LlamaIndex

LangChain

Build context-aware reasoning applications

The most popular framework for developing applications powered by large language models, including agents and RAG.

Ragas

Supervised & reference-free evaluation framework for RAG pipelines

Ragas (Retrieval Augmented Generation Assessment) is the industry-standard evaluation framework designed specifically to measure the performance of RAG pipelines without requiring human-annotated ground truth datasets. Ragas evaluates RAG systems across critical dimensions: Faithfulness (hallucination detection), Answer Relevance (query alignment), Context Precision (signal-to-noise ratio in retrieved chunks), and Context Recall (measuring whether all necessary information was retrieved).

DSPy

Stanford’s Framework for Programmatically Optimizing LM Prompts & Weights

DSPy is an open-source framework created by Stanford University that replaces fragile, manual prompt engineering with algorithmic programming and systematic optimization. Instead of hand-tweaking prompt strings and few-shot examples, DSPy allows developers to express multi-stage AI workflows as modular Python modules with declarative Signatures. DSPy’s teleprompter optimizers automatically synthesize optimal prompt instructions, select high-performing few-shot demonstrations, and fine-tune smaller local language model weights to maximize pipeline accuracy on defined validation metrics.

Haystack

Deepset’s Open-Source Modular Framework for Production RAG & Search

Haystack is an open-source NLP and generative AI framework by Deepset, designed for building production-grade Retrieval-Augmented Generation (RAG), neural search, and multi-modal question-answering systems. Built with modular component architecture, Haystack allows engineering teams to assemble and customize search pipelines using diverse document stores, embedding models, and LLMs. With enterprise features like hybrid search (dense vector + sparse BM25 retrieval), advanced cross-encoder re-ranking, and structured pipeline validation, Haystack powers mission-critical search engines at global enterprises.

RunPod

Globally distributed GPU cloud and serverless platform for AI inference and training

RunPod is a leading globally distributed GPU cloud and serverless computing platform engineered specifically for artificial intelligence workloads. It provides developers, AI researchers, and enterprises with on-demand access to top-tier NVIDIA GPUs (including H100, A100, L40S, and RTX 4090) at up to 80% lower cost than traditional legacy hyperscalers.

Qdrant

High-performance vector database and similarity search engine for AI

Qdrant is an open-source, high-performance vector database and similarity search engine engineered in Rust for production AI systems, semantic search engines, and Retrieval-Augmented Generation (RAG) pipelines. It provides lightning-fast nearest-neighbor search with rich payload filtering and custom distance metrics. Unlike traditional databases adapted for vectors, Qdrant was designed from day one to handle high-dimensional neural embeddings at scale. Its Rust engine provides memory-efficient vector quantization (scalar, product, and binary), allowing engineering teams to search billions of vectors on cost-effective cloud hardware.