AgentOps is live · free plan, paid from $40/mo · last verified 22 Oct 2026 · viability healthy
AgentOps
Trace, replay and debug AI agent runs — LLM calls, tool use and multi-agent interactions in one timeline, via a single SDK
If your agents fail in ways logs can't explain, AgentOps earns its keep quickly — replay is the differentiator, not the dashboards. Datadog and New Relic will show you a slow span; AgentOps shows the agent's reasoning at the moment it went sideways, and the prompt injection detection plus audit trail make it easier to defend an agent in a security review. It is built for CrewAI, Autogen and OpenAI-stack multi-agent work and is wasted on a single-agent chatbot where basic logging suffices. Budget honestly: Basic is $0/mo but capped at 5,000 events, and Pro is pay-as-you-go from $40/mo.
Verdict generatedVerified 8d ago · liveness 80/100 · 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 needing SOC-2, HIPAA or self-hosted agent telemetry
- Simple single-agent chatbots that only need basic logging
- Hobby projects that will never use replay, audit trails or cost tracking
- Teams already standardized on a generic APM with no agent-specific needs
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Skip AgentOps if you run a single-agent chatbot with trivial call volume where basic logging already answers every question, and a pay-as-you-go event bill would buy you nothing you'd actually open.
Basic is free only to 5,000 events per month; past that you move to Pro, which is pay-as-you-go starting at $40/mo, so a busy multi-agent system bills more the more you debug it.
Basic at $0/mo for 5,000 events is a genuinely usable evaluation tier and cheaper than most agent observability peers. Pro is pay-as-you-go from $40/mo — fine for a small platform team, but the meter tracks your agent traffic, so a high-volume production fleet can outgrow it in a way fixed-seat competitors like LangSmith won't. Enterprise with self-hosting and SOC-2 is where regulated buyers land.
In short
AgentOps — Trace, replay and debug AI agent runs — LLM calls, tool use and multi-agent interactions in one timeline, via a single SDK. 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.
No head-to-head comparison for AgentOps yet — the alternatives below are the closest matches we list.
What is AgentOps?
AgentOps — Trace, replay and debug AI agent runs — LLM calls, tool use and multi-agent interactions in one timeline, via a single SDK. 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.
AgentOps is a developer observability platform for AI agents and LLM apps. You add the SDK with pip install agentops and it traces what your stack already does: LLM calls, tool invocations and multi-agent interactions land in one visual trace rather than being stitched together across separate logging vendors. The vendor names native integrations with OpenAI, CrewAI and Autogen and claims coverage of 400+ LLMs and frameworks, and says 4,000+ engineers use the product. The headline capability is time travel debugging — rewinding and replaying an agent run with point-in-time precision so you can step through the exact call that broke. Replay analytics mines recorded sessions for recurring failure patterns, prompt injection detection flags suspicious inputs before they become incidents, and a full audit trail keeps logs, errors and injection events from prototype to production. Cost control sits alongside the debugging tools: token counts and spend visualization with up-to-date price monitoring across agents, plus fine-tuning of specialized LLMs on saved completions, which the vendor markets as up to 25x cheaper. The Basic tier is $0/mo for up to 5,000 events; Pro starts at $40/mo on a pay-as-you-go basis; Enterprise is custom. It suits teams running multi-agent systems — particularly CrewAI and Autogen builds — where a generic APM shows a slow span but not the agent reasoning that led there.
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 surfacing 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 price monitoring across 400+ LLMs
- Fine-tune specialized LLMs on saved completions, marketed as up to 25x cheaper
- Agent-agnostic SDK installed with pip install agentops
- Native integrations with top agent frameworks including OpenAI, CrewAI and Autogen
- Session and event export
- Role-based permissioning
- Self-hosting on AWS, GCP or Azure
- Custom data retention policy
- SOC-2, HIPAA and NIST AI RMF alignment
Behind the verdict
AgentOps positions itself narrowly and sensibly: observability for agents specifically, not generic LLM logging. The strongest argument is time travel debugging. A normal trace tells you span 14 took 9 seconds and returned a malformed tool call; replay lets you rewind to that point in the run and inspect the state the agent was in when it decided to call the tool that way. For multi-agent systems — the CrewAI and Autogen builds the vendor targets — that distinction matters, because failures usually come from one agent misreading another's output several hops back, which is exactly the thing flat logs lose. The security surface is the second differentiator. Prompt injection detection flags suspicious agent inputs, and the audit trail captures logs, errors and injection events in one place. Teams that need SOC-2, HIPAA or NIST AI RMF alignment, or that cannot send data to a multi-tenant SaaS, get custom SSO, on-premise deployment and self-hosting on AWS, GCP or Azure at the Enterprise tier. That is a real procurement unlock rather than a checkbox. Cost tooling is competent but less distinctive: token tracking, spend visualization with price monitoring across 400+ LLMs, and fine-tuning on saved completions for the vendor's claimed 25x savings. The fine-tuning claim is a vendor number and worth testing against your own completions before you plan around it. The honest weaknesses are the free tier and the instrumentation tax. Basic is $0/mo up to 5,000 events, and agent traffic — every LLM call, every tool invocation, every multi-agent handoff — burns events fast, so treat Basic as evaluation only. Pro is pay-as-you-go starting at $40/mo, which means the bill scales with the traffic you are trying to debug. And replay and audit are only as good as the instrumentation you enable in the SDK: if you sample heavily or skip tool spans, replay shows you an incomplete run. Compare against LangSmith if your stack is LangChain-native and against Datadog LLM Observability if you already pay for Datadog and want agent spans inside existing dashboards rather than a second vendor.
Limitations
- AgentOps is an observability and debugging platform for AI agents and LLM apps, not a model provider — it traces calls across 400+ LLMs but does not serve or host models itself.
- The Basic tier is free up to 5,000 events per month, so treat it as an evaluation tier for agents whose traffic consumes events quickly.
- Paid tiers start at $40/month for Pro (pay-as-you-go), with unlimited events and retention, and Enterprise adds SLA, SSO, on-premise and self-hosting options.
- Fine-tuning is offered on saved completions with a vendor-claimed 'up to 25x cheaper' figure that is marketing, not a benchmark against your own data.
as of 2026-10-22
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Is AgentOps still active?
AgentOps is live · free plan, paid from $40/mo · last verified 22 Oct 2026 · viability healthy.
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: October 2026
How we score →What do users say about AgentOps?
What people actually say about AgentOps — is it worth it?
We scanned public community sources for AgentOps on Oct 15, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 4 of the posts we fetched could be positively tied to AgentOps. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about AgentOps— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 22 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified AgentOps 22 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.
- — changed limitations, models
- — changed limitations, switching in, summary, switching out, who should skip it, our verdict, description, setup time, pricing fit, features, who it suits, pricing tiers, hidden costs, use cases
- — changed SSL certificate
- — changed features, description, our verdict, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed features, pricing tiers, description, our verdict, who it suits, summary, integrations, who should skip it
- — changed domain expiry
Showing the 6 most recent of 22 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does AgentOps cost in 2026?
AgentOps: Free to start; paid plans from $40/mo. Pricing last verified 22 Oct 2026.
Plans compared
Every published AgentOps tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Basic | Free | — | Solo developer or small team instrumenting a first agent to see whether replay-style tracing actually helps their failure modes.Adds: Free entry point: $0/mo for up to 5,000 events with the agent-agnostic SDK, replay analytics and cost tracking across 400+ LLMs. |
| Pro | From $40/moVendor: “Pay as you go, starts at $40/mo” | monthly | Platform teams running multi-agent workloads in production who need unlimited events, export into their own warehouse and scoped team access.Adds: Adds unlimited event limit and log retention, session and event export, role-based permissioning and dedicated Slack and email support, at pay-as-you-go pricing starting at $40/mo. |
| Enterprise | Custom | quote | Regulated organizations that must keep agent telemetry inside their own cloud and produce SOC-2, HIPAA or NIST AI RMF evidence.Adds: Adds SLA, Slack Connect, custom SSO, on-premise deployment, self-hosting on AWS, GCP or Azure and custom data retention at custom pricing. |
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.
Where the pricing makes sense
The company stage and team size where AgentOps's pricing actually pencils out — and where peers do it cheaper.
Basic at $0/mo for 5,000 events is a genuinely usable evaluation tier and cheaper than most agent observability peers. Pro is pay-as-you-go from $40/mo — fine for a small platform team, but the meter tracks your agent traffic, so a high-volume production fleet can outgrow it in a way fixed-seat competitors like LangSmith won't. Enterprise with self-hosting and SOC-2 is where regulated buyers land.
Has AgentOps' pricing changed?
No plan price for AgentOps has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick AgentOps?
Pick AgentOps if you are 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 needing SOC-2, HIPAA or self-hosted agent telemetry.
Look elsewhere if you are Simple single-agent chatbots that only need basic logging, Hobby projects that will never use replay, audit trails or cost tracking, Teams already standardized on a generic APM with no agent-specific needs.
Skip it if Skip AgentOps if you run a single-agent chatbot with trivial call volume where basic logging already answers every question, and a pay-as-you-go event bill would buy you nothing you'd actually open.
GeneratedReal-world workflow fit
Concrete scenarios for the personas AgentOps actually fits — and what changes day-one when you adopt it.
A three-agent research pipeline returns a plausible but wrong answer one run in twenty. You install the SDK with pip install agentops and pull up the trace, then rewind and replay the failing run to the handoff where agent two misread agent one's tool output.
Outcome: You find the bad handoff in minutes instead of re-running the pipeline with print statements, and ship a prompt fix the same day.
Your agents call OpenAI for reasoning, Anthropic for drafting and Azure OpenAI for batch tasks. You use AgentOps token tracking and cost visualization with price monitoring across 400+ LLMs to see which agent and which model is driving the bill.
Outcome: A per-agent cost view lets you move the expensive agent to a cheaper model and quantify the saving before you touch production.
Before an agent touches customer data, you enable prompt injection detection and the audit trail so logs, errors and injection events are recorded from prototype through production.
Outcome: The audit trail plus SOC-2, HIPAA and NIST AI RMF alignment at the Enterprise tier gives the review board artifacts instead of assurances.
Use cases
- Trace a multi-agent CrewAI or Autogen run end to end to see which agent broke the chain
- Rewind and replay a failed agent run to inspect the exact call that went wrong
- Track token usage and spend across multiple agents and LLM providers in one view
- Audit agent actions, errors and prompt injection events for security review
- Fine-tune an LLM on saved agent completions to cut per-run inference spend
- Export sessions and events to your own warehouse for deeper analysis
- Give a platform team role-based access to agent traces across environments
- Self-host agent telemetry on AWS, GCP or Azure to keep data in your own cloud
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.
Developer: under 30 minutes to first trace — pip install agentops, add the API key, and your first agent run appears. Platform lead: a few hours to wire team access and alerts. Enterprise on-prem or self-hosted on AWS, GCP or Azure: plan days for procurement, SSO and deployment.
Switching to or from AgentOps
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: run the AgentOps SDK alongside your existing tracer, compare traces on the same agent run, then retire the old client once replay proves more useful for your failures.
- →From print-statement debugging: replace ad-hoc logging with the agent-agnostic SDK so every LLM call and tool invocation lands in one trace.
- →From Datadog APM alone: keep Datadog for infra and add AgentOps spans for agent reasoning, rather than ripping out your existing monitoring.
- ↗To LangSmith: rebuild tracing with the LangChain-native client, accepting that non-LangChain agents in your fleet need separate instrumentation.
- ↗To Datadog LLM Observability: consolidate agent spans into your existing Datadog dashboards and lose replay-style rewinding.
- ↗To a self-hosted open-source tracer: export sessions and events first, then re-instrument the SDK against the new backend.
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
What are the best AgentOps alternatives?
The closest alternatives to AgentOps we list are LangSmith, Braintrust and Athina AI. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| LangSmith | Freemium | Pricing not published | — |
| Braintrust | Freemium | Pricing not published | — |
| Athina AI | Freemium | Pricing not published | — |
Alternatives to AgentOps
View all
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Tools that pair well with AgentOps
Common stack mates teams adopt alongside AgentOps, with the specific reason each pairing earns its keep.
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Athina AI
Athina is a shared workspace where your team writes prompts, runs evals, annotates datasets, and traces LLM calls in production.
Frequently asked questions
Best-of guides
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