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Quickstart

Get SharedMemory running and make your first memory write in under 5 minutes.

Using Claude, Cursor, or Windsurf?

Skip straight to the MCP tab below — it's the fastest way to get started. Zero code required.

1. Get your API key​

  1. Create an account — Sign up at app.sharedmemory.ai using email or Google.
  2. Create a volume — A volume is an isolated memory space. Create one from the dashboard sidebar.
  3. Register an agent — Go to Settings → Agents → Register Agent. This gives you an API key starting with sm_live_.
warning

Save your API key immediately — it's only shown once.

  1. Connect agent to volume — Link your agent to the volume you created. This grants read/write access.

2. Choose your integration​

npm install @sharedmemory/sdk
import { SharedMemory } from '@sharedmemory/sdk'

const memory = new SharedMemory({
apiKey: 'sm_live_...',
volumeId: 'your-volume-id',
})

// Store a memory
const result = await memory.remember("John is a senior engineer at Google")
console.log(result.status) // "approved"

// Query memories
const answer = await memory.recall("What does John do?")
console.log(answer.memories)

3. Upload a document​

SharedMemory can ingest PDFs, text files, markdown, CSV, JSON, and DOCX:

curl -X POST https://api.sharedmemory.ai/agent/documents/upload \
-H "Authorization: Bearer sm_live_..." \
-F "file=@resume.pdf" \
-F "volume_id=your-volume-id"

4. Explore the knowledge graph​

After ingesting data, SharedMemory automatically builds a knowledge graph. View it in:

  • Dashboard — Interactive 2D graph visualization with entity details
  • SDK — memory.getGraph() returns entities and relationships
  • CLI — smem search "topic" finds related entities
  • MCP — Use the explore_graph tool in Claude/Cursor

What happens behind the scenes​

When you write "John is a senior engineer at Google":

  1. Guard check — Verified against existing knowledge (no conflicts found → auto-approved)
  2. Embedding — Text embedded as a 384-dimensional vector
  3. Qdrant indexing — Stored for semantic search
  4. Knowledge extraction — LLM extracts:
    • Entity: John (Person)
    • Entity: Google (Organization)
    • Fact: "Senior engineer at Google" → linked to John
    • Relationship: John → WORKS_AT → Google
  5. Neo4j indexing — Entities, facts, and relationships stored in the graph
  6. Summary updated — John's auto-generated summary now includes this fact
All automatic

You don't need to define entities, configure extraction rules, or manage the graph. SharedMemory handles the entire pipeline from raw text to structured knowledge.

Next steps​

📦 TypeScript SDK​

Full SDK reference with all methods

📖 API Reference​

Complete REST API documentation

🔗 Knowledge Graph​

How the graph is built and queried

🔌 Integrations​

Connect GitHub, Slack, Notion, and more