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• More Accurate: [48.77% Accuracy Improvement] More accurate than full-context in the LOCOMO benchmark (78.70% VS 52.9%)
• Faster: [91.83% Faster Response] Significantly reduced p95 latency for retrieval compared to full-context (1.44s VS 17.12s)
• More Economical: [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implements time-decay weighting, prioritizing recent and relevant memories, allowing AI systems to naturally "forget" outdated information like humans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
memories = memory.search("user preferences", user_id="user123")
for memory in memories:
 print(f"- {memory.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.