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Exabase gives you everything you need to give your agent context.
Over
files, workspaces, and memories created
Problem
Your AI is expensive because it's stateless.
Every API call stuffs the full conversation history into the context window.
10,000 tokens. 50,000 tokens. More.
Your token bill grows with every user, every session, every feature.
You've tried shorter prompts, cheaper models, caching. Each one trades quality for cost.
The real problem: your agent doesn't remember anything, so you send everything, every time.
Solution
Memory & context infra that cut your tokens by 80%.
Exabase gives your agent persistent memory. Instead of sending 50,000 tokens of conversation history, retrieve the 10 relevant memories at 500 tokens.
Better context. Lower cost. Responses that are more accurate because the signal-to-noise ratio improves.
Plus the infrastructure you'd otherwise build yourself – file processing, semantic search, cloud storage, multi-tenant isolation.
One platform instead of five services stitched together.
Stop paying for context your model doesn't need. Stop building infrastructure that isn't your product.
FAQs
What is Exabase?
Exabase is infrastructure for AI agents. It gives your agents memory, versioned file storage, AI deep search, and context automation through a set of APIs. Store what your agent learns, search inside any content type, and keep knowledge bases current automatically. Built for production use. Give your agent precise context and cut your token spend by up to 81%.
Who uses Exabase?
Teams shipping AI agents to production. Startups building copilots that need persistent memory. SaaS companies adding AI features with per-user data isolation. Enterprise teams replacing brittle RAG pipelines with a managed data layer.
What can I build with Exabase?
Anything that needs agents to remember, store, or retrieve. Customer support bots with long-term memory, research agents that build their own knowledge bases, coding assistants that remember your codebase, sales copilots that track deal context across conversations.
What's the difference between Exabase and a vector database?
A vector database gives you semantic search over embeddings you create. You still build the ingestion pipeline, handle chunking, manage file parsing, and figure out citations yourself. Exabase handles the full stack from raw file to searchable, locatable result. It also does things vector databases don't: structured extraction, memory that tracks relationships and resolves contradictions, deep AI search inside files, versioned file storage with folders and tags, and scheduled agents that maintain your knowledge base automatically.
How does Exabase relate to Fabric?
Exabase is the infrastructure layer behind Fabric, the AI personal data platform where hundreds of thousands of users store and search their files, notes, and links. The same technology that powers Fabric is available to developers through Exabase's APIs. It's production-tested at scale, not a research project.
Can I use Exabase with my existing stack?
Yes. Exabase is model-agnostic and framework-agnostic. Use the TypeScript SDK or call the REST API directly. Works with any stack.
Who needs a memory API for AI agents?
Any team building an agent, copilot, or assistant that talks to the same user more than once. Customer support bots, sales copilots, coding assistants, learning platforms, healthcare agents. If your agent forgets everything between sessions, you need a memory layer.
What is a data layer for AI agents?
The infrastructure between your agent and its data. Memory, file storage, search, and extraction. Instead of stitching together a vector database, S3, an embedding pipeline, and a cron scheduler, a data layer gives you all of it through one API.
How do I add long-term memory to an AI agent?
Send your agent's conversations to a memory API. Exabase extracts facts, preferences, and events automatically, organizes them into a self-improving knowledge graph, and makes them searchable via semantic search. Two API calls: one to add, one to retrieve.
What are Exabase Workers?
Autonomous agents that run inside your Bases on a schedule. Define a task in natural language, set a frequency (daily, weekly, monthly), and Workers handle it. They can search the web, organize files, tag content, create notes, and manage tasks. No cron jobs, no servers. Knowledge that compounds instead of decaying.
Do I need to use the whole platform?
No. You can mix and match features, and Memory and Extract can be used by themselves. The platform features work better together, but there's no requirement to adopt everything at once.
What does it cost?
You can sign up and start building for free. See the pricing page for plan details and limits.
