Muscle Mode

10 GitHub repos every AI developer should know before putting an AI app into production. Building an AI application is one thing. Making sure it stays reliable, accurate, and cost-efficient is another. These open-source tools help you understand what's happening behind the scenes: 1. Langfuse Track LLM calls, prompts, performance, and costs. github.com/langfuse/langfuse 2. Arize Phoenix Trace AI workflows and debug retrieval problems. github.com/Arize-ai/phoenix 3. Promptfoo Test prompts, compare model outputs, and catch regressions. github.com/promptfoo/promptf… 4. DeepEval Evaluate LLM responses, hallucinations, and RAG performance. github.com/confident-ai/deep… 5. TruLens Check whether your RAG application retrieves relevant information and generates grounded answers. github.com/truera/trulens 6. LiteLLM Connect multiple LLM providers through one API with routing and spend tracking. github.com/BerriAI/litellm 7. Helicone Monitor LLM requests, latency, usage, and spending. github.com/Helicone/helicone 8. MLflow Track experiments and evaluate machine learning and LLM applications. github.com/mlflow/mlflow 9. OpenLIT Monitor LLM traces, metrics, and GPU usage with OpenTelemetry. github.com/openlit/openlit 10. Oxylabs Web API MCP Bring live web search and extracted data into AI workflows. github.com/oxylabs/web-api-m… A working AI app is just the beginning. You also need to know when it gives poor answers, where it spends money, and what needs fixing. That's what separates a quick AI prototype from a reliable product. Save these repos before your next AI deployment. 🔖