AgentOps

Freemium

Observability and testing platform for AI agents

Verified by editorial·Last updated: April 2026·How we rank

Editor's verdict

AgentOps is a solid freemium pick, rated 4.2/5 by 380 users. Best for monitoring production ai agents for loops, failures, and unexpected behaviour and debugging multi-step agent workflows where standard logging is insufficient. Standout: session replays let developers rewind and inspect any step of an agent run. Watch out: relatively new product with a smaller community than LangSmith.

About AgentOps

AgentOps is an observability, testing, and monitoring platform built specifically for AI agents. As AI applications have moved from single LLM calls to multi-step agent workflows - where one model call triggers tool use, which triggers another model call, which produces an action - debugging and monitoring have become significantly harder. AgentOps tracks every step of an agent session, recording costs, latency, errors, and the full decision trace so developers can understand exactly what their agents did and why. The platform includes session replays that let developers rewind and inspect any point in an agent's execution, cost dashboards that show where token spend is going across agent runs, and failure detection that flags when agents get stuck in loops or hit unexpected errors. Teams building autonomous agents on frameworks like CrewAI, AutoGen, LangChain, and custom architectures use AgentOps as their monitoring layer. For AI product teams moving from prototype to production, AgentOps answers whether your agents are actually working as intended in the real world.

Pros & Cons

Pros

✓Session replays let developers rewind and inspect any step of an agent run
✓Cost dashboards surface where token spend is concentrated across agent executions
✓Supports CrewAI, AutoGen, LangChain, and custom agent frameworks out of the box

Cons

✗Relatively new product with a smaller community than LangSmith
✗Most value is unlocked for multi-step agent workflows rather than simple LLM calls
✗Dashboard and alerting features are still maturing compared to general observability tools

Best Use Cases

→Monitoring production AI agents for loops, failures, and unexpected behaviour
→Debugging multi-step agent workflows where standard logging is insufficient
→Tracking and optimising token costs across autonomous agent runs

AgentOps Preview

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Pricing

Free$0 / mo

ProFrom $0 / mo

EnterpriseCustom

Pricing verified April 2026. Verify current pricing on the official site before purchase.

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MytheAi Rating

4.2

★★★★☆4.2

380 aggregate ratings

Aggregate of third-party review platforms (G2, Capterra, Product Hunt) plus editorial testing. How we rank.

Last verified: April 2026

Editorial Scoring

How AgentOps scores on our 7-criteria framework

See methodology →

Criterion

Weight

Score

Output Quality

Accuracy, polish, and usefulness of what the tool produces.

25%

Ease of Use

Onboarding friction, UI clarity, time to first useful result.

15%

Pricing Value

Output per dollar at the realistic monthly cost for a typical user.

15%

Feature Depth

Breadth and maturity of capabilities relative to category leaders.

15%

Integrations

Native integrations, API quality, and ecosystem coverage.

10%

Reliability

Uptime, output consistency, and battle-test through scale.

10%

Trajectory

Recent product velocity and momentum vs the category.

10%

Overall editorial score

100%

3.95/5

Scores are editorial assessments based on hands-on testing and verified user data. They do not reflect affiliate relationships. How we score.

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AgentOps on MytheAi

Compared with AgentOps (1)

AgentOps vs Langsmith →tie
LangSmith and AgentOps are both observability platforms for LLM applications, but with different specialisations. LangSmith, built by the LangChain team, covers the full spectrum of LLM application observability - tracing chains, prompts, retrievals, and model calls - with a strong focus on evaluation: systematic testing of prompts and chains against labelled datasets before deployment. AgentOps focuses specifically on agent observability, tracking the session-level behaviour of autonomous agents: tool calls, loop iterations, cost per session, and failure patterns. The tools complement each other more than they compete. For teams using LangChain or LangGraph, LangSmith is the natural choice and integrates with near-zero configuration. For teams building custom agent loops with frameworks like AutoGen, CrewAI, or their own implementations, AgentOps provides session-level insight that generic tracing tools miss. In 2026, as more teams move from simple LLM chains to multi-step autonomous agents, the distinction between chain-level and session-level observability becomes practically important. LangSmith tells you what each call in a chain did. AgentOps tells you what an agent session accomplished, where it went wrong, and how much it cost. For production agent systems, using both in tandem is increasingly common.

Ranked in (1)

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Frequently Asked Questions

Is AgentOps free?▼

AgentOps offers a free tier with limited features. Paid plans start from $0/month.

What is AgentOps best for?▼

AgentOps is best suited for: Monitoring production AI agents for loops, failures, and unexpected behaviour, Debugging multi-step agent workflows where standard logging is insufficient, Tracking and optimising token costs across autonomous agent runs.

How does AgentOps compare to alternatives?▼

AgentOps holds a rating of 4.2/5 from 380 reviews. Browse our comparison pages to see detailed side-by-side breakdowns against similar tools.

Reviewed by

John Ethan

Founder & Editor-in-Chief

Founder of MytheAi. Tracking and reviewing AI and SaaS tools since January 2026. Built MytheAi out of frustration with pay-to-rank listicles and SEO-driven AI directories that prioritize ad revenue over honest guidance. Hands-on testing across 500+ tools to date.

AgentOps Review (2026): Is It Worth It?

AgentOps is a freemium tool with a free tier available. It holds a rating of 4.2/5 based on 380 reviews.

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