AgentOps
Trace, debug, and deploy AI agents with session replay and cross-framework cost tracking.
What is AgentOps?
AgentOps is an observability and debugging platform for AI agents, offering session replay, time-travel debugging, and cost tracking across 400+ LLMs and frameworks like CrewAI and Autogen. It is bought by engineering teams shipping multi-agent systems who need to see exactly what their agent did and how much it cost. Differentiator: point-in-time replay for multi-agent traces, not just LLM calls.
Tools for building, hosting, testing, observing, connecting, and giving memory or computer access to AI agents.
See the full Agent Infrastructure guide to compare more tools, buyer criteria, and related workflows.
Use cases to evaluate
Debugging CrewAI or Autogen multi-agent workflows
Tracking LLM token spend across multiple agents
Replaying failed agent sessions to find the broken step
Auditing agent behavior for compliance (SOC-2, HIPAA)
Fit to evaluate
Engineering teams running CrewAI, Autogen, or LangChain agents
Enterprises needing on-prem agent observability
AI platform teams managing cost across many models
Regulated industries (finance, healthcare) deploying agents
Business fit
Right for you if your agent has more than one LLM call per session and you have already lost time staring at logs trying to reconstruct a failure. Skip if you are running single-turn chatbots where standard LLM logging is enough. Enterprise compliance (SOC-2, HIPAA, NIST AI RMF) plus on-prem deployment suggest it can land in regulated industries. Free tier exists for solo developers, but contact sales for pricing on paid plans.
How to evaluate AgentOps
Use this category when a business wants agents that do work across tools, APIs, browsers, and data sources.
Confirm the exact workflow
Map AgentOps to one concrete workflow first, such as debugging crewai or autogen multi-agent workflows. Avoid buying before the owner, trigger, output, and success metric are clear.
Check category fit
Compare tool-calling, memory, browser automation, evals, observability, and deployment controls.
Compare practical alternatives
Shortlist AgentOps against Orgo, Browser Use, Browserbase so the decision is based on fit, effort, and workflow ownership rather than brand recognition alone.
Validate cost and rollout effort
Pricing is not published on the public site. Contact required (adam@agentops.ai or contact form) for paid and enterprise tiers. Free tier available for individual developers. Also confirm implementation time, support needs, and whether the technical setup matches your team.
Compare AgentOps with alternatives
Use this quick comparison before booking demos or moving data into a new system.
| Primary workflow | Debugging CrewAI or Autogen multi-agent workflows, Tracking LLM token spend across multiple agents |
|---|---|
| Best-fit team | Engineering teams running CrewAI, Autogen, or LangChain agents, Enterprises needing on-prem agent observability |
| Implementation effort | Technical setup and maintenance profile |
| Pricing check | Contact sales |
| Closest alternatives | OrgoBrowser UseBrowserbaseHyperbrowser |
AgentOps pricing
| Model | Contact sales |
|---|---|
| Snapshot | Pricing is not published on the public site. Contact required (adam@agentops.ai or contact form) for paid and enterprise tiers. Free tier available for individual developers. |
| Checked |
Common questions about AgentOps
What is AgentOps?
AgentOps is an observability and debugging platform for AI agents, offering session replay, time-travel debugging, and cost tracking across 400+ LLMs and frameworks like CrewAI and Autogen. It is bought by engineering teams shipping multi-agent systems who need to see exactly what their agent did and how much it cost. Differentiator: point-in-time replay for multi-agent traces, not just LLM calls.
What is AgentOps used for?
Common use cases: Debugging CrewAI or Autogen multi-agent workflows; Tracking LLM token spend across multiple agents; Replaying failed agent sessions to find the broken step; Auditing agent behavior for compliance (SOC-2, HIPAA).
How much does AgentOps cost?
Pricing is not published on the public site. Contact required (adam@agentops.ai or contact form) for paid and enterprise tiers. Free tier available for individual developers.
Who is AgentOps best for?
AgentOps fits Engineering teams running CrewAI, Autogen, or LangChain agents, Enterprises needing on-prem agent observability, AI platform teams managing cost across many models, Regulated industries (finance, healthcare) deploying agents. Right for you if your agent has more than one LLM call per session and you have already lost time staring at logs trying to reconstruct a failure. Skip if you are running single-turn chatbots where standard LLM logging is enough. Enterprise compliance (SOC-2, HIPAA, NIST AI RMF) plus on-prem deployment suggest it can land in regulated industries. Free tier exists for solo developers, but contact sales for pricing on paid plans.
What are alternatives to AgentOps?
Common alternatives to AgentOps include Orgo, Browser Use, Browserbase, Hyperbrowser, Steel, Anchor Browser.