AgentOps
Agent observability that traces, replays, and debugs AI agent runs across 400+ LLMs and frameworks
If your agents fail in ways logs can't explain, AgentOps earns its $40/mo fast — replay is the differentiator, not the dashboards. The 5,000-event free tier is enough to evaluate on a hobby project and not much else, since real agent traffic burns through it in days. Generic APMs like Datadog or New Relic will show you a slow span; AgentOps shows you the agent's reasoning at the moment it went sideways.
Verified 1d ago · liveness 78/100 · cite: rightaichoice.com/tools/agentops
- Teams debugging multi-agent systems built with CrewAI or Autogen
- Developers who need replay-level tracing to find where an agent went wrong
- Organizations tracking LLM token spend across multiple agents and providers
- Enterprises that require SOC-2, HIPAA, or on-prem deployment for agent workloads
- Simple single-agent chatbots that only need basic logging
- Projects that will exceed 5,000 events/month but won't pay for Pro
- Teams already satisfied with a generic APM and no agent-specific needs
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Skip AgentOps if you only need basic logging for a simple single-agent chatbot, or if you're a small project that won't use time travel debugging or audit trails—it's more than you need.
Going past 5,000 events per month on the free plan requires upgrading to Pro at $40/mo, which might be steep for hobbyists.
AgentOps' free tier is generous for experimenting, but Pro at $40/mo is competitive with other agent observability tools. For teams that need unlimited events and retention, Pro is a solid value compared to Datadog's per-host pricing, but larger teams may find that Datadog's enterprise plans offer more integrations at a similar price.
In short
AgentOps — Agent observability that traces, replays, and debugs AI agent runs across 400+ LLMs and frameworks. Best for Teams debugging multi-agent systems built with CrewAI or Autogen, Developers who need replay-level tracing to find where an agent went wrong, Organizations tracking LLM token spend across multiple agents and providers. Free to start; paid plans from $40/mo.
Viability Score
How well maintained and how widely used is AgentOps? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Visual trace of LLM calls, tool invocations, and multi-agent interactions
- Time travel debugging: rewind and replay agent runs with point-in-time precision
- Replay analytics to surface failure patterns across recorded agent sessions
- Prompt injection detection flags potential attacks on agent inputs
- Full audit trail of logs, errors, and prompt injection events
- Token count tracking across every LLM your agent calls
- Cost tracking and visualization with up-to-date price monitoring for 400+ LLMs
- Fine-tune specialized LLMs up to 25x cheaper on saved completions
- Agent agnostic SDK installable via pip install agentops
- Native integrations with top agent frameworks including OpenAI, CrewAI, and Autogen
- Session and event export (Pro)
- Role-based permissioning (Pro)
- Self-hosting on AWS, GCP, or Azure (Enterprise)
- Custom data retention policy (Enterprise)
- SOC-2, HIPAA, and NIST AI RMF compliance alignment (Enterprise)
About AgentOps
AgentOps is a developer platform for teams putting AI agents and LLM apps into production. Install one SDK and it traces your existing stack — OpenAI, CrewAI, Autogen, and 400+ other LLMs and frameworks — without re-architecting your code. The pitch is end-to-end agent observability: visualize LLM calls, tool use, and multi-agent interactions in one trace instead of stitching together logs from five vendors. The feature people actually sign up for is time travel debugging. You rewind and replay an agent run at point-in-time precision, stepping through the exact call that broke rather than inferring it from stack traces. Replay analytics mine recorded sessions for recurring failure patterns, and prompt injection detection flags suspicious inputs before they become incidents. A full audit trail captures logs, errors, and injection events for debugging and compliance review. Cost control sits alongside the debugging tools. AgentOps tracks token counts and spend across multiple agents with current price monitoring for 400+ LLMs, so you can see which agent is quietly burning budget. Separate fine-tuning lets you train specialized models on saved completions, which the vendor claims runs up to 25x cheaper than starting from scratch. Pricing starts free at 5,000 events/month. Pro begins at $40/month and lifts the event cap, retention limit, and adds session/event export, role-based permissioning, and dedicated Slack and email support. Enterprise adds SLA, custom SSO, on-prem deployment, self-hosting on AWS, GCP, or Azure, and SOC-2, HIPAA, and NIST AI RMF alignment. If you run multi-agent systems, AgentOps competes on replay and agent-aware tracing that general-purpose APM tools don't model.
Behind the Verdict
Where AgentOps earns its keep is the debugging loop. When a CrewAI or Autogen crew returns garbage, replaying the exact run and stepping through tool calls beats grepping JSON logs by a wide margin. We'd reach for this the day a multi-agent pipeline starts failing intermittently in production — that's the scenario the product is built around. Cost tracking is the sleeper feature. If you're running several agents against different providers, token spend gets opaque quickly, and per-agent attribution is genuinely useful before finance asks why the API bill doubled. When to pass: simple single-agent chatbots with basic logging don't need replay or audit trails, and the free tier's 5,000 events won't survive contact with real traffic. Teams already standardized on a generic APM and happy with it have little reason to add a second observability vendor. Compared to LangSmith, which leans into the LangChain ecosystem, AgentOps positions itself as agent-agnostic across 400+ frameworks — that matters if your stack isn't all LangChain. Against Datadog or New Relic, the tradeoff is narrower: they monitor infrastructure broadly, AgentOps understands agent semantics specifically. One caveat: the pricing page notes Pro is pay-as-you-go with a pricing calculator, so budget for event volume rather than treating $40 as a flat ceiling. Enterprise compliance claims come from the vendor page itself — verify current certifications directly during procurement, since attestations change. The 2025 rebrand to 'High Agency' and the blog's agent-expert service line suggest AgentOps is moving beyond pure tooling into services. For buyers, that's a signal the company is chasing enterprise deals; watch whether the self-serve product keeps pace.
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Real-world workflow fit
Concrete scenarios for the personas AgentOps actually fits — and what changes day-one when you adopt it.
You notice your agents are failing intermittently and can't reproduce the issue in production.
Outcome: You use AgentOps' time travel debugging to replay the exact failing run, step through each agent's decision, and pinpoint the faulty tool call—saving hours of log scraping.
Your team's LLM spend is growing with multiple agents running on different providers.
Outcome: You set up AgentOps to track token counts and spend per agent, and use the cost dashboard to identify high-cost workflows—helping you optimize prompts and models.
You need to demonstrate audit trails and prompt injection protection for an AI system under SOC-2.
Outcome: AgentOps captures a full audit trail of logs, errors, and injection events, and the Enterprise tier provides compliance certifications (SOC-2, HIPAA) and custom data retention—so you can pass audits with confidence.
Use Cases
- Monitor and debug multi-agent interactions in real-time
- Track token usage and costs across multiple agents and LLM calls
- Replay past agent runs to diagnose failures or unexpected behavior
- Audit agent actions for security and compliance requirements
- Fine-tune LLMs on saved agent completions to reduce costs
- Visualize agent workflows for team collaboration and debugging
- Set up alerts for critical agent failures and anomalies
- Export session data for deeper analysis in external tools
Models Under the Hood
as of 2026-08-31
Limitations
- AgentOps is an observability platform for AI agents and LLM apps, not a model provider.
- The free Basic plan is limited to 5,000 events per month, while Pro starts at $40 per month with unlimited events and log retention, and Enterprise offers custom pricing with additional features like SSO and on-premise deployment.
as of 2026-08-30
Verification history
We have re-verified AgentOps 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 19 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published AgentOps tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Basic
$0/mo
Ideal for
Solo developers exploring agent observability with a small scale (≤5,000 events/month) and basic cost tracking.
What this tier adds
Free entry point with agent-agnostic SDK, LLM cost tracking, and replay analytics, but limited to 5,000 events and no export or role-based permissions.
Pro
starts at $40/mo
Ideal for
Professional developers and small teams that need unlimited event tracing, long retention, and data export for debugging and analysis.
What this tier adds
Adds unlimited events, unlimited log retention, session/event export, dedicated support, and role-based permissioning over Basic.
Enterprise
Custom
Ideal for
Larger organizations with compliance requirements (SOC-2, HIPAA, NIST AI RMF) needing on-prem deployment and custom SLAs.
What this tier adds
Adds SLA, Slack Connect, custom SSO, on-premise or self-hosted deployment, custom data retention, and full compliance certifications beyond Pro.
Where the pricing makes sense
The company stage and team size where AgentOps's pricing actually pencils out — and where peers do it cheaper.
AgentOps' free tier is generous for experimenting, but Pro at $40/mo is competitive with other agent observability tools. For teams that need unlimited events and retention, Pro is a solid value compared to Datadog's per-host pricing, but larger teams may find that Datadog's enterprise plans offer more integrations at a similar price.
Setup time & first value
How long it actually takes to get something useful out of AgentOps — broken out by persona, not the marketing-page minute.
For most developers, you can install the SDK with pip and get basic tracing in under 10 minutes. Adding agent-specific integrations (CrewAI, Autogen) may take up to an hour. For enterprise features like SSO and on-prem, plan for a few days to align with security and compliance teams.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “AgentOps”, and we withheld 6: 6 could not be judged, because “AgentOps” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about AgentOps.
Official links
Tools that pair well with AgentOps
Common stack mates teams adopt alongside AgentOps, with the specific reason each pairing earns its keep.
Comet
AI observability and evals that turn agent traces into git-committed code fixes
Fiddler AI
Fiddler AI is an enterprise AI control plane for agent observability, guardrails, and governance across the agentic lifecycle.
Evidently AI
Open-source AI evaluation and observability for LLMs, RAG, agents, and predictive ML models.
Alternatives to AgentOps
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Fiddler AI
Fiddler AI is an enterprise AI control plane for agent observability, guardrails, and governance across the agentic lifecycle.
Evidently AI
Open-source AI evaluation and observability for LLMs, RAG, agents, and predictive ML models.
Frequently Asked Questions
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