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Documentation Index

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CommercialInfrastructure Layer

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Nebula AI

Production memory for AI agents — not just vector similarity, but a living model of what’s true, what’s changed, and why.
Nebula AI

Type

Commercial

Stack Layer

Infrastructure

Pricing

Free tier available

Connectors

Slack, Notion, Gmail, MCP

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What it is

Nebula is a memory and state layer for AI agents that goes beyond naive vector similarity search. It maintains a living model of what is known, what has changed, and why — using a hierarchical vector graph architecture with entity resolution (canonicalizing the same entity referenced different ways), temporal reasoning (understanding when information was true), and contradiction detection (flagging when new information conflicts with existing knowledge). It supports long-horizon memory across sessions, multimodal inputs (text, images, audio, PDFs), and native connectors for Slack, Notion, Gmail, Claude, and MCP. The infrastructure claim is near-lossless scaling to hundreds of millions of tokens — the kind of memory depth required for agents that operate continuously over weeks or months on real business data.
Use this when you’re building long-running AI agents that need consistent, evolving memory across sessions — especially where the same entity might be referenced many ways, or where outdated information would cause incorrect decisions.

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Get started

trynebula.ai ↗

Product details, free tier, and connector documentation.

Ruflo

Multi-agent orchestration with built-in HNSW vector memory via AgentDB.

Paperclip

Human control plane for managing persistent AI agent state and governance.