Langfuse vs Superagent
Side-by-side comparison of two AI agent tools
Short answer
- Langfuse is growing faster: +1,807 GitHub stars in the last 30 days vs +41 for Superagent.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick Superagent for: superagent protects your AI applications against prompt injections, data leaks, and harmful outputs.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Superagentopen-source
Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.
Metrics
| Langfuse | Superagent | |
|---|---|---|
| Stars | 35.3k | 6.8k |
| Star velocity /mo | 1.8k | 41.36842105263158 |
| Commits (90d) | 2.0k | 8 |
| Releases (6m) | 10 | 0 |
| Downloads (30d, npm + PyPI) | 22.4M | — |
| Overall score | 0.8971312686464765 | 0.35990158642254044 |
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 AI security coverage with multiple protection layers including prompt injection detection, PII redaction, and repository scanning
- +Production-ready SDK with dual language support (TypeScript and Python) and straightforward API integration
- +Open-source with strong community backing (6,500+ GitHub stars) and Y Combinator validation
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 key and external service dependency, potentially adding latency to AI application workflows
- -Red team testing feature is still in development (marked as 'coming soon')
- -May introduce additional complexity and cost considerations for high-volume AI applications
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
- •Protecting customer-facing chatbots from prompt injection attacks that could expose system prompts or cause harmful outputs
- •Sanitizing AI-processed documents and conversations to automatically redact sensitive information like SSNs, emails, and medical data for compliance
- •Securing AI development pipelines by scanning code repositories for malicious instructions or AI agent poisoning attempts
FAQ
- Which is more popular, Langfuse or Superagent?
- Langfuse has more GitHub stars (35,329 vs 6,762).
- Which is more actively developed, Langfuse or Superagent?
- Langfuse had more commits in the last 90 days (2,013 vs 8).
- Should I use Langfuse or Superagent?
- 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.