Regtrace

What it does

Open-source CLI that catches LLM quality drift before users do. Standalone binary, no Python, Node, or Docker. Every run compared against baseline automatically. Four metric pillars: factuality, format, tone, regression. NFR gates on latency/cost/coverage. Pluggable providers: Anthropic, OpenAI, Groq, Gemini, Ollama. CI-native with exit codes. MIT license, zero telemetry.

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  Langfuse2023 · langfuse.com · ▲771

  Open source tracing and analytics for LLM applications

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  LangWatch2024 · langwatch.ai · ▲669

  Understand, measure and improve your LLMs

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  OpenLIT's Zero-code LLM ObservabilityOct 2025 · ▲136

  Trace LLM requests + costs with OpenTelemetry monitoring

• OO
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  Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…

• AT
  A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)2025 · llmapitest.com · ▲55

  I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

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  Langtrace AI2024 · ▲119

  Improve your LLM apps with open-source observability tool

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• H6
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