supermemoryai / supermemory

2024-02-27 · Source: Github Trending: All languages · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Supermemory is an AI memory and context engine designed to provide persistent memory for AI assistants and applications, addressing the issue of AI forgetting information between conversations. It leads major AI memory benchmarks, including LongMemEval (81.6% - #1), LoCoMo (#1), and ConvoMem (#1). The system automatically learns from conversations, extracts facts, builds user profiles, handles knowledge updates and contradictions, and manages information forgetting. It offers a comprehensive context stack, including RAG capabilities, connectors for services like Google Drive and GitHub, and multi-modal extractors for PDFs, images, videos, and code. Supermemory is available as a consumer-facing app, browser extension, and through an API for developers building AI products, supporting integrations with frameworks like Vercel AI SDK and LangChain.

Key takeaway

For AI Architects and Machine Learning Engineers building conversational AI, Supermemory offers a robust solution to overcome the "forgetting" problem. Your agents can gain persistent memory, automatically learn user preferences, and access real-time context without complex vector database or embedding pipeline configurations. Consider integrating Supermemory's API to enhance personalization and knowledge retrieval, ensuring your AI applications deliver more coherent and contextually relevant interactions.

Key insights

Supermemory provides persistent, context-aware memory for AI, outperforming benchmarks by integrating RAG with dynamic user profiles.

Principles

• AI memory requires dynamic fact extraction and contradiction resolution.
• Hybrid search combines RAG with personalized memory for comprehensive context.
• Automated forgetting prevents irrelevant or outdated information from persisting.

Method

Supermemory operates by extracting facts from conversations, building user profiles (static and dynamic), and performing hybrid searches across memories and documents, all within a unified memory structure and ontology.

In practice

• Use `client.add()` to store conversation content for memory.
• Utilize `client.profile()` for combined user profile and relevant memories.
• Integrate with frameworks like Vercel AI SDK using `withSupermemory` wrappers.

Topics

• AI Memory Management
• Retrieval-Augmented Generation
• User Profiling
• Multi-modal Data Processing
• AI Benchmarking

Code references

• supermemoryai/supermemory
• xiaowu0162/LongMemEval
• snap-research/locomo
• Salesforce/ConvoMem
• supermemoryai/openclaw-supermemory

Best for: AI Architect, Machine Learning Engineer, CTO, AI Engineer, Software Engineer, AI Researcher

Related on AIssential

• How AI Memory Really Works: Why Your Best Conversations Still Need a Vault — AI models are stateless; effective "memory" relies on external systems managing context windows and retrieval.
• ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting — ScrapMem enables efficient, personalized long-term memory for LLM agents on edge devices via optical compression and structured graphs.
• SCM: Sleep-Consolidated Memory with Algorithmic Forgetting for Large Language Models — SCM introduces a brain-inspired memory architecture for LLMs, combining consolidation and forgetting for efficient, accurate recall.
• Enhancing Long-term RAG Chatbots with Psychological Models of Memory Importance and Forgetting — Integrating psychological models of memory importance and forgetting enhances RAG chatbot effectiveness in long-term conversations.
• How xMemory cuts token costs and context bloat in AI agents — xMemory uses a hierarchical memory structure and uncertainty-gated retrieval to optimize long-term AI agent coherence and reduce token costs.
• rohitg00 / agentmemory — Agentmemory provides persistent, searchable memory for AI coding agents, enhancing efficiency and reducing redundant explanations.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Github Trending: All languages.