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
AuthorAgentOps, Inc.
Release Date2023
Websitehttps://www.agentops.ai/
Documentationhttps://docs.agentops.ai/v1/introduction
Technology TypeMonitoring 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

AgentOps AI technology page Hackathon projects

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

APEX Trader Autonomous Multi-Agent Trading System

APEX Trader Autonomous Multi-Agent Trading System

APEX Trader is a production-grade autonomous AI trading system built on a multi-agent architecture where five specialized agents work in a coordinated pipeline to analyze, validate, and execute cryptocurrency trades autonomously. The system consists of five agents: the Fundamental Agent analyzes NVT ratios, exchange net flows, and fair value models; the Technical Agent processes EMA crossovers, RSI, MACD, Bollinger Bands, and volume confirmation signals; the Sentiment Agent evaluates Fear & Greed Index and social sentiment scores; the Risk Agent enforces position sizing rules, portfolio heat limits, and R:R ratio thresholds; and the Backtester Agent validates every signal against historical win rates and Sharpe ratios before approval. Each trade requires multi-agent consensus above a configurable confidence threshold (72% day trading / 78% swing trading) before execution. Both day trading (5m–15m timeframes) and swing trading (4h–1D timeframes) with dynamically adjusted parameters — risk per trade, stop-loss placement, take-profit scaling, and trailing stops — all tuned to expert-level specifications. The APEX self-learning mechanism (evaluate-agent.py) continuously trains on closed trade P&L data, adjusting confidence thresholds autonomously. A self-healing daemon runs 24/7 with automatic error recovery and cooldown logic. The real-time dashboard (built on React/Next.js at port 3201 with a FastAPI backend at port 3202) provides a fully redesigned Agent Analysis Log where every stakeholder — trader, risk manager, operator, executive — gets layered information: trade identity, agent pipeline status, per-agent reasoning, strategy prediction with entry/SL/TP targets, course of action, and contextual RSS news feed — all grouped by trade, pair, or date. The project demonstrates how agentic AI systems can move beyond single-model decision making into coordinated multi-agent architectures that are transparent, auditable, and continuously self-improving.

ForgeClaw Ă— Kraken Autonomous Crypto Trading Agent

ForgeClaw Ă— Kraken Autonomous Crypto Trading Agent

orgeClaw × Kraken is an autonomous crypto trading agent built on a production-grade stack that goes far beyond a simple trading bot. The agent pipeline executes 7 sequential Temporal activities: connecting to the Kraken CLI MCP server for market data, fetching AI signals from PrismaAPI, computing RSI(14) and VWAP deviation analysis with volume confirmation, gating each trade behind an ERC-8004 USDC micropayment for trustless execution, executing paper trades with 10% position limits and 2% stop loss enforcement, tracking realized PnL with a FINRA-style SQLite audit log, and delivering formatted trade summaries to Slack. ForgeClaw acts as the design-time layer — a BPMN agent composer (forgeclaw-app.vercel.app) that generates Temporal workflows from visual pipelines. VerifyClaw scans every agent skill against 25 SAFE-MCP threat patterns before deployment, with a max risk score of 3/100 on this agent. Redpanda streams all trade events across 5 Kafka-compatible topics in real time. The dashboard is a pixel-accurate Kraken Pro replica with live signal feed, agent workflow panel, executor, trade history, PnL analytics, open orders, Slack log, and ERC-8004 payment ledger — all backed by a FastAPI service proxied through nginx and pulling from SQLite on every cycle. Clicking Run Agent fires a real Temporal workflow end-to-end, not a simulation. Infrastructure: Temporal + PostgreSQL for durable orchestration, Redpanda for event streaming, nginx reverse proxy, Docker Compose for one-command deployment.

Vertex Sentinel

Vertex Sentinel

The Problem: AI trading agents today operate as "black boxes" requiring full private key access. One hallucination, one compromise, and funds are gone. Current safety tools are advisory-only—they warn but don't stop bad trades. The Solution: Vertex Sentinel introduces a production-grade, 3-layer security architecture that makes unauthorized trades mathematically impossible: Intent Layer: Agents construct TradeIntents (pair, volume, maxPrice, deadline) and sign them using EIP-712 typed data signing—completely off-chain. No private key delegation is ever required. Sentinel Layer: The RiskRouter.sol smart contract intercepts every intent and enforces: signature verification via ECDSA.recover(), agent authorization via ERC-8004 identity registry, deadline validation, and circuit breakers preventing volume limit violations. Execution Layer: Only trades with TradeAuthorized events reach the exchange. Any failure triggers CriticalSecurityException—system halts, funds protected. Live Proof: We executed 4 real BTC/USD trades on Kraken with 100% success rate. Every trade cryptographically signed. Every decision auditable. Full P&L tracking demonstrated. Key Technical Achievements: - Deployed RiskRouter on Sepolia: 0xd6A6952545FF6E6E6681c2d15C59f9EB8F40FdBC - ERC-8004 compliant AgentRegistry with on-chain reputation scoring - Model Context Protocol (MCP) integration with Kraken CLI - Immutable audit trail in logs/audit.json with reasoning and signatures - Open-source SDK for rapid AI agent integration The Vertex Gap: Unlike centralized "trust the company" solutions (ARMA, Mamo, ZyFAI), Vertex Sentinel delivers "trust the contract"—verifiable, transparent, and immutable security enforced by code. We're building the trust infrastructure for the agentic economy. Risk management first. Automation second.