Agentops — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for Agentops. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Jun 25, 2026 • Sentiment updated: Jun 26, 2026

GitHub Statistics

• GitHub Stars: 5,655
• Forks: 602
• Open Issues: 165
• Primary Language: Python
• License: MIT
• Weekly Star Velocity: +11 stars/week

Community Sentiment

Community Buzz: Honestly my goal is to learn how to teach an agent to build a maintainable product, so I'm way more interested in the learnings at the agentic level, as posted on HackerNews

Pros & Cons

What People Love

AI agents efficiency, Dev.to users praise AgentOps for its ease of use

Common Complaints

LLM security risks, difficulty in implementing AgentOps

Biggest Positive: AI agents efficiency

Biggest Negative: LLM security risks

Why Agentops Stands Out

AgentOps stands out from alternatives with its focus on observability and devtools for AI agents, providing features like replay analytics and LLM cost management. Its ability to integrate with multiple AI frameworks and LLM providers makes it a valuable tool for developers. By providing a self-hosting option, AgentOps also caters to users who require more control over their data and infrastructure. The project's emphasis on ease of use, as seen in its quick start guide, makes it an attractive choice for developers looking to get started with AI agent development.

Built With

Build an AI agent that tracks LLM costs and provides replay analytics — AgentOps integrates with most LLMs and agent frameworks to enable this, Build a self-hosted observability platform for AI agents — AgentOps provides a self-hosting option with a setup guide in app/README.md, Build a framework for evaluating and monitoring AI agent performance — AgentOps offers native integrations with popular AI frameworks like CrewAI and LangChain, Build a system for managing AI agent sessions and debugging — AgentOps provides features like session replays and step-by-step execution graphs, Build a platform for tracking spend with LLM foundation model providers — AgentOps includes LLM cost management capabilities

Getting Started

1. Install AgentOps using pip: pip install agentops
2. Get an API key from the AgentOps dashboard: https://app.agentops.ai/settings/projects
3. Initialize the AgentOps client in your Python code: agentops.init(<INSERT YOUR API KEY HERE>)
4. Configure your AgentOps setup according to your needs, such as setting up self-hosting or integrating with other AI frameworks
5. Try running a session replay to verify that AgentOps is working correctly

About

Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and CamelAI

Official site: https://agentops.ai

Category & Tags

Category: multi-agent

Tags: agent, agentops, agents-sdk, ai, anthropic, autogen, cost-estimation, crewai, evals, evaluation-metrics, groq, langchain, llm, mistral, ollama, openai, openai-agents

Market Context

Competitive AI market with AgentOps leading