Search AI & ML Showcase

Programming framework for algorithmically optimizing LM prompts

DSPy is a Stanford research framework that replaces manual prompt engineering with programmatic optimization. It treats prompts and LM calls as declarative modules and automatically optimizes them using optimizers like BootstrapFewShot and MIPRO.

Graph-based RAG that extracts knowledge graphs for better retrieval

GraphRAG is an open-source framework from Microsoft Research that uses LLMs to extract entities and relationships into a knowledge graph, enabling global reasoning over large document corpora that traditional RAG cannot handle.

The most widely adopted framework for building LLM-powered applications

LangChain is an open-source framework for building applications powered by large language models. It provides composable building blocks for chains, agents, memory, RAG pipelines, and tool integrations across 50+ LLM providers.

Automated evaluation framework for RAG pipelines

RAGAS is an open-source framework for evaluating Retrieval-Augmented Generation (RAG) pipelines. It provides reference-free metrics like faithfulness, answer relevance, and context precision using LLM-as-judge methods.

Open-source LLM observability, tracing, and evaluation platform

Langfuse is an open-source platform for LLM application observability. It provides traces, spans, scores, and evals for debugging and monitoring LLM pipelines, with SDKs for Python and JavaScript and integrations with LangChain, LlamaIndex, and more.

Open-source AI observability and evaluation for LLMs and ML models

Phoenix by Arize is an open-source AI observability platform that provides real-time tracing, evaluation datasets, and retrieval analysis for LLM and ML applications. Runs locally or on cloud infrastructure.

Git for data — version control for ML data, models, and pipelines

DVC (Data Version Control) is an open-source ML experiment and data versioning tool built on top of Git. It enables reproducible ML pipelines, tracks datasets and models alongside code, and integrates with all major cloud storage providers.

Modern workflow orchestration for data and ML pipelines

Prefect is an open-source workflow orchestration platform for data engineering and ML pipelines. It provides observability, scheduling, and retry logic for Python workflows with a hybrid execution model.

Open-source MLOps platform for the full ML lifecycle

MLflow is an open-source platform for managing the complete machine learning lifecycle including experiment tracking, model packaging, model registry, and deployment. Widely adopted across data science and ML engineering teams.

OpenTelemetry-compatible tracing standard for AI/LLM applications

OpenInference is an open standard for capturing AI model observability data using OpenTelemetry. It enables vendor-neutral tracing of LLM calls, embeddings, and retrieval operations across any observability backend.

High-performance vector database with rich filtering for production AI

Qdrant is an open-source vector similarity search engine built in Rust. It supports dense and sparse vectors, rich payload filtering, named vectors, and advanced quantization — designed for high-throughput production AI workloads.

Streamlined fine-tuning for LLMs with LoRA, QLoRA, and full fine-tune

Axolotl is an open-source fine-tuning framework that simplifies training LLMs with LoRA, QLoRA, FSDP, and DeepSpeed. It supports a wide range of base models and dataset formats with a single YAML config.