Quick Start - Linksee Memory

The basic flow

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

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.

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.

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.

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.

What to remember

Not everything needs to be memorized. Focus on:

Worth remembering	Skip
Decisions and why you made them	Routine code changes
Pain lessons and gotchas	Temporary debug output
Architecture choices	One-off questions
User preferences	Content that’s already in docs
Project-specific conventions	Generic knowledge

The summarize-session prompt can automatically extract the right memories from a session transcript. Use it at the end of important sessions.

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 11 tools

Prompts

5 built-in prompts for common workflows

​The basic flow

​Step 1: Remember something

​Step 2: Add a caveat

​Step 3: Recall in a new session

​Step 4: Use read_smart for files

​What to remember

​Next steps

Memory Layers

Token Saving

Tools Reference

Prompts

The basic flow

Step 1: Remember something

Step 2: Add a caveat

Step 3: Recall in a new session

Step 4: Use read_smart for files

What to remember

Next steps