Firestore
firebase.google.com
Google's NoSQL document database with real-time sync and offline support. Scales automatically from zero to global traffic. Used for persistent agent memory in applications that need real-time state sharing across multiple users or agent instances simultaneously.
More in Memory & Knowledge Management
Unified framework for building enterprise RAG pipelines with models specifically trained for document parsing. Includes a model catalog, vector store, and prompt history management. Optimized for CPU inference on commodity hardware.
Universal data connector API for syncing documents from Google Drive, Notion, Dropbox, OneDrive, and 30+ sources into your RAG pipeline. Handles OAuth, incremental sync, and document parsing automatically. One API key to access all user data sources.
Python client for building AI applications on Redis with vector similarity search. Enables semantic caching, semantic routing, and RAG pipelines on top of Redis. Leverages Redis speed for real-time vector search at low latency.
The AI-native open-source embedding database. ChromaDB makes it effortless to store, query, and filter embedding vectors locally or in the cloud — a go-to choice for prototyping RAG applications and production deployments that don't need managed infrastructure.
The leading data framework for connecting private data sources to LLMs. Provides sophisticated indexing, chunking, retrieval, and query engine abstractions — from simple Q&A over documents to complex multi-step reasoning across heterogeneous data sources.
Vector database and data lake for AI, storing embeddings alongside original data (images, text, audio). Supports streaming data from cloud storage and has native integrations with LangChain and LlamaIndex. Open-source with a managed cloud offering.