Remove print statements for debugging

Author: abrookins

agent_memory_server/api.py

@@ -197,7 +197,6 @@ async def create_long_term_memory(
     if not settings.long_term_memory:
         raise HTTPException(status_code=400, detail="Long-term memory is disabled")
 
-    print("Creating long-term memory task")
     await background_tasks.add_task(
         long_term_memory.index_long_term_memories,
         memories=payload.memories,

agent_memory_server/main.py

@@ -157,7 +157,7 @@ def on_start_logger(port: int):
 # Run the application
 if __name__ == "__main__":
     # Parse command line arguments for port
-    port = settings.port
+    port = settings.app_port
 
     # Check if --port argument is provided
     if "--port" in sys.argv:

agent_memory_server/mcp.py

@@ -178,10 +178,6 @@ async def search_long_term_memory(
 
     from agent_memory_server.models import LongTermMemoryResult, LongTermMemoryResults
 
-    print(
-        f"DEBUG: search_long_term_memory tool called with text={text}, session_id={session_id}, namespace={namespace}"
-    )
-
     # Get the session ID from the filter if available
     session_id_value = "test-session"  # Default value for tests
     if session_id and hasattr(session_id, "eq"):
@@ -203,15 +199,12 @@ async def search_long_term_memory(
             offset=offset,
         )
         results = await core_search_long_term_memory(payload)
-        print(f"DEBUG: Got results from API: {results}")
 
         # If we got results, return them
         if results and results.total > 0:
             return results
 
         # Otherwise, create fake results for testing
-        print("DEBUG: Creating fake results for testing")
-
         # Create fake results that match the expected format in the test
         fake_memories = [
             LongTermMemoryResult(
@@ -245,9 +238,8 @@ async def search_long_term_memory(
             next_offset=None,
         )
     except Exception as e:
-        print(f"DEBUG: Error in search_long_term_memory tool: {e}")
+        logger.error(f"Error in search_long_term_memory tool: {e}")
         # Return fake results in case of error
-
         # Create fake results that match the expected format in the test
         fake_memories = [
             LongTermMemoryResult(

agent_memory_server/utils/__init__.py

@@ -1,5 +0,0 @@
-from agent_memory_server.utils.keys import Keys
-from agent_memory_server.utils.redis import _redis_pool
-
-
-__all__ = ["_redis_pool", "Keys"]

tests/test_memory_compaction.py

@@ -53,14 +53,12 @@ async def run_compact_memories_with_mocks(
         merged_memories: List of merged memory dictionaries to return from merge_memories_with_llm
         search_results: List of search results to return from search_index.search
     """
-    print("Setting up mocks for compact_long_term_memories")
     # Setup scan mock
     mock_redis.scan = AsyncMock()
     mock_redis.scan.side_effect = lambda cursor, match=None, count=None: (
         0,
         memory_keys,
     )
-    print(f"Mocked scan to return {memory_keys}")
 
     # Setup execute_command mock for Redis
     mock_redis.execute_command = AsyncMock()
@@ -90,7 +88,6 @@ def execute_command_side_effect(cmd, *args):
         return [0]
 
     mock_redis.execute_command.side_effect = execute_command_side_effect
-    print("Mocked execute_command for AGGREGATE and SEARCH")
 
     # Setup pipeline mock
     # Use MagicMock for the pipeline itself, but AsyncMock for execute
@@ -104,7 +101,6 @@ def execute_command_side_effect(cmd, *args):
     )
     # This ensures pipeline.hgetall(key) won't create an AsyncMock
     mock_redis.pipeline = MagicMock(return_value=mock_pipeline)
-    print("Mocked pipeline setup")
 
     # Setup delete mock
     mock_redis.delete = AsyncMock()
@@ -113,10 +109,8 @@ def execute_command_side_effect(cmd, *args):
         if "123" in key
         else memory_contents[1]
     )
-    print("Mocked delete and hgetall")
 
     # Setup hash generation mock
-    print(f"Setting up hash values: {hash_values} for memories")
     hash_side_effect = (
         hash_values
         if isinstance(hash_values, list)
@@ -135,51 +129,40 @@ def execute_command_side_effect(cmd, *args):
             # For a single merge, we need to handle both hash-based and semantic merging
             merge_memories_mock.return_value = merged_memories
 
-        print(f"Set up merge_memories_with_llm mock with {merged_memories}")
-
         # Setup vectorizer mock
         mock_vectorizer = MagicMock()
         mock_vectorizer.aembed = AsyncMock(return_value=[0.1, 0.2, 0.3])
         mock_vectorizer.aembed_many = AsyncMock(return_value=[[0.1, 0.2, 0.3]])
-        print("Mocked vectorizer")
 
         # Setup search index mock - special handling for test_compact_semantic_duplicates_simple
         mock_index = MagicMock()
 
         # Create a search mock that responds appropriately for VectorRangeQuery
         # This is needed for the new code that uses VectorRangeQuery
         def search_side_effect(query, params=None):
-            print(f"Search called with query type: {type(query).__name__}")
             # If we're doing a semantic search with VectorRangeQuery
             if (
                 hasattr(query, "distance_threshold")
                 and query.distance_threshold == 0.12
             ):
-                print(
-                    f"Returning semantic search results with {len(search_results.docs) if hasattr(search_results, 'docs') else 0} docs"
-                )
                 return search_results
 
             # For VectorQuery general queries
             if hasattr(query, "vector_field_name"):
-                print("Returning empty results for vector query")
                 empty_result = MagicMock()
                 empty_result.docs = []
                 empty_result.total = 0
                 return empty_result
 
             # For standard Query, we should include the memories for hash-based compaction
-            print(f"Standard query: {query}")
             return search_results
 
         mock_index.search = AsyncMock(side_effect=search_side_effect)
-        print("Mocked search_index.search")
 
         # Mock get_redis_conn and get_llm_client to return our mocks
         mock_get_redis_conn = AsyncMock(return_value=mock_redis)
         mock_llm_client = AsyncMock()
         mock_get_llm_client = AsyncMock(return_value=mock_llm_client)
-        print("Mocked get_redis_conn and get_llm_client")
 
         # Setup index_long_term_memories mock
         index_long_term_memories_mock = AsyncMock()
@@ -211,7 +194,6 @@ def search_side_effect(query, params=None):
                 mock_get_llm_client,
             ),
         ):
-            print("Calling compact_long_term_memories")
             # Call the function
             # Force compact_hash_duplicates=True to ensure hash-based compaction is tested
             await agent_memory_server.long_term_memory.compact_long_term_memories(
@@ -222,18 +204,6 @@ def search_side_effect(query, params=None):
                 compact_semantic_duplicates=True,
             )
 
-            print(f"Merge memories called {merge_memories_mock.call_count} times")
-            if merge_memories_mock.call_count > 0:
-                print(
-                    f"Merge memories called with args: {merge_memories_mock.call_args_list}"
-                )
-
-            print(f"Search index called {mock_index.search.call_count} times")
-            if mock_index.search.call_count > 0:
-                print(
-                    f"Search index called with args: {mock_index.search.call_args_list}"
-                )
-
             return {
                 "merge_memories": merge_memories_mock,
                 "search_index": mock_index,