Exabase is #1 on the leading AI memory benchmark

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Exabase is #1 on the leading AI memory benchmark

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The data layer
for agents.

The data layer
for agents.

Infrastructure for self-improving memory, knowledge grounding, and cloud filesystems for agents.

Everything you need to give your AI agent context and real memory.

Infrastructure for self-improving memory, knowledge grounding, and cloud filesystems for agents.

Everything you need to give your AI agent context and real memory.

Over

20,000,000

20,000,000

files, workspaces, and memories created

Stateful agent infrastructure

Problem

Agents can't remember anything.

Agents show up, do something, disappear. They don't remember yesterday, or what's important to your customer.


So you build it yourself. Conversation logs, files in S3, maybe a vector database. It works until it doesn't.


You end up maintaining plumbing instead of building your product.

Solution

Memory, context and search for agents.

Memory that persists, evolves and updates automatically. On-demand cloud filesystem instances. Semantic search. Access to what the user actually knows.


Exabase gives agents a place to work & remember.


Ship faster with memory and knowledge solved for you.

Production-ready

Private by design

Security-first

Scalable

Production-ready

Private by design

Security-first

Scalable

Production-ready

Private by design

Security-first

Scalable

Datapoint

Exabase M-1 memory system is benchmarked as #1 in the world on the leading benchmark.

Memory

AI memory that works like human memory.

Store facts, preferences, events. Search by meaning. Retrieve context and distilled knowledge for any task.


Memory that tracks relationships, handles contradictions, and evolves over time.


Not just retrieval. True, self-improving memory.

Resources

AI-native filesystem for users and agents.

Files, notes, bookmarks, documents. Full CRUD, semantic search, folders and tags. Everything a user or your agent creates or needs to reference. Persisted and searchable.


With developer-managed accounts, your agent or app can read and write to a virtual workspace.

Bases

Where your agents work and store progress.

Create and manage cloud filesystem instances programmatically.


Each Base is a full workspace environment with memory, files, and search. Isolated and controlled by your app.


Audit through the API or directly through our workspace explorer GUI.


Roll back to any past state with Filesystem Snapshotting. Protect against agent mistakes.

Deep Search

Frontier search, that works out of the box.

Multi-modal hybrid search API. Search inside PDFs, audio, video, and images at the sub-document level. No embedding pipeline to build.

Extract

Structured data from any source.

Send a URL, file, image, audio, or video. Get clean JSON back.

Extraction API for PDFs, images, audio, video, and web pages. Async processing, text chunks with page numbers, thumbnails, webhooks. One endpoint, any source.

Workers

Knowledge on autopilot.

Autonomous AI agents that organize, enrich, and maintain your knowledge base on a schedule. Knowledge that compounds instead of decaying. No cron jobs, no servers.

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.

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.

Who uses Exabase?

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 can I build with Exabase?

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.

What's the difference between Exabase and a vector database?

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.

How does Exabase relate to Fabric?

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.

Can I use Exabase with my existing 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.

Who needs a memory API for AI agents?

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.

What is a data layer for AI agents?

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.

How do I add long-term memory to an AI agent?

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.

What are Exabase Workers?

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.

Do I need to use the whole platform?

What does it cost?

Yes. Sign up and start building for free. See the pricing page for plan details and limits.

What does it cost?

Deciding?

Ask your favourite AI about Exabase:

Deciding?

Ask your favourite AI about Exabase:

Ship your first app in minutes.

Ship your first app in minutes.