Langfuse vs OpenHands
Side-by-side comparison of two AI agent tools
Short answer
- OpenHands is growing faster: +3,150 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 OpenHands for: openHands: AI-Driven Development.
From GitHub data refreshed daily.
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
OpenHandsfree
π OpenHands: AI-Driven Development
Metrics
| Langfuse | OpenHands | |
|---|---|---|
| Stars | 35.3k | 89.8k |
| Star velocity /mo | 1.8k | 3.2k |
| Commits (90d) | 2.0k | 668 |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | β | 17.1K |
| Overall score | 0.8971312686464765 | 0.8744622690849841 |
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
- +Multiple interface options (SDK, CLI, GUI) allowing developers to choose the best fit for their workflow and technical expertise
- +Highly scalable architecture that supports both local development and cloud deployment of thousands of agents simultaneously
- +Strong performance with 77.6 SWEBench score and active community support with nearly 70,000 GitHub stars
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
- -Complex setup process with multiple components and repositories that may overwhelm new users
- -Limited documentation clarity with information scattered across different repositories and interfaces
- -Requires significant technical knowledge to effectively configure and customize agents for specific development needs
Use Cases
- β’Production LLM application monitoring to track performance, costs, and identify issues in real-time
- β’Prompt engineering and management for teams collaborating on optimizing model prompts and tracking versions
- β’LLM evaluation and testing to measure model performance across different datasets and use cases
- β’Automating repetitive coding tasks and software development workflows across large development teams
- β’Building custom AI development assistants tailored to specific project requirements and coding standards
- β’Scaling AI-assisted development operations from individual developers to enterprise-level cloud deployments
FAQ
- Which is more popular, Langfuse or OpenHands?
- OpenHands has more GitHub stars (89,849 vs 35,329).
- Which is more actively developed, Langfuse or OpenHands?
- Langfuse had more commits in the last 90 days (2,013 vs 668).
- Should I use Langfuse or OpenHands?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.