AgentOps
Session replay and cost tracking for AI agent debugging
Overview
AgentOps is a developer SDK for observing AI agents — traces every LLM and tool call, tracks token cost, and lets you replay a run step by step. Integrates with LangChain, CrewAI, AutoGen and 400+ frameworks/LLMs.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 11 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- Agent-agnostic SDK
- LLM cost tracking (400+ models)
- Replay analytics
- Unlimited event limit
- Unlimited log retention
- Session/event export
- Role-based permissions
- Dedicated Slack + email support
- SLA guarantees
- Custom SSO, Slack Connect
- Self-hosting (AWS/GCP/Azure)
- SOC-2, HIPAA, NIST AI RMF
Use cases
What you can produce with AgentOps
- Session replay / time-travel debugging
- Multi-agent workflow visualization
- Token and LLM-cost tracking across 400+ models
- SDK integrations: CrewAI, AutoGen, LangChain, OpenAI Agents SDK, Agno, AG2, CamelAI
- Role-based permissioning and Slack/email support (Pro+)
- SOC-2, HIPAA, NIST AI RMF compliance options (Enterprise)
ASEAN Perspective
AgentOps in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Once an agent is doing more than one LLM call, you lose visibility fast — which step looped, which tool call blew the budget, why the output drifted. AgentOps addresses that directly with session replay, per-call cost and token tracking, and multi-agent workflow visualization, wired in with a few lines of SDK code across a genuinely wide set of frameworks (CrewAI, AutoGen, LangChain, the OpenAI Agents SDK and more). Time-travel debugging — rewinding a run to a specific point — is a real differentiator for untangling multi-agent interactions rather than staring at a flat log.
It's a small, VC-backed company (~$2.6M raised) in a category that's filling up fast with competitors, and independent testing found roughly 12% runtime overhead from instrumentation — worth knowing before you wire it into a latency-sensitive path. The free tier's 5,000-events/month cap is smaller than it sounds, since every LLM call and every tool call counts separately. Worth adding the moment agent reliability, not agent capability, becomes your bottleneck; overkill for a single-call chatbot.
What people say
AgentOps positions itself, and gets described by third parties, as one of the more framework-agnostic options in the AI-agent-observability category that's crowded up through 2026 — aimultiple's roundup of 15 observability tools and Latitude's comparison piece both cite its breadth of integrations (CrewAI, Agno, the OpenAI Agents SDK, LangChain, Autogen, AG2, CamelAI) as the practical reason to pick it when a team is running more than one agent framework rather than standardizing on one.
The feature reviewers keep coming back to is time-travel / session replay — the ability to rewind a multi-agent run to a specific point and inspect exactly what each agent saw and did. Independent performance testing referenced in the observability roundups put instrumentation overhead at around 12%, characterized as a reasonable but non-trivial tax for the visibility gained.
On pricing, the free Basic tier caps out at 5,000 tracked events a month, and reviewers flag that this fills up faster than expected because AgentOps counts each LLM call and each tool invocation as a separate event — a single multi-step agent run can burn through a dozen-plus events on its own. The Pro tier starts at $40/month pay-as-you-go and removes the event cap along with adding unlimited log retention, data export, and dedicated support; Enterprise adds SLA guarantees, custom SSO, self-hosting on AWS/GCP/Azure, and SOC-2/HIPAA/NIST AI RMF compliance options for regulated teams. The company itself is small — founded 2023 in San Francisco by Alex Reibman, Adam Silverman and Braelyn Boynton, and has raised about $2.6M from 645 Ventures, Afore Capital and Plug and Play — which several reviewers note as a reason to weigh it against better-funded observability platforms if you need long-term vendor stability, even as they credit it with genuine, differentiated debugging features today. IBM's own AgentOps explainer treats the category itself, not just this vendor, as increasingly necessary infrastructure once agents move past a demo, which is the backdrop most of these reviews are written against.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including AgentOps's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with AgentOps unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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