AgentOps用于构建可靠 AI 代理的可观测性和调试平台

4.5 (4)

审阅者 Daniel Nikulshyn·更新 2026年5月

Developer Tools Observability Debugging AI Agents Monitoring Analytics SDK

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概览

AgentOps 是一个面向 AI 代理生命周期的开发者平台，提供追踪、监控和调试工具，能够在运行时展示代理的实际行为。它捕获 LLM 调用、工具使用情况、费用和错误，帮助团队了解代理在复杂的多步骤工作流中的行为。超越可视化，AgentOps 提供会话回放、性能分析，并与 LangChain、CrewAI、AutoGen 等流行的代理框架集成。这帮助工程师从原型阶段迈向生产阶段，凭借可度量的可靠性，而不必依赖猜测或日志抓取。它面向需要跟踪回归、控制成本，并在部署前后证明其代理行为正确的开发者和交付代理式应用的团队。

主要功能

代理会话记录和回放
LLM 调用和工具使用跟踪
成本和 token 分析
错误和故障检测
Python 和 JavaScript 的框架 SDK
代理性能指标仪表盘

价格

模型: Free
分类: 标做管球解析
评分: 4.5 / 5 (4)

使用场景

调试多步骤代理工作流

使用会话回放和 LLM 调用跟踪，精确定位代理在复杂多步骤运行中推理或工具使用出现问题的具体步骤。

监控 token 使用和成本

跟踪每个运行的 token 消耗和代理的支出，以控制预算并识别昂贵的提示或低效的工具调用。

在生产前捕获回归

在开发过程中检测代理行为中的错误和故障，帮助团队以可衡量的可靠性交付代理应用。

为 LangChain、CrewAI 或 AutoGen 代理添加监控

通过 Python 或 JavaScript SDK，实现对流行框架构建的代理的跟踪和性能仪表盘监控，无需自定义日志记录。

优点 & 缺点

优点

详细的会话回放和跟踪
与主要代理框架集成
跟踪每个运行的 token 使用和成本
适用于调试多步骤工作流

缺点

主要针对开发者，而非非技术用户
价值取决于框架兼容性
为 LLM 堆栈添加另一个工具

对决战绩

在万神殿中参与了 1 对决。

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Last battle

#4
Observability Showdown — July 25, 2026
Jul 25, 2026 · #4 of 9

评测

4.5

4 个评分的平均值。

登录以留下评测。

Rina Desai

May 10, 2026

Compared a few options

Evaluated this against two competitors. Where it wins: cost and token analytics and detailed session replay and tracing. On balance the feature set — especially cost and token analytics — justifies the 5 stars for our use case.

Robert Ainsworth

Feb 14, 2026

Does the job

Pretty happy overall. Error and failure detection just works and integrates with major agent frameworks. Primarily targets developers, not non-technical users can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Fatima Zahra

Jan 24, 2026

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on cost and token analytics, and tracks token usage and cost per run caught me off guard. Adds another tool to the LLM stack is why this isn't a perfect score, still, I'd recommend giving it a real trial.

Camille Laurent

Jul 24, 2025

Years in this space

I've evaluated a lot of these over the years. What stands out here is lLM call and tool-use tracing — handled better than most — and useful for debugging multi-step workflows. Worth the time if this is your use case.

问答

Can I replay and debug multi‑step agent sessions with AgentOps?

Yes, AgentOps records full session data, enabling point‑in‑time replay of LLM calls, tool interactions, and errors. This time‑travel debugging lets developers step through complex workflows to pinpoint failures or unexpected behavior.

Asked by Kwame Mensah · Oct 2, 2025

How does AgentOps help control token usage and cost for AI agents?

AgentOps tracks token counts and LLM call costs for each agent run, offering dashboards that visualize spend across multiple agents and up‑to‑date price monitoring, so teams can identify expensive calls and optimize budgets.

Asked by Björn Karlsson · Oct 1, 2025

Which agent frameworks does AgentOps integrate with out of the box?

AgentOps provides native SDK integrations for popular Python and JavaScript agent frameworks, specifically LangChain, CrewAI, and AutoGen, allowing developers to capture LLM calls, tool usage, and costs without custom instrumentation.

Asked by Rasheed Osman · Aug 23, 2025

What pricing plans does AgentOps offer and what are the limits of the free tier?

AgentOps has a free tier that includes up to 5,000 events per month with basic tracing and replay features. The Pro plan starts at $40 per month, adding unlimited events, unlimited log retention, export capabilities, and dedicated Slack/email support. Enterprise pricing is custom and adds SLAs, SSO, on‑premises deployment, and advanced compliance options.

Asked by Fernando Rojas · Jul 24, 2025