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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
146 lines
4 KiB
Text
146 lines
4 KiB
Text
---
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title: "How Graph Memory Works"
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sidebarTitle: "Graph Memory"
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description: "Automatic memory evolution, knowledge updates, and intelligent forgetting"
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icon: "vector-square"
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---
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Supermemory builds a living knowledge graph where memories connect to other memories. Unlike traditional knowledge graphs with entity-relation-entity triples, Supermemory's graph is **facts built on top of other facts**.
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## Memory Relationships
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When you add content, Supermemory extracts facts and automatically connects them to existing memories through three relationship types:
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### Updates: Information Changes
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When new information contradicts existing knowledge:
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```
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Memory 1: "Alex works at Google as a software engineer"
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Memory 2: "Alex just started at Stripe as a PM"
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↓
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Memory 2 UPDATES Memory 1
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```
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The system tracks which memory is latest with `isLatest`, so searches return current information while preserving history.
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### Extends: Information Enriches
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When new information adds detail without replacing:
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```
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Memory 1: "Alex works at Stripe as a PM"
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Memory 2: "Alex focuses on payments infrastructure and leads a team of 5"
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↓
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Memory 2 EXTENDS Memory 1
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```
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Both memories remain valid—searches get richer context.
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### Derives: Information Infers
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When Supermemory infers new facts from patterns:
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```
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Memory 1: "Alex is a PM at Stripe"
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Memory 2: "Alex frequently discusses payment APIs and fraud detection"
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↓
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Derived: "Alex likely works on Stripe's core payments product"
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```
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These inferences surface insights you didn't explicitly state.
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---
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## Automatic Memory Extraction
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From a single conversation, Supermemory extracts multiple connected memories:
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**Input:**
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> "Had a great call with Alex. He's enjoying the new PM role at Stripe, though the
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> payments infrastructure work is intense. He moved to Seattle for the job—got a
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> place in Capitol Hill. Wants to grab dinner next time I'm in town."
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**Extracted memories:**
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- Alex works at Stripe as a PM
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- Alex works on payments infrastructure *(extends role memory)*
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- Alex lives in Seattle, Capitol Hill *(new fact)*
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- Alex wants to meet for dinner *(episodic)*
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Each fact is connected to related memories automatically.
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---
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## Automatic Forgetting
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Supermemory knows when memories become irrelevant:
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**Time-based forgetting**: Temporary facts are automatically forgotten when they expire.
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```
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"I have an exam tomorrow"
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↓
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After the exam date passes → automatically forgotten
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"Meeting with Alex at 3pm today"
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↓
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After today → automatically forgotten
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```
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**Contradiction resolution**: When new facts contradict old ones, the Update relationship ensures searches return current information.
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**Noise filtering**: Casual, non-meaningful content doesn't become permanent memories.
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---
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## Memory Types
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Supermemory distinguishes memory types automatically:
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| Type | Example | Behavior |
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|------|---------|----------|
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| **Facts** | "Alex is a PM at Stripe" | Persists until updated |
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| **Preferences** | "Alex prefers morning meetings" | Strengthens with repetition |
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| **Episodes** | "Met Alex for coffee Tuesday" | Decays unless significant |
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---
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## What You Don't Do
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All of this is automatic. You don't:
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- Define relationships manually
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- Tag memory types
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- Clean up old memories
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- Resolve contradictions
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Just add content and search naturally:
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```typescript
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await client.add({
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content: "Alex mentioned he just started at Stripe"
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});
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const results = await client.search({
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query: "where does Alex work?"
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});
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// → Stripe (latest), previously Google (historical)
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```
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---
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## Learn More
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<CardGroup cols={2}>
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<Card title="How It Works" icon="cpu" href="/concepts/how-it-works">
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Deep dive into the architecture
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</Card>
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<Card title="Memory vs RAG" icon="scale" href="/concepts/memory-vs-rag">
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When to use memory vs document retrieval
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</Card>
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<Card title="User Profiles" icon="user" href="/user-profiles">
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Automatic summaries from the graph
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</Card>
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<Card title="Add Memories" icon="plus" href="/add-memories">
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Start building your knowledge graph
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</Card>
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</CardGroup>
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