Skip to main content

AI Memory and Conversation Management Framework - Simple as mem0, Powerful as MemU

Project description

MemU Banner

memU: The Next-Gen Memory Framework for AI Companions

PyPI version License: Apache 2.0 Python 3.8+ Discord Twitter Reddit WeChat

MemU is an open-source memory framework for AI companions—high accuracy, fast retrieval, low cost. It acts as an intelligent "memory folder" that adapts to different AI companion scenarios.

With memU, you can build AI companions that truly remember you. They learn who you are, what you care about, and grow alongside you through every interaction.

🥇 92.9% Accuracy - 💰 90% Cost Reduction - 🤖 AI Companion Specialized

  • AI Companion Specialization - Adapt to AI companions application
  • 92.9% Accuracy - State-of-the-art score in Locomo benchmark
  • Up to 90% Cost Reduction - Through optimized online platform
  • Advanced Retrieval Strategies - Multiple methods including semantic search, hybrid search, contextual retrieval
  • 24/7 Support - For enterprise customers

⭐ Star Us on GitHub

Star MemU to get notified about new releases and join our growing community of AI developers building intelligent agents with persistent memory capabilities.

star-us

💬 Join our Discord community: https://discord.gg/hQZntfGsbJ


🚀Get Started

There are three ways to get started with MemU:

☁️ Cloud Version (Online Platform)

The fastest way to integrate your application with memU. Perfect for teams and individuals who want immediate access without setup complexity. We host the models, APIs, and cloud storage, ensuring your application gets the best quality AI memory.

  • Instant Access - Start integrating AI memories in minutes
  • Managed Infrastructure - We handle scaling, updates, and maintenance for optimal memory quality
  • Premium Support - Subscribe and get priority assistance from our engineering team

Step-by-step

Step 1: Create account & get your API key Go to https://app-preview.memu.so/api-key/ for generating api-keys. Step 2: Add three lines to your code

pip install memu-py

# Example usage
from memu import MemuClient

Step 3: Quick Start

# Initialize
memu_client = MemuClient(
    base_url="https://api-preview.memu.so", 
    api_key=os.getenv("MEMU_API_KEY")
)
memu_client.memorize_conversation(
    conversation_text=conversation_text,
    user_id="user001", 
    user_name="User", 
    agent_id="assistant001", 
    agent_name="Assistant"
)

📖 See example/client/openai_chat_example.py for complete integration details

That's it! MemU remembers everything and helps your AI learn from past conversations.

🏢 Enterprise Edition

For organizations requiring maximum security, customization, control and best quality:

  • Commercial License - Full proprietary features, commercial usage rights, white-labeling options
  • Custom Development - SSO/RBAC integration, dedicated algorithm team for scenario-specific framework optimization
  • Intelligence & Analytics - User behavior analysis, real-time production monitoring, automated agent optimization
  • Premium Support - 24/7 dedicated support, custom SLAs, professional implementation services

📧 Enterprise Inquiries: contact@nevamind.ai

🏠 Self-Hosting (Community Edition)

For users and developers who prefer local control, data privacy, or customization:

  • Data Privacy - Keep sensitive data within your infrastructure
  • Customization - Modify and extend the platform to fit your needs
  • Cost Control - Avoid recurring cloud fees for large-scale deployments

🚀 Coming Soon!


✨ Key Features

Autonomous Memory Management System

Organize - Autonomous Memory Management

Your memories are structured as intelligent folders managed by a memory agent. We do not do explicit modeling for memories. The memory agent automatically decides what to record, modify, or archive. Think of it as having a personal librarian who knows exactly how to organize your thoughts.

Link - Interconnected Knowledge Graph

Memories don't exist in isolation. Our system automatically creates meaningful connections between related memories, building a rich network of hyperlinked documents and transforming memory discovery from search into effortless recall.

Evolve - Continuous Self-Improvement

Even when offline, your memory agent keeps working. It generates new insights by analyzing existing memories, identifies patterns, and creates summary documents through self-reflection. Your knowledge base becomes smarter over time, not just larger.

Never Forget - Intelligent Retention System

The memory agent automatically prioritizes information based on usage patterns. Recently accessed memories remain highly accessible, while less relevant content is deprioritized or forgotten. This creates a personalized information hierarchy that evolves with your needs.


😺 Advantages

Higher Memory Accuracy

MemU achieves 92.09% average accuracy in Locomo dataset across all reasoning tasks, significantly outperforming competitors. Technical Report will be published soon!

Memory Accuracy Comparison

Flexible Retrieval Strategies

MemU offers multiple retrieval methods to handle different query types effectively: semantic search for conceptual queries, hybrid search for balanced precision and recall, and category-based search for structured data access.

Readable Memory Format

Unlike other memory frameworks that store information as fragmented sentences, MemU organizes memories as coherent, readable documents while simultaneously transforming raw data into structured. MemU maintains complete context and relationships, enabling easy debugging, manual editing, seamless analytics, and effortless integration with existing workflows.


🎓 Use Cases


AI Companion

AI Role Play

AI IP Character Avator

AI Education

AI Therapy

AI Robot

AI Creation
More...

🤝 Contributing

We build trust through open-source collaboration. Your creative contributions drive memU's innovation forward. Explore our GitHub issues and projects to get started and make your mark on the future of memU.

📋 Read our detailed Contributing Guide →

📄 License

By contributing to MemU, you agree that your contributions will be licensed under the Apache License 2.0.


🌍 Community

For more information please contact info@nevamind.ai

  • GitHub Issues: Report bugs, request features, and track development. Submit an issue

  • Discord: Get real-time support, chat with the community, and stay updated. Join us

  • X (Twitter): Follow for updates, AI insights, and key announcements. Follow us

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

memu_py-0.1.6.tar.gz (83.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

memu_py-0.1.6-py3-none-any.whl (92.2 kB view details)

Uploaded Python 3

File details

Details for the file memu_py-0.1.6.tar.gz.

File metadata

  • Download URL: memu_py-0.1.6.tar.gz
  • Upload date:
  • Size: 83.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for memu_py-0.1.6.tar.gz
Algorithm Hash digest
SHA256 e477203b8bcf7cf932a70a670174cc9893ab16e8555c58cea9611173c14fc605
MD5 cce9e36d81e257a58d3bce5caea4e80f
BLAKE2b-256 79e7080bdc4b0cfde66e5ca35ce0e4a4016fc2f0b85f07768170f80e088e3ce5

See more details on using hashes here.

Provenance

The following attestation bundles were made for memu_py-0.1.6.tar.gz:

Publisher: workflow.yml on NevaMind-AI/memU

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file memu_py-0.1.6-py3-none-any.whl.

File metadata

  • Download URL: memu_py-0.1.6-py3-none-any.whl
  • Upload date:
  • Size: 92.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for memu_py-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 e343b57614438f55f0c3fb4d9302281bed8447329adc880ff5f1361f41ad1ff5
MD5 f684da4ab447331d6bf1b74682a91b40
BLAKE2b-256 cebf9dca0ed2fc54460c90e7c0cbe280aa0b0ffb41332f2b7710717e42977611

See more details on using hashes here.

Provenance

The following attestation bundles were made for memu_py-0.1.6-py3-none-any.whl:

Publisher: workflow.yml on NevaMind-AI/memU

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page