Merge remote-tracking branch 'origin/main' into mintlify/af82bda4

# Conflicts:
#	docs/cookbooks/integrations/supabase.mdx

docs/changelog/sdk.mdx

@@ -182,7 +182,7 @@ mode: "wide"
 <Update label="2026-06-24" description="v2.0.8">
 
 **New Features:**
-- **Embeddings:** Add native `embed_batch` to five embedders: LM Studio, Together, HuggingFace, Vertex AI, and Google GenAI: for batched embedding requests ([#5609](https://github.com/mem0ai/mem0/pull/5609))
+- **Embeddings:** Add native `embed_batch` to five embedders for batched embedding requests: LM Studio, Together, HuggingFace, Vertex AI, and Google GenAI ([#5609](https://github.com/mem0ai/mem0/pull/5609))
 
 **Bug Fixes:**
 - **Core:** Guard against malformed `image_url` entries in `parse_vision_messages` to prevent crashes ([#5631](https://github.com/mem0ai/mem0/pull/5631))

examples/misc/movie_recommendation_grok3.py

@@ -45,11 +45,11 @@ grok_client = OpenAI(
 
 def recommend_movie_with_memory(user_id: str, user_query: str):
     # Retrieve prior memory about movies
-    past_memories = memory.search("movie preferences", user_id=user_id)
+    past_memories = memory.search("movie preferences", filters={"user_id": user_id})
 
     prompt = user_query
-    if past_memories:
+    if past_memories["results"]:
-        prompt += f"\nPreviously, the user mentioned: {past_memories}"
+        prompt += f"\nPreviously, the user mentioned: {[m['memory'] for m in past_memories['results']]}"
 
     # Generate movie recommendation using Grok 3
     response = grok_client.chat.completions.create(model="grok-3-beta", messages=[{"role": "user", "content": prompt}])

examples/misc/strands_agent_aws_elasticache_neptune.py

@@ -198,7 +198,7 @@ def search_memory_tool(query: str, user_id: str = "user") -> str:
         Relevant vector memories found or message if none found
     """
     try:
-        results = m.search(query, user_id=user_id)
+        results = m.search(query, filters={"user_id": user_id})
 
         if isinstance(results, dict) and 'results' in results:
             memory_list = results['results']
@@ -245,7 +245,7 @@ def search_graph_memory_tool(query: str, user_id: str = "user") -> str:
     """
     try:
         graph_query = f"relationships connections {query}"
-        results = m.search(graph_query, user_id=user_id)
+        results = m.search(graph_query, filters={"user_id": user_id})
 
         if isinstance(results, dict) and 'results' in results:
             memory_list = results['results']
@@ -290,7 +290,7 @@ def get_all_memories_tool(user_id: str = "user") -> str:
         All memories for the user or message if none found
     """
     try:
-        all_memories = m.get_all(user_id=user_id)
+        all_memories = m.get_all(filters={"user_id": user_id})
 
         if isinstance(all_memories, dict) and 'results' in all_memories:
             memory_list = all_memories['results']

examples/misc/vllm_example.py

@@ -107,16 +107,16 @@ def main():
 
     for query in search_queries:
         print(f"\nQuery: {query}")
-        memories = memory.search(query=query, user_id="user_123")
+        memories = memory.search(query=query, filters={"user_id": "user_123"})
 
-        for memory_item in memories:
+        for memory_item in memories["results"]:
             print(f"  - {memory_item['memory']}")
 
     print("\n--> Getting all memories for user...")
-    all_memories = memory.get_all(user_id="user_123")
+    all_memories = memory.get_all(filters={"user_id": "user_123"})
-    print(f"Total memories stored: {len(all_memories)}")
+    print(f"Total memories stored: {len(all_memories['results'])}")
 
-    for memory_item in all_memories:
+    for memory_item in all_memories["results"]:
         print(f"  - {memory_item['memory']}")
 
     print("\n--> vLLM integration demo completed successfully!")