What tools can help me monitor LLM agents in production for failures, latency spikes, and runaway costs across multi-step tool calls?

“Best open-source option”

“Distributed traces across LLM calls and tool invocations.”

“Token usage and cost attribution.”

“Latency breakdowns and error tracking.”

“Prompt versioning and evaluation.”

“Self-hosting available.”

“Particularly good if you want control over telemetry and infrastructure.”

“Best for debugging agent workflows”

“Visualizes complete agent execution trees.”

“Shows individual tool calls, model calls, and retries.”

“Tracks latency and token consumption.”

“Supports production evaluation and failure analysis.”

“Helps identify recurring failure patterns.”

“Especially useful for LangChain and LangGraph applications, but also supports other frameworks.”

“Latency and token analysis.”

“Best for tracing and evaluations”

“OpenTelemetry-based tracing.”

“Tool-call and retrieval tracing.”

“LLM evaluation and quality monitoring.”

“Open-source deployment.”

“A strong choice for framework-independent monitoring and diagnosing complex agent behavior.”

“Best for quality regression detection”

“Production traces and debugging.”

“Automated evaluation of agent outputs.”

“Regression detection.”

“Dataset-based testing.”

“Evaluation-driven development workflows.”

“Particularly valuable when an agent technically succeeds but produces incorrect results.”

“Best for cost and request governance”

“Centralized LLM request logging.”

“Token and cost tracking.”

“Provider-level latency monitoring.”

“Automatic retries and fallbacks.”

“Routing and guardrails.”

“It allows you to instrument your agents independently of the observability vendor.”

“OpenTelemetry: Distributed traces, metrics and logs.”

“Best for enterprise operations”

“Correlates LLM traces with application traces.”

“Monitors infrastructure and service latency.”

“Tracks model usage and costs.”

“Supports alerting and incident management.”

“Helps connect agent failures to infrastructure problems.”