Langfuse vs private-gpt
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 +57 for private-gpt.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick private-gpt for: interact with your documents using the power of GPT, 100% privately, no data leaks.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
private-gptopen-source
Interact with your documents using the power of GPT, 100% privately, no data leaks
Metrics
| Langfuse | private-gpt | |
|---|---|---|
| Stars | 35.3k | 57.6k |
| Star velocity /mo | 1.8k | 56.82539682539682 |
| Commits (90d) | 2.0k | 62 |
| Releases (6m) | 10 | 4 |
| Overall score | 0.9067292616632036 | 0.5500380972578883 |
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
- +Complete data privacy with 100% local processing and no external data transmission
- +Production-ready with comprehensive API following OpenAI standards and streaming support
- +Flexible architecture offering both high-level RAG pipeline and low-level API for custom implementations
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 significant local compute resources to run LLMs effectively
- -Setup complexity may be challenging for non-technical users
- -Limited to documents that can be processed and stored locally
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
- •Enterprise document analysis for regulated industries requiring complete data privacy
- •Offline research and document querying in environments without internet connectivity
- •Building custom AI applications with contextual document understanding without cloud dependencies
FAQ
- Which is more popular, Langfuse or private-gpt?
- private-gpt has more GitHub stars (57,562 vs 35,301).
- Which is more actively developed, Langfuse or private-gpt?
- Langfuse had more commits in the last 90 days (2,007 vs 62).
- Should I use Langfuse or private-gpt?
- 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.