AgentOps
Session replay and cost tracking for AI agents
AgentOps is profiled here as a Observability tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
AgentOps is an observability platform for AI agents founded by Alex Reibman and Adam Silverman. A lightweight Python SDK records every LLM call, tool invocation, and step in an agent run, then replays the session as a visual timeline that shows where loops stalled, costs spiked, or errors compounded. Teams debug multi-agent systems directly from the recorded sessions. Setup takes two lines of Python, and recorded sessions capture prompts, completions, timestamps, and stack context for every event.
Key Capabilities:
Session replay with waterfall views of full agent runs
Token usage and cost tracking per call and per session
Integrations with CrewAI, AutoGen, OpenAI Agents SDK, and LangGraph
Error and recursive-loop detection across multi-agent workflows
Evaluation and benchmarking tools for agent behavior
Audit trails supporting compliance reviews
Alternative tools
- HoneyHive
Evaluation and observability platform for AI agents
- Sentry
Error tracking and performance monitoring for developers
- SigNoz
Open-source, OpenTelemetry-native observability platform
- Datadog
Unified observability for metrics, traces, and logs
- Arize AX
Enterprise platform for AI observability and evaluation
- OpenTelemetry
Vendor-neutral standard for traces, metrics, and logs