Memory Search

Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.

You have two tools for recalling information from your memory files. Use them.

Tools

memory_search

Semantic vector search across your indexed memory files (MEMORY.md, memory/*.md, and session transcripts).

Parameters:

Param	Type	Required	Description
query	string	yes	Natural language question or topic to search for
maxResults	number	no	Max results to return (default: 6)
minScore	number	no	Minimum relevance score threshold (0-1)

Example calls:

json

{ "query": "what projects is the human working on" }
{ "query": "preferences about code style", "maxResults": 3 }
{ "query": "important dates birthdays deadlines", "maxResults": 10, "minScore": 0.3 }

Returns: Array of results, each with:

• snippet — the matching text chunk
• path — relative file path (e.g. MEMORY.md, memory/2026-02-07.md)
• startLine / endLine — line range in the source file
• score — relevance score
• citation — formatted source reference (in direct chats)

memory_get

Read a specific section of a memory file by path and line range. Use this after memory_search to pull more context around a result.

Parameters:

Param	Type	Required	Description
path	string	yes	Relative path from workspace (e.g. MEMORY.md, memory/2026-02-07.md)
from	number	no	Starting line number
lines	number	no	Number of lines to read

Example calls:

json

{ "path": "MEMORY.md" }
{ "path": "memory/2026-02-07.md", "from": 15, "lines": 30 }

When to Use Memory Search

Always search before answering about:

• Prior conversations or decisions
• The human's preferences, habits, or opinions
• Dates, deadlines, birthdays, events
• Project status or history
• Anything the human said "remember this" about
• Todos, action items, or commitments
• People, names, relationships

The pattern is:

1. Receive a question that might involve past context
2. Call memory_search with a relevant query
3. Review the results
4. If a snippet looks promising but needs more context, call memory_get with the path and line range
5. Answer using what you found (cite sources in direct chats)

When NOT to Use

• Purely factual questions with no personal context ("what is Python?")
• The human explicitly gives you all the context you need in the message
• You just searched and the results are still in your context

Tips

• Be specific in queries. "birthday" works better than "important information about the human."
• Search multiple angles. If one query returns nothing useful, try rephrasing. "project deadlines" and "what's due soon" might return different results.
• Don't over-fetch. Start with default maxResults. Only increase if you need more coverage.
• Use memory_get sparingly. The search snippets are usually enough. Only pull full sections when you need surrounding context.
• Say when you checked. If you searched and found nothing, tell the human: "I checked my memory and didn't find anything about that." Don't silently guess.

What Gets Indexed

Your memory search covers:

• MEMORY.md — your curated long-term memory
• memory/*.md — daily notes and raw logs
• Session transcripts (if enabled)

These files are automatically indexed. You don't need to trigger indexing — just write to the files and the system handles the rest.

Do NOT

• Do NOT try to run shell commands like cat or ls to read memory files. Use memory_search and memory_get.
• Do NOT try to configure or debug the search system. That's operator config, not your job.
• Do NOT assume memory is empty without searching first. The index may have content even if the memory/ directory looks sparse.