OpenLLMetry vs Promptfoo
Side-by-side comparison of two AI agent tools
Short answer
- Promptfoo is growing faster: +1,110 GitHub stars in the last 30 days vs +80 for OpenLLMetry.
- Pick OpenLLMetry for: open-source observability for your GenAI or LLM application, based on OpenTelemetry. Pick Promptfoo for: open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps.
From GitHub data refreshed daily.
OpenLLMetryopen-source
Open-source observability for your GenAI or LLM application, based on OpenTelemetry
Promptfooopen-source
Open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps
Metrics
| OpenLLMetry | Promptfoo | |
|---|---|---|
| Stars | 7.5k | 25.7k |
| Star velocity /mo | 80.36842105263159 | 1.1k |
| Commits (90d) | 12 | 920 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | — | 3.0M |
| Overall score | 0.5912367252217405 | 0.8639349362705032 |
Pros
- +Built on OpenTelemetry standard with official semantic conventions integration, ensuring compatibility with existing observability infrastructure
- +Open-source with strong community support (6,900+ GitHub stars) and active development backed by Y Combinator
- +Multi-language support covering both Python and JavaScript/TypeScript ecosystems for broad developer adoption
- +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
- +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
- +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments
Cons
- -Requires familiarity with OpenTelemetry concepts and infrastructure setup, which may have a learning curve for teams new to observability
- -As a specialized tool for LLM observability, it may be overkill for simple AI applications or proof-of-concepts
- -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
- -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
- -Limited to evaluation and testing - does not provide actual LLM application development capabilities
Use Cases
- •Production LLM application monitoring to track performance metrics, token usage, and error rates across different models and providers
- •Debugging complex GenAI workflows by tracing requests through multiple AI services and identifying bottlenecks or failures
- •Cost optimization and performance analysis of AI applications to understand usage patterns and optimize model selection
- •Automated testing and evaluation of prompt performance across different models before production deployment
- •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
- •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture
FAQ
- Which is more popular, OpenLLMetry or Promptfoo?
- Promptfoo has more GitHub stars (25,665 vs 7,467).
- Which is more actively developed, OpenLLMetry or Promptfoo?
- Promptfoo had more commits in the last 90 days (920 vs 12).
- Should I use OpenLLMetry or Promptfoo?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.