personalized outreach, runs multi-channel sequences, places browser-based calls with a real-time copilot, and produces coaching scorecards — answering "who do I contact today, why now, and what do I say?" Every sponsor is load-bearing. Bright Data is the foundation — a real-time web-intelligence layer powering all prospecting, account research, enrichment, and signal discovery. Cognee is long-term memory: a knowledge graph storing every company, contact, signal, call, and email so agents reason over history instead of starting cold. Trigger.dev orchestrates durable account-monitoring, sequence automation, and follow-up workflows. LiveKit powers real-time browser calling and the AI SDR, while Speechmatics transcribes live calls for the copilot, objection detection, summaries, and coaching. Together they form a complete GTM Intelligence stack — Bright Data finds opportunities, Cognee remembers them, Trigger.dev acts, LiveKit enables the conversation, and Speechmatics understands it — naturally extending into Finance & Market Intelligence through hiring, funding, and growth signals.

Markster Recon: Be the First Call, Not the Fifth

A rep opens the CRM Monday morning. A target account: "no recent activity." They move on. Meanwhile that same account just posted 40 sales roles, closed a round, and quietly repriced - on the open web, where the CRM never looks. That gap is where pipeline dies. (76% of companies say fewer than half their CRM records are accurate - Validity, 2025.) Markster Recon closes it. Point it at any company and it runs a real pipeline, not a prompt: COLLECT - six Bright Data products fire in parallel: LinkedIn hiring (Web Scraper API), news + funding (Web Unlocker), competitor landscape (Discover API), market results (SERP API), JS-rendered pricing (Browser API), and funding research (Deep Lookup). SOURCE - every datum carries provenance: source URL, timestamp, method. Click any claim and verify it live. Nothing is unattributed. SCORE - confidence is computed (coverage x signal strength), not guessed by a model. SYNTHESIZE - the LLM writes the Account Action Plan: the read, routes in, who shapes the decision, honest evidence gaps, next actions. It writes narrative only - it can never invent a signal. Then the part most projects skip: Recon acts. It writes the decision into a live HubSpot - gtm_* properties, a sourced note on the timeline, and an urgent task where an AI agent executes or prioritizes for the rep. And it polices itself: a thin or low-confidence run is gated to "review only," so a weak signal can never look like an approved action. It is a standing watch on your target list, not a one-time lookup: run the loop on a schedule and you catch the window the day it opens. Judge-testable, no login: any company returns a full plan plus a preview of exactly what hits the CRM. It runs on a real production CRM, and synthesis is provider-portable - Azure OpenAI, AI/ML API, or open-source via Featherless. Built by a team that runs GTM on this exact stack. Live web -> sourced signal -> CRM action. That's the loop.

ReIn Agent

ReIn Agent is an AI-powered voice research assistant that converts spoken or text queries into structured research reports by combining speech recognition, web intelligence, and LLM-based summarization. It enables users to perform voice-guided research, analyze sources, generate insights, and manage research history through an interactive dashboard powered by Speechmatics, Bright Data, and AI models. ReIn Agent is an AI-powered research assistant that transforms voice or text queries into structured insights using speech recognition, web intelligence, and LLM-based summarization. It helps users research topics, analyze information, generate reports, and track research history through an interactive dashboard.