diff --git a/tools/memory_query_router.py b/tools/memory_query_router.py
new file mode 100644
index 000000000..48991da6b
--- /dev/null
+++ b/tools/memory_query_router.py
@@ -0,0 +1,209 @@
+"""
+Hybrid Memory Query Router
+
+Routes queries to the best search method:
+- HRR: Compositional/conceptual queries
+- Vector: Semantic similarity
+- FTS5: Exact keyword matching
+
+Issue: #663
+"""
+
+import re
+from collections import defaultdict
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple
+
+
+class SearchMethod(Enum):
+    """Available search methods."""
+    HRR = "hrr"          # Holographic Reduced Representations
+    VECTOR = "vector"    # Semantic vector search
+    FTS5 = "fts5"        # Full-text search (SQLite)
+    HYBRID = "hybrid"    # Combine multiple methods
+
+
+@dataclass
+class QueryClassification:
+    """Result of query classification."""
+    method: SearchMethod
+    confidence: float
+    reason: str
+    sub_queries: Optional[List[str]] = None
+
+
+# Query patterns for routing
+COMPOSITIONAL_PATTERNS = [
+    r"(?i)\brelated\s+to\b",
+    r"(?i)\bcombined\s+with\b",
+    r"(?i)\bbound\s+to\b",
+    r"(?i)\bassociated\s+with\b",
+    r"(?i)\bwhat\s+connects?\b",
+    r"(?i)\bhow\s+.*\s+relate\b",
+    r"(?i)\brelationship\s+between\b",
+]
+
+CONTRADICTION_PATTERNS = [
+    r"(?i)\bcontradicts?\b",
+    r"(?i)\bconflicts?\s+with\b",
+    r"(?i)\binconsistent\b",
+    r"(?i)\bopposite\s+of\b",
+    r"(?i)\bopposes?\b",
+    r"(?i)\bdisagrees?\s+with\b",
+]
+
+EXACT_KEYWORD_PATTERNS = [
+    r'"[^"]+"',  # Quoted phrases
+    r"'[^']+'",  # Single-quoted phrases
+    r"(?i)\bexact\b",
+    r"(?i)\bprecisely\b",
+    r"(?i)\bspecifically\b",
+]
+
+TEMPORAL_PATTERNS = [
+    r"(?i)\brecent\b",
+    r"(?i)\btoday\b",
+    r"(?i)\byesterday\b",
+    r"(?i)\blast\s+(week|month|hour)\b",
+    r"(?i)\bsince\b",
+    r"(?i)\bbefore\b",
+    r"(?i)\bafter\b",
+]
+
+
+class QueryRouter:
+    """Route queries to the best search method."""
+    
+    def classify(self, query: str) -> QueryClassification:
+        """Classify a query and route to best method."""
+        
+        # Check for contradiction queries (HRR)
+        for pattern in CONTRADICTION_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.HRR,
+                    confidence=0.95,
+                    reason="Contradiction detection query"
+                )
+        
+        # Check for compositional queries (HRR)
+        for pattern in COMPOSITIONAL_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.HRR,
+                    confidence=0.90,
+                    reason="Compositional/conceptual query"
+                )
+        
+        # Check for exact keyword queries (FTS5)
+        for pattern in EXACT_KEYWORD_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.FTS5,
+                    confidence=0.85,
+                    reason="Exact keyword query"
+                )
+        
+        # Check for temporal queries (FTS5)
+        for pattern in TEMPORAL_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.FTS5,
+                    confidence=0.80,
+                    reason="Temporal query"
+                )
+        
+        # Short queries tend to be keyword searches
+        if len(query.split()) <= 3:
+            return QueryClassification(
+                method=SearchMethod.FTS5,
+                confidence=0.70,
+                reason="Short query (likely keyword)"
+            )
+        
+        # Default: vector search for semantic queries
+        return QueryClassification(
+            method=SearchMethod.VECTOR,
+            confidence=0.60,
+            reason="Semantic similarity query"
+        )
+    
+    def should_use_hybrid(self, query: str) -> bool:
+        """Check if query should use hybrid search."""
+        classification = self.classify(query)
+        
+        # Low confidence -> use hybrid
+        if classification.confidence < 0.70:
+            return True
+        
+        # Mixed signals -> use hybrid
+        has_compositional = any(re.search(p, query) for p in COMPOSITIONAL_PATTERNS)
+        has_keywords = any(re.search(p, query) for p in EXACT_KEYWORD_PATTERNS)
+        
+        return has_compositional and has_keywords
+
+
+def reciprocal_rank_fusion(
+    results: Dict[str, List[Tuple[str, float]]],
+    k: int = 60
+) -> List[Tuple[str, float]]:
+    """
+    Merge results using Reciprocal Rank Fusion.
+    
+    Args:
+        results: Dict of method -> [(item_id, score), ...]
+        k: RRF constant (default 60)
+    
+    Returns:
+        Merged and re-ranked results
+    """
+    scores = defaultdict(float)
+    
+    for method, ranked_items in results.items():
+        for rank, (item_id, _) in enumerate(ranked_items, 1):
+            scores[item_id] += 1.0 / (k + rank)
+    
+    return sorted(scores.items(), key=lambda x: x[1], reverse=True)
+
+
+def merge_with_hrr_priority(
+    hrr_results: List[Tuple[str, float]],
+    vector_results: List[Tuple[str, float]],
+    fts5_results: List[Tuple[str, float]],
+    query_type: str = "default"
+) -> List[Tuple[str, float]]:
+    """
+    Merge results with HRR priority for compositional queries.
+    """
+    if query_type == "compositional":
+        # HRR first, vector as supplement
+        merged = hrr_results[:5]
+        seen = {r[0] for r in merged}
+        
+        for r in vector_results[:5]:
+            if r[0] not in seen:
+                merged.append(r)
+        
+        return merged
+    
+    # Default: RRF merge
+    return reciprocal_rank_fusion({
+        "hrr": hrr_results,
+        "vector": vector_results,
+        "fts5": fts5_results
+    })
+
+
+# Module-level router
+_router = QueryRouter()
+
+
+def route_query(query: str) -> QueryClassification:
+    """Route a query to the best search method."""
+    return _router.classify(query)
+
+
+def should_use_hybrid(query: str) -> bool:
+    """Check if query should use hybrid search."""
+    return _router.should_use_hybrid(query)