Agent observability
Provides observability for AI agents and LLM applications so developers can inspect system behavior while building and deploying them.
AgentOps
Freemium
AgentOps is a developer platform for building, tracing, debugging, and deploying AI agents and LLM applications. It provides observability for agents built with OpenAI, CrewAI, AutoGen, and more than 400 LLMs and frameworks.
AgentOpsとは？
AgentOps is a developer platform for building AI agents and LLM applications. Its core purpose is to help developers trace, debug, and deploy these systems with agent observability. The site presents AgentOps as a tool for understanding agent behavior during development and deployment rather than as an end-user chatbot or standalone model.
The platform supports observability for OpenAI, CrewAI, AutoGen, and more than 400 LLMs and frameworks, giving teams a way to work with agent stacks that use different model and framework combinations. The documented installation cue is pip install agentops, indicating a Python package-based setup path.
AgentOps also provides educational material for AI agent development and evaluation through courses. The listed course topics include agent fundamentals, evaluation, and RAG chatbot development, making the product site relevant both to teams instrumenting agent applications and developers learning how to build and assess them.
The available source does not establish pricing, plan limits, detailed outputs, or specific collaboration controls. The pricing URL returned a 404, so those details should be confirmed directly with AgentOps before purchase or deployment decisions.
AgentOpsでできること
Tracing and debugging
The platform is positioned for tracing and debugging agent workflows, helping developers investigate how an application operates rather than treating the agent as a black box.
Broad framework and model coverage
The site states support for OpenAI, CrewAI, AutoGen, and more than 400 LLMs and frameworks.
Python package installation
The site provides `pip install agentops` as the installation cue, indicating a package-based path for adding AgentOps to a development environment.
Agent development education
AgentOps offers courses covering AI agent fundamentals, evaluation, and RAG chatbot development.
利用シーン
“Developing AI agents”
Use AgentOps while building agent applications to add an observability layer around development and deployment work.
“Investigating agent behavior”
Use tracing and debugging capabilities to examine an agent or LLM application when its behavior needs to be understood during development.
“Working across agent stacks”
Use the platform when an application uses supported technologies such as OpenAI, CrewAI, or AutoGen, or when a team works with multiple LLMs and frameworks.
“Learning agent evaluation”
Use the AgentOps course material to study how AI agents are built and how their performance can be evaluated.
“Building RAG chatbots”
Use the listed Chatbots 101 course as an educational starting point for building and deploying RAG chatbots with modern AI techniques.
よくある質問
How is AgentOps installed?
The website shows `pip install agentops` as the installation command, indicating a Python package-based setup path. The available source does not provide further setup steps.
Which technologies does AgentOps support?
The site names OpenAI, CrewAI, and AutoGen, and states that AgentOps supports more than 400 LLMs and frameworks. The complete compatibility list is not provided in the available source.
What can AgentOps help developers do?
AgentOps is presented as a platform for tracing, debugging, and deploying reliable AI agents and LLM applications through agent observability.
Does the source confirm pricing or plan limits?
No. The supplied pricing URL returned a 404, and the available page text does not provide verified prices, plan limits, or billing details.
Does AgentOps provide learning resources?
Yes. Its courses page lists courses on AI agent fundamentals, agent evaluation, and RAG chatbot development. The listed sessions are described as coming soon and use Zoom.
クイック情報
- Product type
- Developer platform for AI agents and LLM applications
- Primary users
- Developers building and deploying AI agents
- Installation cue
- pip install agentops
- Named integrations
- OpenAI, CrewAI, and AutoGen
- Supported ecosystem
- 400+ LLMs and frameworks, according to the site
- Learning resources
- Courses on agents, evaluations, and RAG chatbots
AgentOpsの代替品
HAR HQ
harproject.dev
HAR HQ is a team governance and observability layer for organizations running coding agents with HAR. It standardizes verification, tracks AI-related work and spend, and helps engineering teams review and improve agent workflows.
Evlat
evlat.kalaomer.com
Evlat is a quiet macOS status strip for monitoring Claude Code, Codex, and Antigravity sessions, plus long-running commands. It highlights sessions that need your response and shows activity from local or SSH-connected machines.
OpenController
www.lyzr.ai
OpenController is Lyzr’s control plane for discovering, evaluating, governing, and monitoring AI agents, models, tools, data, and workflows across an enterprise AI estate. It is intended for teams managing agents across clouds, frameworks, runtimes, and environments.
Privent
www.privent.ai
Priventは、特にn8nワークフロー向けのエージェント型AI用ランタイムセキュリティレイヤーです。外部モデルに届く前に機密データをマスキングし、リスク判断を監査記録として残し、ChatGPT、Claude、Geminiの組織全体での利用も監視します。
Orchestra
getorchestra.io
データとAIのワークフローを構築・実行・監視する宣言型オーケストレーション基盤
LangSmith
www.langchain.com
LangSmith is an observability and evaluation platform for AI agents and LLM applications. It helps development and production teams trace agent behavior, monitor quality and cost, investigate failures, and evaluate changes.