AgentOps tools help teams inspect the observable steps behind an AI agent’s response—such as model calls, retrieval, tool use, errors, timing, and cost—so they can investigate failures and improve evaluations. The 19 products below serve different jobs: tracing, testing, request monitoring, quality controls, and broader IT operations. Treat the list as a useful inventory, not a ranking or complete census of the market.
What AgentOps monitoring can—and cannot—show
An agent trace can connect the events an application records during a run: a model request, a retrieval result, a tool call, a handoff, an error, and the final output. Depending on the instrumentation and product, teams may also inspect latency, token use, estimated spend, evaluation scores, and outcomes. This gives engineers and operations teams evidence to investigate what happened and where a run diverged from expectations.
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Observability is not access to a model’s private hidden reasoning. It reconstructs application-visible execution from the telemetry the system collects. The 2024 AgentOps paper by Liming Dong, Qinghua Lu, and Liming Zhu describes observability as a way to help stakeholders understand agent behavior, detect anomalies, and prevent failures; it proposes a taxonomy for tracing artifacts across the agent lifecycle, rather than testing the products below. Read the paper abstract.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteMonitoring is most useful when a system has multiple steps—such as retrieval, routing, retries, memory, tool calls, or feedback—and a simple request-and-response log no longer explains failures. For the broader category framing and list of 19 products, see CIO’s July 16, 2026 guide.
What the 19 tools focus on
This table summarizes the editorial positioning in CIO’s July 2026 guide; it is not an independent hands-on assessment. The categories overlap, and a product’s practical fit depends on its current capabilities, instrumentation, and the workflow a team needs.
| Tool | Reported focus | Most relevant when the team needs to… |
|---|---|---|
| AgentOps.ai | Agent events, replay, token and spend visibility, and latency | Debug complex agent runs and inspect their recorded sequence. |
| Arize Phoenix | Tracing and evaluation across model providers and frameworks | Trace workflows and score outputs, including with LLM-as-a-Judge methods. |
| BigPanda | Operations issue detection and triage | Correlate issues against historical data and route alerts in a large operations environment. |
| Braintrust | Production traces feeding test cases and evaluations | Turn observed behavior into continuous tests and regression checks. |
| Chronicle Labs | Staging and agent backtesting | Use telemetry-derived cases to test agent releases. |
| Comet Opik | Trace tracking and agent evaluation | Score agent or RAG runs; CIO’s guide describes more than 30 metrics. |
| Datadog | AI telemetry within broader operations monitoring | Connect LLM performance with existing operations and cost views. |
| Dynatrace | Full-stack dataflow and autonomous monitoring | Monitor AI workflows in complex hybrid environments. |
| Galileo | Quality monitoring and runtime guardrails | Focus on issues such as hallucinations and data leakage. |
| Grafana Labs | Telemetry, dashboards, and AI-level analysis | Investigate system visibility alongside drift or context degradation. |
| Helicone | Proxy-based request monitoring | Review request records, latency, model failures, and AI spend. |
| Laminar | OpenTelemetry-oriented trace search and replay | Search traces, inspect transcripts, and replay recorded runs. |
| LangChain LangSmith | Agent traces, cost and tool visibility, alerts, and regression testing | Work with LangChain or LangGraph workflows; newer comparisons also describe framework-agnostic support. |
| Lunary | Interaction analytics and prompt iteration | Review dashboards, user feedback, and costs while refining prompts. |
| New Relic | Full-stack monitoring for AI workflows | Extend an established enterprise monitoring environment. |
| Nova AI Ops | Cloud telemetry and incident response | Use agent-assisted incident detection and potential self-healing actions. |
| Splunk | Enterprise logging and AI or agent operations | Extend existing enterprise logging and legacy systems. |
| SuperPenguin | Cost allocation and forecasting | Allocate usage by customer, feature, or team. |
| Vellum | Prompt and multi-model workflow management | Manage prompt workflows and pursue cost optimization. |
These descriptions reflect how the CIO guide positions each product, not verified feature guarantees. Features, integrations, and deployment choices can change; confirm that a shortlisted product supports the exact framework, data, and actions your team needs.
Choose by the work you need to do after a trace appears
Start with the operational decision the telemetry should enable. LangChain’s vendor-authored comparison makes the same practical point: choose around the workflow the team will actually run when production behavior becomes surprising. The following checks help turn that into a shortlist.
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Can you see enough of the execution to diagnose it?
Check whether instrumentation captures the steps that matter in your application: model requests, retrieval, tool invocations, errors, timing, and final outcomes. A record containing only the prompt and response may not explain why a multi-step agent failed. Also verify that the trace helps surface recurring patterns rather than merely storing events. LangChain’s comparison and Arize’s July 2026 guide discuss trace and debugging considerations.
Can a production failure become a regression test?
If the goal is to prevent a failure from returning, check for a path from production traces to datasets, experiments, scores, and repeatable evaluation runs. The LangChain comparison positions Braintrust around datasets, experiments, scores, and evaluation runs; LangSmith around production debugging and evaluation; and Langfuse around traces, prompts, datasets, and evaluations. These are vendor-published descriptions, not comparative test results.
Does the deployment model fit your ownership requirements?
Establish whether you need a hosted service, self-managed deployment, or both. Compare license and terms, retention, infrastructure requirements, support, and whether the product accepts the telemetry conventions or SDK instrumentation your stack already uses, including OpenTelemetry or OpenInference where relevant. Arize’s July 2026 article describes Phoenix as open source and AX as SaaS or enterprise self-hosted; its September 2026 alternatives guide describes Langfuse as MIT-licensed and self-managed. Verify current licensing and deployment terms directly before selecting a product. Arize’s September 2026 alternatives guide
Rank #4
Will it fit the systems and framework you already operate?
A team whose production operations already run in Datadog may value correlating AI telemetry with its existing APM, logs, infrastructure, security, and cost views. Teams invested in LangChain or LangGraph may find LangSmith’s alignment relevant. Those are conditional fit considerations, not universal recommendations; confirm integrations and workflow support against your architecture.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteWhat is the real billing unit at your expected volume?
Pricing may be tied to seats, traces, spans, events, requests, or data volume. Estimate telemetry per agent task, then model expected task volume and retention. Include overages, storage, deployment, and staff time in the estimate: a starting price alone cannot establish total cost or make two plans comparable.
Do you need debugging, guardrails, or governance?
Separate engineering diagnostics from runtime quality controls and risk reporting. If quality or safety scoring is central, test the evaluator against domain-specific human labels; a feature description does not establish that its scores are accurate for your use case. If the need is auditability or operational risk management, verify what data and reports the product actually retains and produces.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reported price examples are not like-for-like quotes
The figures below were reported by comparison publishers in 2026, not independently confirmed vendor quotes. Where the billing unit is not stated in the available comparison, it is marked accordingly rather than inferred. Plan limits, terms, and prices can change, so verify them with the vendor before budgeting.
| Product and reported plan | Reported figure | What the comparison specifies |
|---|---|---|
| LangSmith Plus | $39 per seat per month | Per-seat monthly rate; LangChain comparison, 2026. |
| Datadog Agent Observability Pro | Starts at $160 per month, billed annually | For the first 100,000 LLM spans, as reported by LangChain in 2026. |
| Langfuse Core | $29 per month | Billing unit not stated; LangChain comparison, 2026. |
| Helicone Pro | $79 per month | Billing unit not stated; LangChain comparison, 2026. |
| Portkey Production | $49 per month | Billing unit not stated; LangChain comparison, 2026. |
| Comet Opik Pro | $19 per month | Billing unit not stated; Arize comparison, 2026. |
| Braintrust Pro | $249 per month | Billing unit not stated; Arize comparison, 2026. |
| Arize AX Pro | $50 per month | Billing unit not stated; Arize comparison, 2026. |
Price sources: LangChain’s 2026 comparison and Arize’s 2026 comparison. These vendor-authored comparisons do not provide a normalized total-cost analysis.
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How to evaluate a shortlist in your own stack
- Pick one representative agent workflow. Include the retrieval, tool use, routing, or retries that make your real system difficult to debug.
- Define the events you need to inspect. Specify which model calls, inputs and outputs, tool activity, errors, timings, outcomes, and cost fields must appear in a usable trace.
- Decide what action should follow an incident. Examples include finding repeated failures, creating an evaluation case, routing an alert, tightening a runtime control, or allocating spend to a team.
- Check deployment and data handling. Confirm instrumentation, retention, access, and deployment requirements against your organization’s policies and existing infrastructure.
- Estimate cost using your workload. Apply each candidate’s actual billing unit to expected volume and retention, then account for overages and operational effort.
- Run a representative evaluation. Use known successful and failed cases, compare the resulting traces and evaluations, and have domain experts review quality or safety scores.
No market-wide adoption or performance statistic establishes a winner among these 19 products, and the cited comparisons are not a benchmark of them. Treat the inventory as a starting shortlist; the most defensible choice is the one that captures the signals your team needs and fits the action it will take next.
Quick Recap
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