Langfuse vs MinerU
Side-by-side comparison of two AI agent tools
Short answer
- MinerU is growing faster: +3,732 GitHub stars in the last 30 days vs +1,807 for Langfuse.
- Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management. Pick MinerU for: transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
MinerUfree
Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.
Metrics
| Langfuse | MinerU | |
|---|---|---|
| Stars | 35.3k | 81.0k |
| Star velocity /mo | 1.8k | 3.7k |
| Commits (90d) | 2.0k | 905 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | 22.4M | 96.9K |
| Overall score | 0.8971312686464765 | 0.8796799796634358 |
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
- +专门针对 LLM 优化的输出格式,确保转换后的 Markdown/JSON 能够被 AI 模型高质量理解和处理
- +支持复杂 PDF 文档的结构化解析,保持表格、图像和文本布局的完整性
- +提供 Python SDK 和 Web 应用双重接口,既适合程序化集成也支持交互式使用
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
- -主要专注于 PDF 处理,对其他文档格式的支持可能有限
- -复杂文档的处理质量可能依赖于原始文档的质量和结构清晰度
- -大规模批量处理时可能需要考虑计算资源和处理时间的平衡
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
- •构建 RAG(检索增强生成)系统时,将企业内部 PDF 文档转换为向量数据库可索引的格式
- •为 AI 代理开发智能文档分析功能,自动提取和结构化合同、报告中的关键信息
- •建立知识管理系统,将历史文档资料转换为可搜索和可查询的结构化数据
FAQ
- Which is more popular, Langfuse or MinerU?
- MinerU has more GitHub stars (81,025 vs 35,329).
- Which is more actively developed, Langfuse or MinerU?
- Langfuse had more commits in the last 90 days (2,013 vs 905).
- Should I use Langfuse or MinerU?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.