AI Observability & MLOps

AgentOps

Observability and monitoring for AI agents — trace runs, measure costs, and debug multi-agent systems.

Free
Pricing Tier
Medium
Learning Curve
1–2 days for SDK instrumentation
Implementation
small, medium, large
Best For
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Use when

Engineering teams running production agent systems that need debugging, cost control, and reliability analysis beyond generic LLM logs.

Avoid when

Teams running simple prompt-response LLM apps — LangSmith or Langfuse are better for non-agent workflows.

What is AgentOps?

AgentOps is purpose-built observability for agent-based AI systems. It captures traces across agent runs, tracks token usage and latency per step, and surfaces failure modes that generic LLM observability tools miss (infinite loops, tool-use failures, planning errors). Integrates with CrewAI, LangChain, AutoGen, and custom frameworks via SDK.

Key features

Session replay for agent runs
Token and cost tracking per step
Failure mode detection (loops, stuck)
Tool-use and planning visualization
Multi-framework SDK support

Integrations

CrewAILangChainAutoGenOpenAI
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