Introducing CortexDB: The Long-Term Memory Layer for AI Systems

Why we built CortexDB

AI systems often struggle with continuity. They lose context between sessions, forget important prior decisions, and cannot easily draw from the full working memory of a team, product, or organization.

Every agent framework today has the same blind spot: memory. Agents can reason, plan, and execute tools. But ask them what happened yesterday, what decisions were made last week, or what a customer said three conversations ago, and they fall apart.

The existing solutions approach this problem by running every piece of incoming data through an LLM to "summarize" it before storage. This sounds reasonable until you realize what it actually means: your data is being rewritten by a model that hallucinates, loses nuance, and costs $10,000/day at even modest scale.

We built CortexDB because we believe memory systems should work like databases, not like LLM chains. Store the raw data. Index it properly. Let the LLM enrich it asynchronously, off the critical path. This one architectural decision changes everything.

What teams use CortexDB for

Whether you are building an internal copilot, a support assistant, an engineering knowledge layer, or a companion-style application, CortexDB is designed to make memory a first-class part of the product. Teams choose CortexDB when they want to:

• Give agents durable memory beyond a single conversation
• Connect knowledge across tools like Slack, GitHub, and Jira
• Provide richer context to workflows, copilots, and assistants
• Support multi-tenant and enterprise use cases with full isolation
• Make memory accessible through SDKs, APIs, and MCP-compatible tools

Key advantages

How CortexDB fits into your stack

At a high level, CortexDB sits between your applications and the knowledge they need to retain and retrieve. It commonly works with:

• Application events and user interactions
• Chat, ticketing, and collaboration systems (Slack, Jira, Discord)
• Code and engineering systems (GitHub, GitLab, Linear)
• Agent frameworks and orchestration layers (LangChain, CrewAI, Temporal)
• APIs, SDKs, and MCP-based tool environments

How it works

The architecture: event sourcing vs. LLM rewriting

CortexDB is built on an event-sourced architecture. Every piece of information that enters the system is stored as an immutable event -- the raw content, exactly as received. This is the source of truth. Everything else (knowledge graph entries, vector embeddings, search indexes) is a materialized view derived from these events.

This architecture gives us three critical advantages: lossless data preservation, dramatically lower write-path cost (no LLM on the critical path), and crash durability (WAL + durable storage means zero data loss, even under SIGKILL).

Technical advantages

CortexDB's architecture delivers fundamental advantages over memory systems that rewrite data through an LLM before storage.

Systems that rewrite data through an LLM before storage fundamentally lose information. CortexDB preserves the original. When your retrieval system has the full source data to search against, it finds better answers.

51+ integrations across the AI ecosystem

CortexDB connects to the tools your team already uses — agent frameworks, orchestration layers, data sources, LLM providers, and IDE environments.

Getting started

CortexDB is ready to use today. Here is the fastest path to your first deployment:

# Install the Python SDK
pip install cortexdbai

# Store your first memory
from cortexdb import Cortex

client = Cortex(api_key="your-api-key")
client.remember(
    content="We decided to migrate from PostgreSQL to CockroachDB "
            "for the payments service. Timeline is Q2 2026.",
    tenant_id="acme-corp",
)

# Query it back
result = client.recall(
    query="What database are we using for payments?",
    tenant_id="acme-corp",
)
print(result.context)
# => "We decided to migrate from PostgreSQL to CockroachDB..."

CortexDB Cloud handles multi-node clustering and high availability automatically. See the Python quickstart or TypeScript quickstart for step-by-step guides. You can also use CortexDB directly from your IDE via the MCP server.

What comes next

Today marks the beginning. Over the coming months, we are shipping:

• Managed cloud (Pro tier) — so you never think about infrastructure
• More data connectors — Notion, Linear, Google Drive, and more
• Multi-DC replication — consistent hashing and CRDTs for global distribution
• Cortex OS — a platform layer with agent orchestration and SDK marketplace

We believe every AI system deserves a memory layer as reliable as a production database. CortexDB is that layer.