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Supermemory Review

Supermemory is a context infrastructure platform for AI agents, providing user profiles, memory graph, retrieval, extractors, and connectors.

shipped Apr 15, 2026updated May 27, 2026aifreemium
ai
Supermemory - AI tool

Why it matters

1Supermemory raised $3 million in Seed funding in October 2025.
2The Model Context Protocol (MCP) server received a major update to version 4.0 on December 30, 2025.
3Reported a 37.4% mean improvement and 41.4% median improvement in latency over Mem0 in user tests.
4Offers a freemium pricing model with a Pro Tier available at $29 per month.

Stork’s verdict on Supermemory

Supermemory delivers persistent, contextual memory for AI agents, yet its comprehensive infrastructure implies a significant setup and integration commitment.

Supermemory reviewed by Stork AI · stork.ai/en/supermemory

About Supermemory

Business Model
Subscription SaaS
Headquarters
Mumbai, India
Founded
2025
Team Size
10-50
Funding
Seed
Total Raised
$3 million
Platforms
Web, API
Target Audience
Developers, Enterprises, AI researchers

Pricing Plans

Free Tier
Free
  • • Basic features
  • • Limited API access
Pro Tier
$29/mo
  • • Full API access
  • • Advanced features
  • • Priority support

Leadership

Dhravya ShahFounderLinkedIn

Investors

Google Ventures, Susa Ventures

API DocsGitHubOpen Source

overview

What is Supermemory?

Supermemory is an AI memory layer and context engineering platform developed by Dhravya Shah that enables developers and enterprises to provide AI agents with persistent, scalable, and contextual memory. It handles the ingestion of raw data, transforms it into vector embeddings, and makes them retrievable through semantic search queries. The platform functions as an external brain for AI, addressing the 'digital amnesia' problem in AI applications by allowing agents to retain and leverage information across sessions and interactions. It supports various data sources and is compatible with major Large Language Models (LLMs), offering an API for integration.

features

Key Features of Supermemory

Supermemory provides a comprehensive suite of features designed to enhance AI agent capabilities through advanced memory and context management. These features are exposed via an API, allowing developers to integrate persistent memory into their AI applications. The platform includes open-source components for flexibility and control over data.

  • Context engineering platform for AI agents
  • Enterprise-grade API support
  • Developer plugins for various AI frameworks
  • Personal memory application for individual use
  • Open-source components for customization
  • Advanced retrieval capabilities for semantic search
  • Memory graph for structured information storage
  • Low-latency retrieval of contextual data
  • Support for ingestion of documents, chat histories, and user profiles
  • Vector embedding transformation and distributed database indexing

use cases

Who Should Use Supermemory?

Supermemory targets developers, teams building AI agents, and enterprises seeking to implement persistent and contextual memory into their AI applications. Its architecture supports a range of applications requiring continuous information retention and real-time data access for enhanced AI performance.

  • Developers and Teams building AI agents/applications: For giving AI agents continuous memory across sessions and building context-aware applications.
  • Educational platforms: For adapting educational content to learner progress in real time and creating AI tutors.
  • Healthcare companies: For securely enriching and retrieving patient data, ensuring compliance with privacy policies.
  • Customer support teams: For building chatbots that remember past interactions, providing more relevant and personalized responses.
  • Enterprises: For building internal knowledge bases accessible through AI agents and maintaining consistent brand voice in content generation.

pricing

Supermemory Pricing & Plans

Supermemory operates on a freemium business model, offering a free tier for initial exploration and a paid Pro tier for more extensive usage. The pricing structure is designed to accommodate both individual developers and larger teams or enterprises requiring advanced features and higher capacity.

  • Free Tier: Free
  • Pro Tier: $29/month

Similar Tools

Supermemory vs Competitors

Supermemory positions itself as a universal memory API that simplifies the infrastructure complexity of AI memory, offering an all-in-one solution for Retrieval Augmented Generation (RAG), memory, and extraction. It aims to provide a more stable and performant alternative to building in-house solutions or using certain existing memory layers.

1
Mem0On StorkCompare

Mem0 is a universal, self-improving AI memory layer for LLM applications, offering multi-level memory scopes and hybrid retrieval through a multi-store architecture.

Similar to Supermemory in providing an AI memory layer, Mem0 focuses heavily on personalization and adaptive updates across user, session, and agent levels, utilizing a multi-store architecture for comprehensive memory management. It offers both a managed platform and an open-source option for self-hosting.

2
ZepOn StorkCompare

Zep is a context engineering and long-term memory platform specifically optimized for conversational AI, focusing on extracting facts, summarizing conversations, and providing efficient context retrieval through semantic and temporal search.

While both offer memory for AI agents, Zep is particularly tailored for conversational AI applications, emphasizing chat history summarization and structured fact extraction, whereas Supermemory is described more broadly as a context engineering platform. Zep also leverages temporal knowledge graphs to organize memories and relationships.

3
LlamaIndexOn StorkCompare

LlamaIndex serves as a versatile data framework that connects custom data sources and formats to LLMs, enabling agents to remember and reason over structured information and documents by combining chat history with document context.

Supermemory focuses on an AI memory layer and context engineering, while LlamaIndex provides a broader data orchestration framework for integrating diverse data sources with LLMs, making it a comprehensive solution for knowledge-intensive agents. It offers both high-level APIs for quick integration and lower-level APIs for extensive customization.

4
LangChainOn StorkCompare

LangChain is a comprehensive open-source orchestration framework for building LLM-powered applications, offering a highly flexible memory component with various types and storage options that integrate natively with its broader ecosystem.

Supermemory is a dedicated memory and context engineering platform, whereas LangChain provides memory as a component within a larger framework for agent development, offering more extensive orchestration capabilities alongside its memory features. LangChain's ecosystem includes tools like LangGraph for agent workflows and LangSmith for observability.

5
CogneeOn StorkCompare

Cognee is an open-source AI memory engine and knowledge graph layer that structures, connects, and retrieves information with precision by building knowledge graphs from unstructured data, allowing agents to reason over relationships.

Supermemory provides an AI memory layer and context engineering, while Cognee specifically leverages knowledge graphs to give agents a dynamic, queryable understanding of interconnected data, which is a more structured approach to memory than simple retrieval. Cognee offers a freemium model with developer and enterprise plans.

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