AgentOps
Monitoring and session replay for AI agents.
Updated
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Use cases
About AgentOps
AgentOps is listed in our evals & observability category and is aimed at builders. Monitoring and session replay for AI agents. Pricing, as the maker states it: Free tier + paid plans. Maturity: Active. We last checked the official link on 2026-10-14.
What is AgentOps?
AgentOps is an observability and DevTool platform for AI agents that helps developers build, evaluate and monitor agents from prototype to production.
The AgentOps app is open source under the MIT license.
Sources: the official README and our directory listing, checked 2026-10-18. Official site: agentops.ai.
Who is AgentOps for?
Developers monitoring agent behavior and cost.
AgentOps at a glance
- Open source: Yes (MIT License)
- GitHub: AgentOps-AI/agentops · 5,901 stars (as of Oct 21, 2026)
Key features
- Agent monitoring
- Session replay
- Evaluation
- MIT-licensed app
AgentOps FAQ
What is AgentOps?
AgentOps is an observability and DevTool platform for AI agents that helps developers build, evaluate and monitor agents from prototype to production.
Who is AgentOps for?
Developers monitoring agent behavior and cost.
Is AgentOps free?
AgentOps's pricing, as the maker states it: Free tier + paid plans. Check the official site for current plans.
Is AgentOps open source?
Yes. AgentOps is released under the MIT License, according to its GitHub repository.
What are alternatives to AgentOps?
Similar tools include LangSmith, Confident AI, W&B Weave, Hamming AI, Basalt and Superlog.
AgentOps ratings and reviews
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AgentOps alternatives
Other evals & observability tools in our directory. See all evals & observability tools →
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Confident AI
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W&B Weave
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Hamming AI
Testing and QA platform for voice and chat agents that generates scenarios and replays real calls.
Basalt
Captures customer behavior so your AI agent can learn and improve from real interactions.
Superlog
Superlog runs AI agents on routine engineering work around production. You set up automations with triggers and connectors, for example rating each new Sentry issue, posting a plain-language summary to Slack and opening a pull request with the fix, comparing error rates and latency against their normal range every hour, adding missing logs and spans to hard-to-debug code paths, or answering community questions from your code and docs. You choose the model and agent harness and bring your own tokens or subscriptions. The core is open source (Apache-2.0) with a hosted version.
Related lists
🏆 See also: Best AI Agent Observability & Eval Tools · All best-of lists
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