Label Studio

Open-source data curation platform for LLM fine-tuning

Argilla is an open-source platform for curating and annotating data for LLM fine-tuning and RLHF workflows. It provides collaborative annotation interfaces for text classification, ranking, and preference labeling with integrated quality metrics. Part of the Hugging Face ecosystem, Argilla supports direct dataset publishing to the Hub and integrates with major training frameworks for seamless model improvement pipelines.

Multimodal data labeling and curation for production AI

Encord is a data labeling and curation platform for teams building production AI systems with complex multimodal data. It supports image, video, audio, DICOM medical imaging, and 3D point cloud annotation with AI-assisted labeling, advanced ontology management, and quality assurance workflows. Features active learning for prioritizing high-value samples and integrates with major ML frameworks.

Data-centric AI platform for programmatic data labeling

Snorkel AI is a data-centric AI platform that enables programmatic labeling of training data through labeling functions rather than manual annotation. Spun out of Stanford AI Lab, it lets teams write Python functions that encode domain heuristics to label data at scale, with the platform combining weak labels into high-quality training sets. Used by Fortune 500 companies for text, image, and structured data labeling.

AI data runtime for multimodal datasets and vector search

Deep Lake is an open-source AI data runtime from Activeloop for storing, versioning, and querying multimodal data and embeddings. It fits teams building RAG, training, evaluation, or dataset-heavy agent workflows that need a bridge between vector search, structured metadata, and large image, text, audio, or video collections.

AI-native state store with hybrid vector and full-text search

SeekDB is an open-source AI-native state store from the OceanBase ecosystem that combines MySQL-compatible data access with hybrid vector and full-text retrieval. It targets agent and AI application teams that need embedded or server deployment, copy-on-write style sandboxes, and searchable state without gluing together several separate storage layers.

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.