gstack vs Langfuse
Side-by-side comparison of two AI agent tools
Short answer
- gstack is growing faster: +13,132 GitHub stars in the last 30 days vs +1,812 for Langfuse.
- Pick gstack for: use Garry Tan's exact Claude Code setup: 15 opinionated tools that serve as CEO, Designer, Eng Manager. Pick Langfuse for: open-source LLM engineering platform for observability, evaluation, prompt and dataset management.
From GitHub data refreshed daily.
gstackopen-source
Use Garry Tan's exact Claude Code setup: 15 opinionated tools that serve as CEO, Designer, Eng Manager, Release Manager, Doc Engineer, and QA
Langfuseopen-source
Open-source LLM engineering platform for observability, evaluation, prompt and dataset management
Metrics
| gstack | Langfuse | |
|---|---|---|
| Stars | 134.7k | 35.3k |
| Star velocity /mo | 13.1k | 1.8k |
| Commits (90d) | 89 | 2.0k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.7172870458637581 | 0.9067292616632036 |
Pros
- +Provides structured specialist roles instead of generic AI prompts, making interactions more focused and productive
- +Comprehensive workflow coverage from strategic planning to code review, QA testing, and deployment automation
- +Battle-tested by a high-profile user with impressive productivity claims and strong community adoption (52K+ GitHub stars)
- +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
- -Highly opinionated approach may not suit all development workflows or team preferences
- -Requires Claude Code setup and familiarity, limiting accessibility for users of other AI tools
- -May be overly complex for simple projects or developers who prefer minimal tooling
- -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
- •Technical founders who want to maintain engineering rigor while shipping code quickly as a solo developer
- •Engineering teams looking to standardize code review, QA, and release processes with AI assistance
- •Claude Code users who want specialized agent roles for different aspects of software development instead of general-purpose prompting
- •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
FAQ
- Which is more popular, gstack or Langfuse?
- gstack has more GitHub stars (134,746 vs 35,301).
- Which is more actively developed, gstack or Langfuse?
- Langfuse had more commits in the last 90 days (2,007 vs 89).
- Should I use gstack 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.