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Welcome to Nebula

Nebula is a persistent memory layer for AI applications. Store, search, and retrieve information with semantic understanding—giving your AI long-term memory across every interaction.

Quick Example

from nebula import Nebula

nebula = Nebula(api_key="your_api_key")

# Create a memory collection
collection = nebula.create_collection(name="customer_support")

# Store information
nebula.store_memory({
    "collection_id": collection.id,
    "content": "User prefers email notifications over SMS",
    "metadata": {"user_id": "user_123", "preference": "email"}
})

# Search semantically
results = nebula.search("How does user_123 want to be contacted?", collection_id=collection.id)
print(results[0].content)  # Returns: "User prefers email notifications over SMS"
Get your API key and start building →

Key Features

  • Semantic Search - Find information by meaning, not just keywords
  • Persistent Memory - Context that persists across sessions and conversations
  • Rich Metadata - Filter and organize with custom metadata fields
  • Collection Organization - Group related information into logical collections
  • Sub-100ms Retrieval - Fast semantic search even at scale
  • Multi-Language SDKs - Python, JavaScript/TypeScript, and MCP support
  • SOC 2 Compliant - Enterprise-grade security and encryption

Core Concepts

Client Libraries

Next Steps

1

Quickstart

Get started in 5 minutes → Install the SDK, create your first collection, and store memories
2

Core Concepts

Learn the fundamentals → Understand how Nebula stores and organizes information
3

Build

Explore guides → Deep dive into memory operations, filtering, and conversations

Support

Need help? Reach out to [email protected]