diff --git a/agent/evolution/memory_compressor.py b/agent/evolution/memory_compressor.py
new file mode 100644
index 000000000..f7faeb37b
--- /dev/null
+++ b/agent/evolution/memory_compressor.py
@@ -0,0 +1,49 @@
+"""Phase 7: Long-Context Memory Compression.
+
+Compresses years of session transcripts into a hierarchical, searchable "Life Log".
+"""
+
+import logging
+import json
+from typing import List, Dict, Any
+from agent.gemini_adapter import GeminiAdapter
+from agent.symbolic_memory import SymbolicMemory
+
+logger = logging.getLogger(__name__)
+
+class MemoryCompressor:
+    def __init__(self):
+        self.adapter = GeminiAdapter()
+        self.symbolic = SymbolicMemory()
+
+    def compress_transcripts(self, transcripts: str) -> Dict[str, Any]:
+        """Compresses massive transcripts into a hierarchical memory map."""
+        logger.info("Compressing transcripts into hierarchical memory map.")
+        
+        prompt = f"""
+The following are session transcripts spanning a long period:
+{transcripts}
+
+Please perform a deep, recursive summarization of these transcripts.
+Identify key themes, major decisions, evolving preferences, and significant events.
+Create a hierarchical 'Life Log' map and extract high-fidelity symbolic triples for the Knowledge Graph.
+
+Format the output as JSON:
+{{
+  "summary": "...",
+  "hierarchy": {{...}},
+  "triples": [{{"s": "subject", "p": "predicate", "o": "object"}}]
+}}
+"""
+        result = self.adapter.generate(
+            model="gemini-3.1-pro-preview",
+            prompt=prompt,
+            system_instruction="You are Timmy's Memory Compressor. Your goal is to turn massive context into structured, searchable wisdom.",
+            thinking=True,
+            response_mime_type="application/json"
+        )
+        
+        memory_data = json.loads(result["text"])
+        self.symbolic.ingest_text(json.dumps(memory_data["triples"]))
+        logger.info(f"Ingested {len(memory_data['triples'])} new memory triples.")
+        return memory_data