PacktPublishing/LLM-Engineers-Handbook

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

awsfine-tuning-llmgenaillmllm-evaluationllmopsml-system-designmlopsrag

Licencia y mantenimiento

MIT es una licencia permisiva: uso comercial libre con atribución.

Último cambio de código hace 6 meses.

	Repositorio	Estrellas	Lenguaje	Licencia	Actividad
1	HKUDS/LightRAG[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation	★ 40.2k	Python	MIT	Activo
2	langfuse/langfuse🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.	★ 36k	TypeScript	Other	Activo
3	promptfoo/promptfooTest your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, DeepS	★ 26.1k	TypeScript	MIT	Activo
4	comet-ml/opikDebug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, an	★ 22.7k	Python	Apache-2.0	Activo
5	mem0ai/mem0The Memory Layer for AI Agents - Drop-in memory infrastructure for AI agents and apps. Context that persists. Built for production.	★ 67.4k	Python	Apache-2.0	Activo
6	BerriAI/litellmThe fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, l	★ 61.5k	Python	Other	Activo
7	run-llama/llama_indexLlamaIndex is the document processing platform for AI	★ 52.6k	Python	MIT	Activo
8	VectifyAI/PageIndex📑 PageIndex: Document Index for Vectorless, Reasoning-based RAG	★ 39.3k	Python	MIT	Activo