Transformershuggingface/transformers
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
58 libraries, 48 updated this year
Tools for applications built on large language models. Hugging Face Transformers loads and fine-tunes open models, Haystack and txtai assemble retrieval-augmented generation pipelines, and vector databases such as Milvus, Qdrant, Chroma and Weaviate store the embeddings those pipelines search.
58 libraries
A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
Milvus is open source vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
Qdrant is open source vector similarity search engine with extended filtering support, written in Rust.
Open-source search and retrieval database for AI applications. Vector, full-text, regex, and metadata search. Self-host or Cloud available.
Platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
A framework for building industrial-strength applications with Transformer models and LLMs.
Open-source LLM evaluation and red teaming framework. Test prompts, models, agents, and RAG pipelines. Run adversarial attacks (jailbreaks, prompt injection) and integrate security testing into CI/CD.
Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. (Source Code)
Track, log, visualize and evaluate your LLM prompts and prompt chains.
Weaviate is an open source vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
Open-source SDK for running LLMs and multimodal models on-device across iOS, Android, and cross-platform apps.
> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and Outlier Ensembles.
Production-ready Multi-AI Agents framework with self-reflection. Fastest agent instantiation (3.77μs), 100+ LLM support via LiteLLM, MCP integration, agentic workflows (route/parallel/loop/repeat), built-in memory, Python & JS SDKs.
Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit activeloop.ai for more info.
Toolkit for package and deploy machine learning models for serving in production
Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware.
Interactive reports to analyze machine learning models during validation or production monitoring.
Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution.
Python-free Rust inference server for NLP models with OpenAI API compatibility and hot model swapping.
The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
Open source Kubernetes-style control plane for deploying AI agents as distributed microservices, with built-in service discovery, durable workflows, and observability.
In-context learning framework that allows agents to learn from execution feedback.
Open-source, self-hostable platform to evaluate, trace, and guard LLM applications and AI agents.
Universal memory service with semantic search, autonomous consolidation, and multi-client support for AI applications.
An Easy-to-Use and High-Performance AI deployment framework.
The simplest way to run an LLM locally. Supports tool calling and grammar constrained sampling.
Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. Supports LangGraph, CrewAI, n8n, OpenAI Agents, and more.
Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure.
Is an open source on-prem AI coding autonomous assistant that lives inside your repo, edits and tests files at SSD speed. Think Claude Code but with UI. plug in any LLM (OpenAI, Gemini, Ollama, etc.) and let it work for you.
Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features.
Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution.
Ignite your models into blazing-fast machine learning APIs with a modern framework.
Visual AI agent workflow automation platform with local LLM integration. Build intelligent workflows using drag-and-drop, no cloud required.
Minimal, hackable PyTorch/Hugging Face codebase for training LLM-based speech recognition by connecting a frozen pretrained speech encoder to a pretrained LLM through a small trained projector, with word timestamps, speaker diarization and a free course.
Enterprise-grade LLM integration framework for building production-ready AI applications with built-in hallucination prevention, RAG, and MCP support.
A simple Python framework for building ML/DL workflows and LLM agents faster, with prebuilt trainers, agent templates, logging, configuration management, and much more.
Blazing fast, lightweight and customizable fuzzy and semantic text search in Python with fuzzywuzzy/thefuzz compatible API.
Godot 4.x asset that enables NPCs to interact with players using local LLMs for structured, offline-first learning conversations in games.
Open-source runtime security scanner for AI agents. Detects prompt injection, jailbreak, PII leakage, memory poisoning, and tool misuse. Zero deps, MIT licensed.
Agentic AI operating system (h9y.ai) that replaces brittle/fragmented automations with long-lived, self-improving systems. Open-source, self-hosted/cloud, visual workflow, omni-channel, decentralized, extensible.
Neural networks framework in pure C: training and inference, no dependencies.
A python package that integrates an LLM copilot inside the keras model development workflow.
A native C CPU inference engine for running the 284B-A13B DeepSeek-V4-Flash-0731 on one laptop, reaching up to 1.12 token/s on tested hardware while streaming the 167 GB checkpoint from disk with a tested 8 GB memory path and no GPU or Python.
Pure-Rust tokenizer for GGUF models, compatible with llama.cpp tokenization.
Drop-in AsyncOpenAI replacement that transparently batches requests via the Batch API for cheaper LLM inference.
A native C inference engine for running Qwen3.8-27B locally on a single laptop CPU, with direct GGUF loading and a tested 8 GB memory path.