AgentOps
Agent-specific monitoring with session replays, LLM cost tracking, and compliance features.
AgentOps is a developer observability platform built specifically for AI agents and LLM applications. Where general-purpose monitoring tools treat an agent as a black box, AgentOps surfaces the internal structure of agent runs — individual LLM calls, tool invocations, multi-agent interactions, and errors — giving engineers the visibility they need to debug, audit, and improve agent behavior in production.
The platform centers on session replay: every agent run is recorded and can be rewound step by step, allowing developers to inspect exactly what happened at any point in time without needing to reproduce the issue. This "time travel debugging" capability addresses one of the core frustrations in agent development, where failures are often non-deterministic and hard to reproduce locally.
AgentOps also handles cost tracking across 400+ LLMs, breaking down token usage by prompt and completion for each agent or sub-agent. This is particularly useful in multi-agent architectures where costs can accumulate across many models running in parallel or in sequence. The platform keeps price data current, so cost estimates reflect what teams are actually paying.
Integration is straightforward: a single pip install agentops and a few lines of initialization code instrument most popular frameworks. Native integrations cover OpenAI, CrewAI, AutoGen, and a wide range of other agent frameworks, meaning developers rarely need to write custom instrumentation. The SDK is designed to be framework-agnostic, so it works across heterogeneous stacks.
Compared to general APM tools like Datadog or New Relic, AgentOps is narrowly focused on the agent execution model — sessions, tool calls, LLM spans — rather than infrastructure metrics. Compared to LLM-specific tools like LangSmith or Langfuse, AgentOps emphasizes the multi-agent session as the primary unit of observability, with session replay as a first-class feature rather than an afterthought.
The platform is used by engineering teams at organizations including Microsoft, Google, Meta, Deloitte, Fidelity, and Samsung, suggesting it handles enterprise-scale workloads. Enterprise tiers add SOC-2, HIPAA, and NIST AI RMF compliance, custom SSO, on-premise deployment options (AWS, GCP, Azure), and Slack Connect support.
For teams building on fine-tuned models, AgentOps saves LLM completions and offers fine-tuning workflows on that captured data, reportedly at significantly lower cost than training from scratch. This closes the loop between observability and model improvement.
AgentOps is backed by a $2.6M raise and has over 4,000 GitHub stars, indicating meaningful traction in the developer community. The free tier supports up to 5,000 events per month, making it accessible for teams in early development before committing to a paid plan.
Key Features
- Session replay with point-in-time rewinding for debugging agent runs without reproduction
- Visual tracing of LLM calls, tool invocations, errors, and multi-agent interactions
- Cost and token tracking across 400+ LLMs, broken down by prompt and completion per agent
- Native SDK integrations with OpenAI, CrewAI, AutoGen, and hundreds of other frameworks
- Full audit trail including logs, errors, and prompt injection attack detection
- Fine-tuning support using saved completions from production agent runs
- Role-based permissioning, SSO, and on-premise deployment for enterprise teams
- Compliance certifications: SOC-2, HIPAA, NIST AI RMF (Enterprise tier)
Pros & Cons
Pros
- Session replay is a genuinely differentiated capability for debugging non-deterministic agent failures
- Broad framework coverage (400+ LLMs, major agent frameworks) minimizes custom instrumentation work
- Free tier with 5,000 events per month lets teams evaluate before paying
- Cost tracking per agent and per model helps manage spend in complex multi-agent architectures
- Enterprise compliance options (SOC-2, HIPAA, on-premise) make it viable for regulated industries
Cons
- Focused exclusively on agent observability — not a replacement for infrastructure or application-level monitoring
- Pro plan starts at $40/month on a pay-as-you-go model, which can scale unpredictably under high event volumes
- Enterprise pricing is custom and requires a sales conversation, making it hard to budget in advance
- Younger platform compared to established APM tools, with a smaller ecosystem of community resources
Pricing
AgentOps offers a free Basic plan covering up to 5,000 events per month with core features including the agent-agnostic SDK, LLM cost tracking, and replay analytics. The Pro plan starts at $40 per month on a pay-as-you-go basis and adds unlimited events, unlimited log retention, session export, and dedicated support. Enterprise pricing is custom and includes SLA guarantees, Slack Connect, custom SSO, on-premise deployment, and compliance certifications.
Who Is This For?
AgentOps is best suited for software engineers and AI teams building production-grade AI agents who need detailed visibility into agent behavior beyond simple logging. It is particularly well-matched for teams running multi-agent systems across multiple LLMs who need to track costs per agent, debug non-deterministic failures, and maintain an audit trail for compliance or enterprise security requirements.
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