Exabase

Teams building AI agents and copilots need their agents to remember, search, and retrieve information across sessions, but doing this today means stitching together a vector database, file storage like S3, an embedding pipeline, chunking logic, citation handling, and a cron scheduler for upkeep. Agents that forget everything between sessions cannot build the kind of trust needed for customer support bots, sales copilots, or coding assistants that must track context over time. Teams also end up burning excess tokens because their agents lack precise, well-organized context, and knowledge bases go stale without manual maintenance. Startups, SaaS companies adding AI features, and enterprise teams replacing brittle RAG pipelines all face this same fragmented, do-it-yourself infrastructure problem.

Exabase is a data layer for AI agents delivered through APIs and a TypeScript SDK, combining memory, file storage, search, and extraction into one platform. Its Memory system automatically extracts facts, preferences, and events from conversations and organizes them into a self-improving knowledge graph searchable via semantic search, using two API calls: one to add and one to retrieve. Bases provide isolated, per-tenant instances with version rollback ('agent undo'), Resources offer portable storage for files, notes, and links, Deep Search performs sub-document multi-modal hybrid search, Extract turns PDFs, websites, images, and other files into structured JSON or Markdown, and Workers are autonomous agents that run scheduled tasks like searching the web, organizing files, or tagging content without cron jobs or servers. The platform is described as model-agnostic and framework-agnostic, usable via a simple API or TypeScript SDK, and claims to reduce agent token spend by up to 81% while keeping knowledge bases continuously current instead of stale.