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Velixar can automatically extract key facts, preferences, and decisions from conversations — no manual tagging required.

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How it works

1

Conversation flows normally

Your user chats with your AI. No changes to your chat flow needed.
2

Extraction triggers

Extraction runs on two triggers:
  • Session end — POST /api/session/end fires extraction on the full conversation
  • Periodic — Every 10 messages as a fallback
3

LLM extracts memories

An LLM analyzes the conversation and pulls out discrete facts, preferences, and decisions with a salience score.
4

Dedup & conflict resolution

  • Semantic dedup: If a new memory has cosine similarity > 0.85 with an existing one, it’s skipped
  • Conflict resolution: If the new memory contradicts an old one, the old memory is deleted and the new one is stored
5

Tier routing

Memories are auto-routed based on salience:
  • Salience ≥ 0.8 → Tier 0 (pinned)
  • Salience < 0.8 → Tier 2 (semantic)

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Example

A user says during a chat:
“Actually, I switched from VS Code to Cursor last week. And I prefer TypeScript over JavaScript now.”
Velixar extracts:
MemoryTypeSalienceTier
”User’s primary editor is Cursor (switched from VS Code)“preference0.850
”User prefers TypeScript over JavaScript”preference0.820
If a previous memory said “User’s primary editor is VS Code”, it gets deleted and replaced.

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Triggering extraction

End a session to trigger extraction: 
curl -X POST https://api.velixarai.com/api/session/end \
  -H "Authorization: Bearer vlx_your_key" \
  -H "Content-Type: application/json" \
  -d '{"session_id": "sess_abc123"}'
The extraction runs asynchronously. Extracted memories appear in your memory store within seconds.