From d6020d8bd786416794dd734f9d7c0637207c82be Mon Sep 17 00:00:00 2001
From: MarkLo127 <your-email@example.com>
Date: Thu, 12 Feb 2026 04:52:16 +0800
Subject: [PATCH]

---
 frontend/bun.lock                    |  1 +
 tradingagents/agents/utils/memory.py | 16 ++++++++++++----
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/frontend/bun.lock b/frontend/bun.lock
index b29dacc6..26ad08e0 100644
--- a/frontend/bun.lock
+++ b/frontend/bun.lock
@@ -1,5 +1,6 @@
 {
   "lockfileVersion": 1,
+  "configVersion": 0,
   "workspaces": {
     "": {
       "name": "frontend",
diff --git a/tradingagents/agents/utils/memory.py b/tradingagents/agents/utils/memory.py
index 6ee38eb1..e635185c 100644
--- a/tradingagents/agents/utils/memory.py
+++ b/tradingagents/agents/utils/memory.py
@@ -4,6 +4,9 @@ from chromadb.config import Settings
 
 
 class FinancialSituationMemory:
+    # Class-level cache: share a single SentenceTransformer instance across all memory objects
+    _model_cache: dict = {}
+
     def __init__(self, name, config):
         """
         Initialize the memory with configurable embedding provider.
@@ -42,12 +45,17 @@ class FinancialSituationMemory:
         model_name = config.get("embedding_model", "all-MiniLM-L6-v2")
         
         import logging
-        logging.info(f"Loading local embedding model: {model_name}")
         
-        self.model = SentenceTransformer(model_name)
+        # Reuse cached model if already loaded (avoids loading 5 copies and OOM)
+        if model_name not in FinancialSituationMemory._model_cache:
+            logging.info(f"Loading local embedding model: {model_name}")
+            FinancialSituationMemory._model_cache[model_name] = SentenceTransformer(model_name)
+            logging.info(f"Local embedding model loaded successfully.")
+        else:
+            logging.info(f"Reusing cached embedding model: {model_name}")
+        
+        self.model = FinancialSituationMemory._model_cache[model_name]
         self.embedding_dim = self.model.get_sentence_embedding_dimension()
-        
-        logging.info(f"Local embedding model loaded. Dimension: {self.embedding_dim}")
 
     def _init_openai_embedding(self, config):
         """Initialize OpenAI embedding client."""