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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.

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The basic flow

Linksee Memory has one core loop: remember → recall → act.

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Step 1: Remember something

In any conversation with your AI agent, say:
“Remember that our production database is PostgreSQL 16 on Supabase, and we had a connection pooling issue last week that was fixed by switching to transaction mode.”
The agent calls remember and stores this as structured memory:
  • Entity: your project name
  • Layer: implementation (how it was done)
  • Content: the PostgreSQL + pooling fix details
You can also say “use linksee” or “don’t forget this” to trigger memory storage.

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Step 2: Add a caveat

Caveats are the most valuable layer — pain lessons that should never be forgotten:
“Linksee caveat: Never use pgbouncer in session mode with Supabase — it causes prepared statement conflicts. Always use transaction mode.”
This gets stored in the caveat layer with protected = true — it will never be auto-forgotten.

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Step 3: Recall in a new session

Start a fresh session. Ask:
“I’m about to set up a new Supabase project. What do I need to know? Check linksee.”
The agent calls recall and returns your memories, ranked by relevance: 
{
  "memories": [
    {
      "id": 42,
      "layer": "caveat",
      "content": "Never use pgbouncer in session mode with Supabase...",
      "importance": 0.95,
      "match_reasons": ["content_match_fts", "caveat_protected", "heat:hot"]
    },
    {
      "id": 41,
      "layer": "implementation",
      "content": "Production database is PostgreSQL 16 on Supabase...",
      "importance": 0.6,
      "match_reasons": ["content_match_fts", "entity_name_match"]
    }
  ]
}
The caveat surfaces first because it’s protected and highly important.

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Step 4: Use read_smart for files

When re-reading a file you’ve seen before:
“Read src/db/connection.ts using linksee read_smart”
First read returns full content. On subsequent reads, if the file hasn’t changed, you get: 
{
  "status": "unchanged",
  "chunks": [
    { "name": "createPool", "hash": "a1b2c3...", "lines": "1-25" },
    { "name": "getConnection", "hash": "d4e5f6...", "lines": "27-45" }
  ],
  "tokens_saved": "~850 (was ~900, now ~50)"
}
~95% token savings on unchanged files.

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What to remember

Not everything needs to be memorized. Focus on:
Worth rememberingSkip
Decisions and why you made themRoutine code changes
Pain lessons and gotchasTemporary debug output
Architecture choicesOne-off questions
User preferencesContent that’s already in docs
Project-specific conventionsGeneric knowledge
The summarize-session prompt can automatically extract the right memories from a session transcript. Use it at the end of important sessions.

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Next steps

Memory Layers

Understand goal / context / emotion / implementation / caveat / learning

Token Saving

How read_smart saves 50-99% tokens with AST-aware diffing

Tools Reference

Full parameter docs for all 8 tools

Prompts

5 built-in prompts for common workflows