Which is more popular, Auto-evaluator or Langfuse?

Langfuse has more GitHub stars (35,301 vs 1,104).

Which is more actively developed, Auto-evaluator or Langfuse?

Langfuse had more commits in the last 90 days (2,007 vs 0).

Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.

Side-by-side comparison of two AI agent tools

Auto-evaluator has had no commit in 41 months; Langfuse is actively maintained (2,007 commits in the last 90 days).
Langfuse is growing faster: +1,812 GitHub stars in the last 30 days vs +51 for Auto-evaluator.
Pick Auto-evaluator for: evaluation tool for LLM QA chains. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.

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Evaluation tool for LLM QA chains

Langfuseopen-source

Open-source LLM engineering platform for observability, evaluation, prompt and dataset management

+Fully automated evaluation pipeline that generates question-answer pairs from documents without manual dataset creation
+Comprehensive configuration testing across multiple parameters including chunk sizes, retrieval methods, and embedding approaches
+User-friendly Streamlit interface with hosted versions available on HuggingFace and langchain.com for easy access

+Open source with MIT license allowing full customization and transparency, plus active community support
+Comprehensive feature set combining observability, prompt management, evaluations, and datasets in one platform
+Extensive integrations with major LLM frameworks and tools including OpenTelemetry, LangChain, and OpenAI SDK

-Requires paid API access to both OpenAI (GPT-4) and Anthropic services for full functionality
-Limited to GPT-3.5-turbo for both question generation and response scoring, which may introduce model-specific biases
-Evaluation quality depends on the automatic question generation, which may not capture all important aspects of document content

•Optimizing RAG system parameters by testing different chunk sizes, overlap settings, and retrieval strategies on domain-specific documents
•Benchmarking multiple embedding methods and language models to find the best combination for specific document types and query patterns
•Conducting systematic performance comparisons when migrating between different QA architectures or upgrading model versions

•Production LLM application monitoring to track performance, costs, and identify issues in real-time
•Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
•LLM evaluation and testing to measure model performance across different datasets and use cases

Which is more popular, Auto-evaluator or Langfuse?: Langfuse has more GitHub stars (35,301 vs 1,104).
Which is more actively developed, Auto-evaluator or Langfuse?: Langfuse had more commits in the last 90 days (2,007 vs 0).
Should I use Auto-evaluator or Langfuse?: Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.