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Documentation Index

Fetch the complete documentation index at: https://docs.trynebula.ai/llms.txt

Use this file to discover all available pages before exploring further.

You’ll need a Nebula API key before starting. Get one at trynebula.ai.
Using Claude Code, Cursor, or Codex? Run npx skills add nebula-agi/skills to integrate Nebula automatically.

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Prerequisites

  • Python 3.10+ or Node.js 18+
  • A Nebula API key from the dashboard

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1. Install

# Python
pip install nebula-sdk

# Node.js
npm install @nebula-ai/sdk

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2. Initialize Client

from nebula import Nebula

# Uses NEBULA_API_KEY environment variable
nebula = Nebula()


from nebula import Nebula

nebula = Nebula()

# Create a collection
collection = nebula.collections.create(
    name="my_notes",
    description="Personal notes",
).results

# Store a memory
created = nebula.memories.create(
    collection_id=collection.id,
    raw_text="Machine learning is transforming healthcare",
    metadata={"topic": "AI", "importance": "high"},
).results
memory_id = created.id

# Search memories
results = nebula.memories.search(
    query="machine learning healthcare",
    collection_ids=[collection.id],
).results

# Results contain semantic, procedural, episodic, and sources
for fact in results.semantic or []:
    print(f"[{fact['category']}] {fact['description']}")
for source in results.sources or []:
    print(source['text'])

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What You’ll Get Back

When you store a memory, Nebula returns the memory ID: 
memory_id = "eng_abc123def456"
When you search, Nebula returns a MemoryResponse with structured memory: 
{
  "semantic": [
    {
      "category": "fact",
      "description": "Machine Learning is transforming healthcare",
      "activation_score": 0.92
    }
  ],
  "procedural": [],
  "episodic": [],
  "sources": [
    {
      "text": "Machine learning is transforming healthcare",
      "activation_score": 0.91
    }
  ]
}
  • Semantics: Subject-predicate-value assertions (facts, inferences, tasks)
  • Procedures: User preferences and behavioral patterns
  • Episodes: Temporally clustered events
  • Sources: The original source text that grounds each assertion
See Core Concepts for a deeper explanation of the memory response.

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Next Steps