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• 🎯 Accurate: [48.77% Accuracy Improvement] More accurate than full-context in the LOCOMO benchmark (78.70% VS 52.9%)
• ⚡ Agile: [91.83% Faster Response] Significantly reduced p95 latency for retrieval compared to full-context (1.44s VS 17.12s)
• 💰 Affordable: [96.53% Token Reduction] Significantly reduced costs compared to full-context without sacrificing performance (0.9k VS 26k)

In AI application development, enabling large language models to persistently "remember" historical conversations, user preferences, and contextual information is a core challenge. PowerMem combines a hybrid storage architecture of vector retrieval, full-text search, and graph databases, and introduces the Ebbinghaus forgetting curve theory from cognitive science to build a powerful memory infrastructure for AI applications. The system also provides comprehensive multi-agent support capabilities, including agent memory isolation, cross-agent collaboration and sharing, fine-grained permission control, and privacy protection mechanisms, enabling multiple AI agents to achieve efficient collaboration while maintaining independent memory spaces.

• 🔌 Lightweight Integration: Provides a simple Python SDK, automatically loads configuration from .env files, enabling developers to quickly integrate into existing projects

• 🔍 Intelligent Memory Extraction: Automatically extracts key facts from conversations through LLM, intelligently detects duplicates, updates conflicting information, and merges related memories to ensure accuracy and consistency of the memory database
• 📉 Ebbinghaus Forgetting Curve: Based on the memory forgetting patterns from cognitive science, automatically calculates memory retention rates and implements time-decay weighting, prioritizing recent and relevant memories, allowing AI systems to naturally "forget" outdated information like humans

• 🔐 Agent Shared/Isolated Memory: Provides independent memory spaces for each agent, supports cross-agent memory sharing and collaboration, and enables flexible permission management through scope control

• 🖼️ Text, Image, and Audio Memory: Automatically converts images and audio to text descriptions for storage, supports retrieval of multimodal mixed content (text + image + audio), enabling AI systems to understand richer contextual information

• 📦 Sub Stores Support: Implements data partition management through sub stores, supports automatic query routing, significantly improving query performance and resource utilization for ultra-large-scale data
• 🔗 Hybrid Retrieval: Combines multi-channel recall capabilities of vector retrieval, full-text search, and graph retrieval, builds knowledge graphs through LLM and supports multi-hop graph traversal for precise retrieval of complex memory relationships

pip install powermem

✨ Simplest Way: Create memory from .env file automatically! Configuration Reference

from powermem import Memory, auto_config

# Load configuration (auto-loads from .env)
config = auto_config()
# Create memory instance
memory = Memory(config=config)

# Add memory
memory.add("User likes coffee", user_id="user123")

# Search memories
results = memory.search("user preferences", user_id="user123")
for result in results.get('results', []):
    print(f"- {result.get('memory')}")

For more detailed examples and usage patterns, see the Getting Started Guide.

• 🔗 LangChain Integration: Build medical support chatbot using LangChain + PowerMem + OceanBase, View Example
• 🔗 LangGraph Integration: Build customer service chatbot using LangGraph + PowerMem + OceanBase, View Example

• 📖 Getting Started: Installation and quick start guide
• ⚙️ Configuration Guide: Complete configuration options
• 🤖 Multi-Agent Guide: Multi-agent scenarios and examples
• 🔌 Integrations Guide: Integrations Guide
• 📦 Sub Stores Guide: Sub stores usage and examples
• 📋 API Documentation: Complete API reference
• 🏗️ Architecture Guide: System architecture and design
• 📓 Examples: Interactive Jupyter notebooks and use cases
• 👨‍💻 Development Documentation: Developer documentation

• 🐛 Issue Reporting: GitHub Issues
• 💭 Discussions: GitHub Discussions

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.