Seamlessly migrate from Mem0 to GravixLayer with full API compatibility and enhanced features for memory management.
Mem0 Compatibility
GravixLayer Memory provides full API compatibility with Mem0 while adding enhanced features like dynamic configuration and multi-index support.
Default Configuration: Uses baai/bge-large-en-v1.5 embedding model, gravixlayer_memories index, AWS us-east-1. Available models: baai/bge-large-en-v1.5, microsoft/multilingual-e5-large, nomic-ai/nomic-embed-text-v1.5
Basic Mem0 Compatibility
Python SDK
JavaScript SDK
from gravixlayer import GravixLayer
client = GravixLayer()
memory = client.memory
# Add memory (same as Mem0)
result = memory.add("I love pizza", user_id="alice")
print(f"Added: {result['results'][0]['memory']}")
# Search memories (same as Mem0)
results = memory.search("pizza", user_id="alice")
print(f"Found {len(results['results'])} memories")
# Get all memories (same as Mem0)
all_memories = memory.get_all(user_id="alice")
print(f"Total: {len(all_memories['results'])} memories")
# Update memory (same as Mem0)
memory_id = result['results'][0]['id']
memory.update(memory_id, "alice", "I absolutely love pizza")
# Delete memory (same as Mem0)
memory.delete(memory_id, "alice")
Enhanced Features
Python SDK
JavaScript SDK
# List available indexes
indexes = memory.list_available_indexes()
print(f"Available indexes: {indexes}")
# Switch to different index
memory.switch_index("user_preferences")
memory.add("User likes large fonts", user_id="alice")
# Switch configuration for multilingual support
memory.switch_configuration(
embedding_model="microsoft/multilingual-e5-large",
index_name="multilingual_memories"
)
# Add multilingual memories
memory.add("El usuario prefiere pizza", user_id="alice")
memory.add("L'utilisateur aime le café", user_id="alice")
# Search works across languages
results = memory.search("food preferences", user_id="alice")
print(f"Found: {len(results['results'])} memories")
Migration from Mem0
Before (Mem0)
After (GravixLayer)
from mem0 import Memory
memory = Memory()
result = memory.add("I love pizza", user_id="alice")
results = memory.search("food", user_id="alice")
Key Features
Core Methods
add() - Add memoriessearch() - Search memoriesget_all() - List all memoriesupdate() - Update memoriesdelete() - Delete memories
Enhanced Methods
list_available_indexes() - List available indexes- Per-operation
index_name parameter for all methods
{
"results": [
{
"id": "memory-id",
"memory": "memory content",
"metadata": {},
"created_at": "timestamp"
}
]
}
Parameters
user_id - User identifier (required)messages - Text or conversation arraymetadata - Additional context (optional)infer - Enable AI inference (optional)index_name - Target specific index (optional, new)
