Supermemory
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About
Supermemory is a specialized context engineering infrastructure designed to provide AI agents and assistants with a sophisticated, persistent memory layer. Unlike standard Retrieval-Augmented Generation (RAG) systems or vector databases that often lose context as data evolves, Supermemory utilizes a hybrid vector-graph engine. This approach allows AI systems to track relationships between data points, understand user intent, and evolve their knowledge base over time. By acting as an independent middle layer, the platform enables developers to maintain user context even if they switch between different large language models, such as those from OpenAI or Anthropic, effectively preventing model lock-in. The system works by ingesting data from a wide variety of sources, including PDFs, Markdown files, chat histories, and video content. Through its SDKs for TypeScript and Python, developers can connect their existing tech stack to the Supermemory API in minutes. Once data is ingested, it undergoes automated cleaning and chunking before being enriched through graph-based indexing. This process links related memories across different applications and contexts, ensuring that when an AI agent retrieves information, it receives the most relevant and current data with sub-300ms latency. The infrastructure is built on high-performance tools like Postgres and custom vector engines on Cloudflare Durable Objects, capable of handling billions of tokens daily. This tool is primarily designed for developers, startups, and enterprises building complex AI applications that require a deep understanding of their users. It is particularly effective for AI tutors needing to track a student's learning progress, healthcare platforms managing patient records, and legal copilots searching through case law. Because it is model-agnostic, it serves as a universal resource for teams who want to prioritize personalization without being tied to a specific AI provider's ecosystem. What sets Supermemory apart is its focus on "semantic memory" that behaves similarly to the human brain by adapting, learning, and even forgetting outdated information. While many RAG solutions are static, Supermemory's adaptive layer ensures that AI agents improve their reasoning and recall as interactions increase. It also provides a comprehensive suite of pre-built connectors for popular tools like Google Drive, Notion, and OneDrive, reducing the amount of custom infrastructure work typically required to build a high-quality memory system for AI products.
Pros & Cons
Enables AI to remember user preferences across different chat sessions and platforms.
Eliminates model lock-in by maintaining an independent memory state from the LLM provider.
Provides extremely low latency retrieval at sub-300ms for real-time applications.
Offers a generous free tier for developers to experiment with 1M tokens per month.
Supports a wide range of data formats including videos, PDFs, and structured app data.
Advanced analytics features are locked behind the paid Pro tier.
The Scale and Enterprise plans have a significant jump in cost from the Pro plan.
Direct Slack support is only available for high-tier enterprise users.
Self-hosted data ownership is restricted to the custom Enterprise plan.
Use Cases
AI assistant developers can use the API to give their agents persistent memory of user roles and preferences across every interaction.
Education platforms can implement learning graphs that track a student's comprehension of textbooks and lectures to personalize study paths.
Legal tech teams can build copilots that search across complex ecosystems of contracts and case law with high precision.
SaaS companies can unify internal documentation from wikis and emails into a single evolving memory layer for faster employee onboarding.
Healthcare organizations can securely process and summarize patient records to support clinical decision-making with context-aware AI agents.
Platform
Features
• model-agnostic memory layer
• multi-format data extraction
• graph-based relationship enrichment
• adaptive semantic memory
• pre-built app connectors
• automated data chunking
• sub-300ms recall latency
• vector-graph engine
FAQs
Which AI models are compatible with Supermemory?
Supermemory is model-agnostic and works with any model provider, including OpenAI, Anthropic, and various open-source models via its SDKs for TypeScript and Python.
How does the memory retrieval latency compare to other systems?
The platform is built for high speed, offering sub-300ms recall for both semantic and keyword searches, ensuring that personalization does not slow down the AI interaction.
Can I import data from my existing apps?
Yes, Supermemory includes built-in connectors for Google Drive, Notion, and OneDrive, and it can also ingest data from PDFs, videos, and website projects.
What happens if I exceed my monthly token limit on a paid plan?
For Pro and Scale plans, overage fees apply at a rate of $0.01 per 1000 tokens processed and $0.10 per 1000 search queries.
Is there support available for early-stage startups?
Yes, Supermemory offers a Startup Program that provides $1,000 in Pro credits for six months and direct collaboration with their team for eligible founders.
Pricing Plans
Pro
USD19.00 / per month• 3M tokens processed
• 100K search queries
• Priority support
• Advanced analytics
Scale
USD399.00 / per month• 80M tokens processed
• 20M search queries
• Dedicated support
• Custom Integration
• Slack Support Channel
Enterprise
Unknown Price• Unlimited token usage
• Unlimited search queries
• Forward deployed engineer
• SLA guarantees
• Self own data
Job Opportunities
There are currently no job postings for this AI tool.
Ratings & Reviews
No ratings available yet. Be the first to rate this tool!
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