helicone vs Langfuse

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 +132 for helicone.
  • Pick helicone for: open source LLM observability platform. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.

From GitHub data refreshed daily.

heliconeopen-source

🧊 Open source LLM observability platform. One line of code to monitor, evaluate, and experiment. YC W23 🍓

Langfuseopen-source

Open-source LLM engineering platform for observability, evaluation, prompt and dataset management

Metrics

heliconeLangfuse
Stars6.2k35.3k
Star velocity /mo132.47368421052631.8k
Commits (90d)102.0k
Releases (6m)010
Downloads (30d, npm + PyPI)1.3K—
Overall score0.45266826426174790.8971312686464765

Pros

  • +一行代码集成多个主流 AI 服务商，支持 OpenAI、Anthropic、Gemini 等
  • +完整的可观测性套件，包含请求追踪、成本监控、延迟分析和质量评估
  • +开源架构提供完全的数据控制权和自定义能力，无厂商锁定风险
  • +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

Cons

  • -相对较新的项目，生态系统和第三方集成可能不如成熟的商业解决方案完善
  • -自部署需要一定的运维成本和技术能力
  • -大规模使用时可能需要额外的性能优化和资源配置
  • -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

Use Cases

  • •AI Agent 系统的全链路监控和调试，追踪多步骤推理过程和工具调用
  • •生产环境中的 LLM 成本控制和性能优化，实时监控 API 使用情况
  • •多模型 A/B 测试和提示工程，比较不同模型和提示版本的效果
  • •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
View helicone DetailsView Langfuse Details

FAQ

Which is more popular, helicone or Langfuse?
Langfuse has more GitHub stars (35,329 vs 6,196).
Which is more actively developed, helicone or Langfuse?
Langfuse had more commits in the last 90 days (2,013 vs 10).
Should I use helicone or Langfuse?
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.