Tracing, replay, cost monitoring, and debugging for AI agents
Who should use this?
Multi-step agent debugging and Tool and token cost tracing.
Who should avoid it?
Sensitive traces without redaction, No telemetry governance
What problem does it solve?
AgentOps makes agent runs easier to inspect, but traces can capture prompts, outputs, tool arguments, and customer data unless collection is minimized.
Would I recommend it?
AgentOps earns a shortlist for teams needing agent-specific debugging with a low-friction start. Begin outside production, disable unnecessary environment capture, redact before export, define retention, and forecast events from real traces.
Advisor score
Premium review framework
AgentOps makes agent runs easier to inspect, but traces can capture prompts, outputs, tool arguments, and customer data unless collection is minimized.
AgentOps earns a shortlist for teams needing agent-specific debugging with a low-friction start. Begin outside production, disable unnecessary environment capture, redact before export, define retention, and forecast events from real traces.
Instrument 200 synthetic runs with nested tools, failures, retries, sensitive decoys, concurrency, and provider changes. Measure trace completeness, redaction, isolation, replay fidelity, event multiplication, deletion, export, latency, and observed versus billed cost.
Personal Recommendation
AgentOps earns a shortlist for teams needing agent-specific debugging with a low-friction start. Begin outside production, disable unnecessary environment capture, redact before export, define retention, and forecast events from real traces.
Try the recommendation
Agent-focused instrumentation
Overall Score
Editorial Review Framework
Who should use this?
Multi-step agent debugging, Tool and token cost tracing, Replayable run history.
Who should avoid it?
Sensitive traces without redaction, No telemetry governance
What problem does it solve?
AgentOps makes agent runs easier to inspect, but traces can capture prompts, outputs, tool arguments, and customer data unless collection is minimized.
Would I recommend it?
AgentOps earns a shortlist for teams needing agent-specific debugging with a low-friction start. Begin outside production, disable unnecessary environment capture, redact before export, define retention, and forecast events from real traces.
Overall Score
8.0
Ease of Use
7.6
AI Quality
8.0
Features
8.2
Speed
7.8
Integrations
8.2
Value for Money
8.0
Customer Support
7.4
Learning Curve
7.2
Recommended Because…
Agent-focused instrumentation
Scores use a 0-10 editorial scale. The source data is maintained as 5-point review dimensions, then normalized for reader-friendly comparison.
Evaluation
Research-based
Price posture
From $40/month
Reviewed
2026-09-02
No authentic product screenshot is published for this review. DiscoverAI does not use generated interface images as product evidence.
AgentOps lists Basic at $0 for up to 5,000 events. Pro starts at $40 per month with usage pricing; verify the current calculator for retention, members, volume, and support. A self-hosting path is documented. Model usage remains separate. Reviewed September 2, 2026.
Free plan: Yes. Basic includes up to 5,000 events under the current public pricing page.
Yes. Basic is listed at $0 for up to 5,000 events.
Pro is advertised as starting at $40 monthly with usage pricing; verify the live calculator.
Depending on instrumentation, traces can include model calls, tools, tokens, costs, errors, prompts, and completions.
Yes. Official docs describe self-hosting, with security and operations then owned by the deployer.
Keep Deciding
AgentOps Review 2026: Agent Tracing, Pricing, and Security
AgentOps makes agent runs easier to inspect, but traces can capture prompts, outputs, tool arguments, and customer data unless collection is minimized.
Traceloop Review 2026: OpenLLMetry, Pricing, and Fit
Traceloop combines OpenLLMetry with hosted or private observability, but span volume, trace sensitivity, access, and evaluator validity determine fit.
Best AI Coding Tools
AI coding assistants ranked by real developer workflow fit, not demo magic.
Best AI Tools for Code & Development
A practical shortlist of AI tools for ai coding assistants, code generators, and developer tools.
Code & Development
AI coding assistants, code generators, and developer tools.
See how similar tools stack up
Open-source tracing, evaluation, prompt management, and metrics for LLM applications
Langfuse unifies traces, costs, prompts, datasets, and evaluation with cloud and self-hosted options, but telemetry sensitivity, retention, operational load, and fast-rising plan costs demand a scoped pilot.
Open-source tracing and evaluation for LLM, RAG, and agent applications
Phoenix gives teams OpenTelemetry-based traces, evaluations, experiments, datasets, and prompt tooling in a self-hostable project, but telemetry volume, sensitive content, evaluator validity, and operations remain buyer-owned.
An evaluation, prompt, dataset, and observability platform for AI product development
Braintrust connects production traces, datasets, experiments, scorers, prompts, and human review, but judge validity, sensitive logs, retention, score volume, and release-gate design require calibration.