Exabase: the context infrastructure layer every AI company needs

Every AI agent in production needs the same set of capabilities underneath it. It needs to remember things across sessions. It needs to search inside documents, not just about them. It needs to extract structured data from PDFs, images, audio, and video. It needs file storage that is indexed and searchable from the moment content arrives. And if it serves multiple users or teams, it needs isolated environments so one tenant's data never leaks into another's context.

These are not optional features that some agents need and others do not. They are the baseline infrastructure that any agent operating over real data, in a real product, with real users, requires to function reliably.

Most teams build this by stitching together a vector database for retrieval, a separate memory tool for conversation history, an object store for files, a parsing pipeline for document extraction, and some form of tenant isolation layered on top. Each tool has its own API, its own data model, its own failure modes, and its own maintenance burden. The integration work takes weeks or months. The ongoing maintenance is indefinite.

Exabase is the argument that this should be one system.

What the platform covers

Memory is persistent, structured recall across sessions. The engine underneath it, M-1, holds state-of-the-art results on both LongMemEval and BEAM at every evaluated scale. It extracts facts from conversations, resolves contradictions automatically, tracks how information changes over time, and retrieves using multiple signals rather than similarity alone. You add a memory, you search memories, you get back context that is accurate and ready for your prompt.

Deep Search is hybrid semantic and keyword search with temporal weighting, reranking, and sub-document precision. Text, PDFs, images, audio, and video are all searchable through the same endpoint, with results at the paragraph, timestamp, or region level. The precision parameter lets you explicitly trade recall for relevance depending on the query type.

Extract processes PDFs, images, audio, video, and web pages into structured JSON or markdown. Common document types like invoices and contracts return typed fields automatically. Every file is indexed at write time and immediately searchable through Deep Search.

Resources is the file storage layer. Upload a file and it is extracted, chunked, embedded, and indexed without a pipeline to build. The storage and the search are the same system.

Bases are isolated cloud filesystem instances for multi-tenant or team-scoped workloads, with snapshot rollback for version control. Each Base has its own resources, memories, and search scope.

Workers handle scheduled maintenance, enrichment, and automation tasks in the background.

Why it matters that these are one system

The value of a unified data layer is not convenience, though that matters too. It is that the components can work together in ways that separate tools cannot.

A file uploaded through Resources is automatically extracted and indexed, making it searchable through Deep Search without any additional integration. A conversation processed through Memory produces structured facts that are queryable alongside document search results. A Base scopes both memory and search to the right tenant without you building a permissions layer across multiple tools.

When these capabilities live in separate systems, every connection between them is integration code that you write and maintain. When they live in one system, the connections are handled and the data model is consistent. A query can draw on memory and document search simultaneously through the same API.

This also matters for token cost. Memory extraction replaces raw conversation history with structured facts, reducing context size by an order of magnitude. Deep Search retrieves precise passages rather than generic chunks. The combination produces an 81% median reduction in input tokens per request across the deployments we have measured. You can estimate the savings for your workload with the token cost calculator.

Where it came from

Exabase was not built speculatively. It grew out of Fabric, a consumer knowledge management product where real users store and retrieve information across months and years. The memory, search, and storage infrastructure was built against that production workload before it was offered as a developer platform.

That origin matters because the failure modes of context infrastructure reveal themselves slowly. Retrieval that works well at small scale degrades at production scale through semantic collapse. Memory that works for recent conversations fails when facts change over time through memory drift. Entity references that are obvious in a short history become fragmented across thousands of sessions. These problems were encountered and solved in Fabric before Exabase existed as a product for other developers.

Who it is for

Exabase is for teams building AI agents that operate over real data in production. If your agent needs to remember things across sessions, search inside documents and media, extract structured data from files, and serve multiple users with isolated contexts, Exabase covers all of it through one SDK.

It is model-agnostic and framework-agnostic. It works via REST API, Python and JavaScript SDKs, and MCP support for Claude, Cursor, and Windsurf. There is a free tier and a Scale plan at $149 per month.

The docs cover everything needed to get started. The examples show working implementations across common use cases.

FAQs

What is Exabase?

Exabase is a data layer for AI agents. It provides memory, search, file storage, extraction, and isolated workspaces through a single API. The platform is model-agnostic and framework-agnostic, accessible via REST API, Python and JavaScript SDKs, and MCP support for Claude, Cursor, and Windsurf.

What does "context infrastructure" mean?

It is the infrastructure between the model and the product: everything an agent needs to retrieve, store, and manage the data it operates on. Memory for cross-session recall, search for finding information inside documents and media, extraction for turning files into structured data, and storage with automatic indexing. Most teams build this from multiple separate tools. Exabase provides it as one system.

How is Exabase different from a vector database?

A vector database stores embeddings and retrieves by similarity. Exabase is a complete data layer that includes hybrid retrieval, memory extraction, contradiction resolution, temporal reasoning, entity resolution, document extraction, file storage, and multi-tenant isolation. A vector database gives you one retrieval signal. Exabase handles the full pipeline. See why a vector database is not a memory system.

How is Exabase different from Mem0?

Mem0 is a memory-only platform with an open-source codebase and a large developer community. Exabase provides memory alongside Deep Search, Extract, Resources, and Bases as a unified data layer. M-1 leads both major memory benchmarks using a cheaper model than Mem0's published results. See the full comparison.

What file types does Extract support?

PDFs, images, audio, video, and web pages. Common document types like invoices and contracts return typed structured fields. Every extracted file is automatically indexed and searchable through Deep Search. See the Extract docs.

What are Bases?

Bases are isolated cloud filesystem instances. Each Base has its own resources, memories, and search scope. They are designed for multi-tenant applications where one user's or team's data should never appear in another's context. Bases include snapshot rollback for version control. See Bases.

Does Exabase reduce LLM token costs?

Yes. Memory extraction replaces raw conversation history with structured facts, and Deep Search retrieves precise passages rather than generic chunks. Our testing shows a median 50% reduction and up to 81% on some deployments. See the token cost calculator and the full breakdown.

What does Exabase cost?

There is a free tier with no credit card required. The Scale plan is $149 per month. See pricing.

Does Exabase work with my agent framework?

Yes. Exabase is framework-agnostic. It works with LangChain, LlamaIndex, CrewAI, Vercel AI SDK, or any custom agent architecture via REST API, Python SDK, JavaScript SDK, or MCP. See the docs for integration guides.

Is Exabase open source?

No. Exabase is a managed API. The M-1 engine is proprietary. Benchmark evaluation methodology, prompts, and results are published and reproducible at exabase.io/research.