Langfuse vs Promptfoo
Side-by-side comparison of two AI agent tools
Short answer
- Langfuse is growing faster: +1,812 GitHub stars in the last 30 days vs +1,112 for Promptfoo.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. 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.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Promptfooopen-source
Open-source CLI and library for evaluating and red-teaming prompts, agents, RAG systems, and LLM apps
Metrics
| Langfuse | Promptfoo | |
|---|---|---|
| Stars | 35.3k | 25.6k |
| Star velocity /mo | 1.8k | 1.1k |
| Commits (90d) | 2.0k | 913 |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9067292616632036 | 0.8742860723201702 |
Pros
- +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
- +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
- -May require significant setup and configuration for self-hosted deployments
- -Could be overwhelming for simple use cases that only need basic LLM monitoring
- -Self-hosting requires technical expertise and infrastructure resources
- -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, 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
- •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, Langfuse or Promptfoo?
- Langfuse has more GitHub stars (35,301 vs 25,640).
- Which is more actively developed, Langfuse or Promptfoo?
- Langfuse had more commits in the last 90 days (2,007 vs 913).
- Should I use Langfuse 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.