Mem0 is a self-improving memory layer for AI applications that enhances user experiences by remembering key details, reducing costs by up to 80%, and improving the accuracy of AI responses. It integrates easily with existing models and supports various use cases, including customer support, personal AI companions, and personalized learning.
Mem0
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Mem0 is a self-improving memory layer for LLM applications, enabling personalized AI experiences that save costs and delight users.
from mem0 import MemoryClient
client = MemoryClient(api_key="your-api-key")
# Store user preferenceclient.add([
{"role": "user", "content": "I love spicy food."},
{"role": "assistant", "content": "Noted! You enjoy spicy cuisine."}
], user_id="user123")
# Later, retrieve and use the preferencequery = "What food does the user like?"memory = client.search(query, user_id="user123")
print(f"Retrieved: {memory}")
# Output: Retrieved: The user loves spicy food.Trusted by
Key Features
Enhance Future Conversations
Remember key details across chats, building on past context for smarter interactions.
Save Money
Cut LLM costs by up to 80% by sending only relevant data to AI models.
Improve AI Responses
Deliver more accurate and contextually relevant AI outputs.
Easy Integration
Plug into existing AI models like OpenAI and Claude with minimal setup.
Use Cases
Customer Support
Speed up resolution times and improve customer satisfaction by providing agents with the right user and session context.
Personal AI Companion
Deliver personalized experiences every time by creating AI companions that remember past interactions.
AI Assistants
Improve task efficiency by building smart assistants that leverage historical context and adapt to user-specific needs.
Personalized Learning
Boost learner engagement by offering personalized content recommendations and tracking progress to tailor the educational experience.