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Laminar vs Langtrace

LaminarLangtrace

Bottom line: Laminar for agent developers; Langtrace for lLM app developers.

LaminarLaminar

Open-source, OpenTelemetry-native observability and evals built for AI agents

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LangtraceLangtrace

Open-source, OpenTelemetry-based observability for LLM applications

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Votes00
PricingFreemiumFreemium
CategoryLlm ObservabilityLlm Observability
Tags
llm-observabilityagent-tracingopentelemetryevalsopen-source
llm-observabilityopentelemetrytracingevaluationsopen-source
Best for
  • Agent developers
  • LLM app engineers
  • Teams needing evals plus tracing
  • LLM app developers
  • RAG engineers
  • Platform teams
Pros
  • Open source under Apache 2.0
  • OpenTelemetry-native, one-line tracing
  • Fast Rust implementation
  • Plain-English Signals for agent behaviors
  • Built-in evals SDK and CLI
  • Fully open source and self-hostable
  • Built on the OpenTelemetry standard
  • Traces export to any observability stack
  • Supports Python and TypeScript
  • Covers models, frameworks, and vector DBs
Cons
  • Younger and smaller than major competitors
  • Ecosystem and integrations still growing
  • Free tier has short retention
  • Best suited to agent-heavy use cases
  • Requires OpenTelemetry familiarity for advanced use
  • Smaller ecosystem than larger competitors
  • Fewer turnkey enterprise features
  • Evaluations can require manual effort
  • Requires OpenTelemetry familiarity for advanced setups
  • Managed cloud is less mature than incumbents

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