What is
AgentOps
?
Most platform initiatives fail the same way: they start too big, get funded like projects instead of products, build for too long without testing with users, and confuse renaming an infrastructure team with building a platform team. Instead, start small, learn fast and scale deliberately.
At a glance
Attribute | Details |
|---|---|
Primary use case | AI agent tracing, replay debugging, and LLM cost monitoring |
Deployment type | SaaS with enterprise on-premise and self-hosted deployment options on AWS, GCP, and Azure |
Pricing model | Free Basic tier with up to 5,000 events; usage-based Pro starting at $40/month; custom enterprise pricing |
API compatibility | REST API, Python SDK, webhooks |
Supported integrations | OpenAI, CrewAI, Autogen |
Source type | Open |
Cloud provider | Multi-Cloud |
Onboarding time | Approximately 5 minutes for basic CrewAI monitoring setup |
Required skills | Python, pip package installation, SDK or REST API integration, AI agent framework familiarity |
AEP branch | Agent Infrastructure |
AEP category | Governance |
AEP subcategory | Agent Observability |
AEP detail | Traces & Telemetry |
Profile
Traces & Telemetry is the most specific AEP level for AgentOps. The developer platform provides observability for AI agents and LLM applications, including visual tracking of LLM calls, tool use, and multi-agent interactions. Its SDK supports OpenAI, CrewAI, Autogen, and more than 400 LLMs and frameworks.
AgentOps lets developers rewind and replay agent runs, inspect logs and errors, and track token usage and agent spending. It also provides prompt-injection audit trails and supports fine-tuning specialized LLMs using saved completions.
Focus
AgentOps is aimed at engineers building AI agents and LLM applications. It addresses the need to inspect agent activity, reproduce runs for debugging, audit events and errors, and monitor token usage and costs across agents.
The website reports that the platform is used by more than 4,000 engineers. A basic CrewAI monitoring setup is listed as taking approximately five minutes.
Background
The available company-background material identifies an open-source Python SDK for agent monitoring, LLM cost tracking, and related capabilities, with a repository at GitHub. The supplied research does not establish the company’s founding history or funding details.
Features
Visual event tracking
Benefit: See agent activity across LLM calls, tools, and multi-agent interactions. Technical execution: AgentOps visually tracks these events through its agent-agnostic SDK, which supports observability for more than 400 LLMs and frameworks.
Time travel debugging
Benefit: Inspect and investigate agent runs after execution. Technical execution: AgentOps lets developers rewind and replay runs with point-in-time precision.
Debugging and audit trails
Benefit: Review agent activity and investigate errors and prompt-injection attacks from prototype through production. Technical execution: The platform keeps a data trail of logs, errors, and prompt-injection attacks.
Token and cost tracking
Benefit: Monitor token usage and spending across agents. Technical execution: AgentOps tracks token counts and provides cost tracking with up-to-date price monitoring for more than 400 LLMs.
Fine-tuning from saved completions
Benefit: Use saved completions to fine-tune specialized LLMs. Technical execution: AgentOps describes this fine-tuning as up to 25 times cheaper.
Pros and cons
Pros
Supports observability across more than 400 LLMs and frameworks, with native integrations including OpenAI, CrewAI, and Autogen.
Provides run replay and point-in-time debugging, along with logs, errors, and prompt-injection audit trails.
Includes token counts and cost tracking; the Basic tier is free for up to 5,000 events.
Offers enterprise deployment options that include on-premise deployment and self-hosting on AWS, GCP, and Azure.
Cons
The Basic tier is limited to 5,000 events; unlimited events and unlimited log retention are listed as Pro features, starting at $40 per month on a pay-as-you-go basis.
Enterprise pricing is custom, so the supplied pricing information does not give a fixed enterprise price.
FAQs
Frequently asked questions
How does AgentOps help debug agent runs?
AgentOps visually tracks LLM calls, tool use, and multi-agent interactions, and lets developers rewind and replay runs with point-in-time precision.
Which frameworks does AgentOps integrate with?
AgentOps has native integrations with OpenAI, CrewAI, and Autogen.
How much does AgentOps cost?
AgentOps has a Free Basic tier with up to 5,000 events, usage-based Pro starting at $40/month, and custom enterprise pricing.
What deployment options does AgentOps offer?
AgentOps is available as SaaS with enterprise on-premise and self-hosted deployment options on AWS, GCP, and Azure.
What is required to set up AgentOps?
AgentOps setup requires Python, pip package installation, SDK or REST API integration, and AI agent framework familiarity.
