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Telemetry & Observability

Production-ready monitoring for your AI methods.

Overview

Pipelex provides comprehensive telemetry and observability capabilities to monitor your AI methods in production. Track costs, latency, errors, and execution patterns across multiple destinations. Custom telemetry is opt-in and configurable per integration mode (CLI, pytest, API). When Pipelex Gateway is enabled as an inference backend, privacy-respecting Gateway metrics are collected automatically.

Langfuse Integration

Full LLM observability with complete span data. Track every LLM call, its inputs, outputs, tokens, cost, and latency. Supports both Langfuse Cloud and self-hosted instances via the OTLP exporter.

OpenTelemetry (OTLP)

Send execution spans to any OTLP-compatible backend for integration with your existing observability stack. Configure multiple OTLP exporters with custom endpoints and headers to fan out telemetry to different destinations.

PostHog Integration

Event tracking and AI span tracing with fine-grained privacy controls. Choose between anonymous and identified modes. Configure what data to capture: content, pipe codes, output class names, and content length limits.

Gateway Telemetry

When using Pipelex Gateway as your inference backend, privacy-respecting metrics are automatically collected: models used, token counts, latency, and error rates. This data is tied to your Gateway API key (hashed for security) and requires no additional configuration.

Privacy Controls

  • DO_NOT_TRACK — Universal telemetry disable flag, respected by all integrations
  • Configurable destinations — Choose independently what data goes to PostHog, Langfuse, OTLP, or Gateway
  • Content redaction — Control whether prompt/completion content is included in spans
  • Mode-based gating — Enable custom telemetry for CLI usage but disable it during unit tests

For configuration, see Telemetry Configuration and Telemetry Setup.