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MQL — Memory Query Language

LLMFS includes a custom query language that compiles to ChromaDB + SQLite queries. MQL provides a SQL-like syntax for expressive memory retrieval.

Syntax

-- Semantic similarity search in a path prefix
SELECT memory FROM /knowledge WHERE SIMILAR TO "authentication bug" LIMIT 5

-- Tag filter
SELECT memory FROM /knowledge WHERE TAG = "s3" LIMIT 10

-- Combined semantic + tag filter
SELECT memory FROM /knowledge WHERE SIMILAR TO "bucket error" AND TAG = "s3" LIMIT 5

-- Time-scoped search
SELECT memory FROM /events WHERE date > 2026-01-01 AND date < 2026-04-01

-- Topic / keyword filter
SELECT memory FROM /projects WHERE topic = "authentication"

-- Order by recency
SELECT memory FROM /session ORDER BY created_at DESC LIMIT 10

-- Graph traversal (BFS, depth 2)
SELECT memory FROM /projects RELATED TO "/events/2026-03-15/bug" WITHIN 2

Usage

results = mem.query(
    'SELECT memory FROM /knowledge WHERE SIMILAR TO "JWT expiry" AND TAG = "auth" LIMIT 5'
)
for r in results:
    print(r.path, r.score)

llmfs query 'SELECT memory FROM /knowledge WHERE SIMILAR TO "auth bug"'
llmfs query 'SELECT memory FROM /events WHERE TAG = "deploy"' --json

Supported Conditions

Condition Syntax Backed By
SIMILAR TO SIMILAR TO "query string" ChromaDB vector search
TAG TAG = "tagname" SQLite tag index
date date > 2026-01-01 SQLite date filter
topic topic = "keyword" SQLite metadata filter
RELATED TO RELATED TO "/path" WITHIN N Graph BFS traversal
AND / OR logical combinators Merged result sets

How It Works

  1. The MQL parser tokenizes the query string and builds an AST (Abstract Syntax Tree)
  2. The MQL executor walks the AST and translates each node to ChromaDB or SQLite operations
  3. Results from different conditions are fused and de-duplicated
  4. The final result set is returned as list[SearchResult]