Langfuse
LLM observability: traces, evals, and why your agent went rogue.
Best use cases
• Trace LLM calls
• Evaluate outputs
• Debug agents
• Monitor costs
• A/B test prompts
Pros
• Great visibility
• Self-hostable option
• Team workflows
• Makes issues reproducible
Cons
• Setup overhead
• Evals still require good design
• Learning curve for teams new to observability
Pricing
freemium
Free tier + paid cloud; self-host option
Appears in collections
Developer AI Stack (Lite, Practical)
A focused set of tools and prompts that reduce time-to-fix and time-to-ship, without turning your brain into a checklist.
Debugging Workflow (Systematic, Not Random)
Stop guessing. Find the bug, fix it properly, and prevent it from coming back.
Performance Optimization Workflow (Measure, Fix, Verify)
Make things faster through data, not guessing. Profile, optimize, benchmark.
Related tools
LangSmith
Debug, evaluate, and monitor LLM apps built with LangChain.
Helicone
Open-source observability layer for LLM API calls.
PromptLayer
Track, version, and debug prompts across LLM applications.
Cursor
AI-first editor that makes refactors feel less like punishment.
VS Code + GitHub Copilot
Most flexible dev setup, if you don't install 47 extensions you'll regret.
JetBrains AI
Convenient AI inside IntelliJ/WebStorm for people already deep in JetBrains.