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Documentation Index

Fetch the complete documentation index at: https://docs.linksee.app/llms.txt

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

Most agent memory systems store everything in a flat list. Linksee Memory organizes memories into 6 cognitive layers, each with different retention and retrieval behavior.

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The 6 layers

WHY this work exists. The target outcome. Persists across sessions so the agent doesn’t drift.
  • Never auto-forgotten (risk = 0)
  • Set at session start or when the user states a new objective
  • Example: “Ship v1.0 by end of Q2 with cross-agent recall and token-saving”
WHY THIS, NOW. Situational timing, background info, reasons for current priorities.
  • Normal decay rate
  • Consolidated after going cold
  • Example: “Vercel had a security incident in April — rotating all API keys across projects”
User tone and sentiment. Frustration, excitement, urgency expressed during work.
  • Normal decay rate
  • Helps agents calibrate tone in future sessions
  • Example: “User frustrated with freee API pagination — 3 sessions debugging”
HOW it was done. What worked, what failed, technical details of execution.
  • Normal decay rate
  • Most common layer for day-to-day memories
  • Example: “Switched from REST to GraphQL for freee sync — 3x faster batch queries”
PAIN lessons. “Never X when Y.” The protected pile of things you don’t want to relearn.
  • Always protected — never auto-forgotten, never consolidated
  • Start with a verb: “Never”, “Always”, “Watch out”
  • Example: “Never use pgbouncer session mode with Supabase — prepared statement conflicts”
GROWTH. Decisions made, insights gained, patterns recognized.
  • Normal decay rate but typically higher importance
  • Target layer for consolidation output
  • Example: “freee webhook reliability is ~95% — always implement polling fallback”

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Layer aliases

You don’t need to remember exact layer names. Common aliases are automatically resolved:
You sayStored as
why, intent, targetsgoal
background, reason, situation, timingcontext
tone, feelings, moodemotion
impl, how, tried, success, failureimplementation
warning, pain, pitfall, dont, rulecaveat
decision, insight, growth, learnedlearning

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Retention behavior

LayerAuto-forgetConsolidation targetProtection
goalNeverNoImplicit
contextNormalYes → learningNo
emotionNormalYes → learningNo
implementationNormalYes → learningNo
caveatNeverNoAlways
learningNormalNo (already target)No

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Why layers matter

Without layers, recall("Supabase") returns a wall of undifferentiated text. With layers, the agent can:
  • Start with goal to understand direction
  • Check caveat before making changes
  • Skim implementation for prior approaches
  • Use learning for distilled wisdom
This is the difference between “I read my notes” and “I understand my history.”