RunAnywhere SDK

Run LLMs locally with one command

Tool for running large language models locally on your machine with a simple CLI interface. Download and run Llama 3, Mistral, Gemma, Phi, Code Llama, and dozens of other open-source models with a single command. Features model management, GPU acceleration (NVIDIA/AMD/Apple Silicon), OpenAI-compatible API server, Modelfile for customization, and multi-model switching. Ideal for offline AI development, privacy-sensitive use cases, and local testing. 120K+ GitHub stars.

High-performance local LLM inference in C/C++

llama.cpp is the foundational C/C++ library with 75K+ GitHub stars powering local LLM inference on consumer hardware. Provides optimized CPU and GPU inference for quantized models in GGUF format. Supports LLaMA, Mistral, Phi, Gemma, and most open-weight families. Features 2-8 bit quantization for reduced memory, multi-GPU support, context extension, grammar-constrained output, and an OpenAI-compatible API server. The engine behind Ollama and LM Studio.

High-throughput LLM serving engine

vLLM is an open-source LLM serving engine with 50K+ GitHub stars achieving 14-24x higher throughput than HuggingFace Transformers through PagedAttention memory management. Serves LLaMA, Mistral, Qwen, and 100+ architectures with continuous batching, tensor parallelism for multi-GPU, and prefix caching. Provides an OpenAI-compatible API server for drop-in replacement. Used in production by major AI companies for serving models at scale with optimal GPU utilization.

Cross-platform on-device AI model runtime

Nexa SDK enables running frontier LLMs and multimodal models locally across PC, mobile, IoT, and wearables with automatic hardware acceleration for GPU, NPU, and CPU. It supports Qwen, Gemma, Llama, DeepSeek models with Python/C++ desktop SDKs, Android/iOS mobile SDKs, and Docker for edge deployment. Includes an OpenAI-compatible API server with chat and function calling support.

Embedding-first search and discovery engine for AI-powered product experiences.

Marqo is an open-source tensor search engine that combines embedding generation and vector search in a single API, removing the need to manage separate embedding pipelines and vector databases. Built for product discovery and multi-modal search, it lets teams index text, images, and structured data together, returning ranked results based on semantic similarity rather than keyword overlap.

AI-powered file-type detection at Google scale

Open-source AI-powered file-type detection tool from Google that uses a custom deep-learning model under a few megabytes to identify more than 200 binary and textual content types in milliseconds, even on a single CPU. Magika ships as a CLI, Python package, JavaScript/TypeScript library, and an ONNX model, achieves around 99% accuracy on its test set, and is already used at Google scale across Gmail, Drive, and Safe Browsing as well as by VirusTotal and abuse.ch.

Context engineering platform for AI agents with temporal knowledge graphs

Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

ML experiment tracking and model monitoring

Weights and Biases is the AI developer platform for experiment tracking, model monitoring, and ML workflow orchestration. Weave extends W&B with LLM ops capabilities for prompt engineering, evaluation, and deployment. Enables teams to track experiments, monitor model performance in production, manage datasets, log LLM application traces, and collaborate on ML projects with visualization dashboards, automated logging, and enterprise SSO and RBAC compliance.

Data factory for AI teams and model training

Labelbox is a comprehensive data platform for AI teams handling reinforcement learning, evaluations, robotics, and human feedback workflows. Core capabilities include RL data generation with knowledge work rubrics, custom evaluations for private benchmarks and model comparisons, robotics data with full-stack video and trajectories, and an expert network of 1.5M+ knowledge workers including 50K+ PhDs. Trusted by 80% of leading AI labs for production data operations.