Mem0 is a universal, self-improving memory layer for AI agents and LLM applications, helping teams build personalized, context-aware experiences with lower token usage and faster responses.
Mem0 is a universal memory layer for AI agents and LLM applications. It helps developers build personalized, context-aware AI experiences by giving applications persistent memory across sessions, users, and workflows.
Most LLM applications are stateless by default: they lose context after a conversation ends and often rely on repeatedly sending large histories back into the model. Mem0 provides a dedicated memory layer that detects, stores, updates, and retrieves relevant memories so agents can remember user preferences, traits, prior interactions, important events, and domain-specific context over time.
With Mem0, teams can reduce redundant context, improve response relevance, and build AI systems that adapt as user needs evolve. Mem0 supports long-term, short-term, semantic, and episodic memory patterns, with APIs designed for fast integration into existing AI applications.
Key capabilities include:
Persistent memory across conversations, users, sessions, and agents
Automatic memory extraction, updates, and retrieval from user interactions
Semantic search with relevance, recency, and importance-aware retrieval
Support for personalized assistants, customer support agents, healthcare assistants, education tools, sales workflows, and productivity applications
Developer-friendly SDKs and APIs for rapid integration
Mem0 is available as open-source software and as a managed platform for teams that want production-ready memory infrastructure without managing the underlying system. The open-source project has more than 56,000 GitHub stars, and Mem0's research reports significant improvements in latency and token efficiency compared with full-context approaches.
Use Mem0 to build AI agents that remember what matters, personalize every interaction, and scale from prototype to production.
Highlights
1. Persistent memory for AI agents that remember user preferences, history, and context across sessions.<br>2. Reduce redundant context and token usage while improving response relevance and personalization.<br>3. Developer-friendly APIs with open-source flexibility and a managed platform for production deployments.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This listing offers one plan, called Hobby. You pay through a contract commitment for a fixed monthly allowance. The plan includes 10,000 memories and 1,000 retrieval API calls per month. Memories store information from user conversations. Retrieval API calls fetch that stored information back. Your usage is capped at these two monthly limits. There are no additional tiers or usage add-ons to select on AWS Marketplace. Pricing does not scale by seat or instance size. You commit to the single allowance defined by this plan.
Top-of-mind questions for buyers
What is the difference between a memory add request and a retrieval API call for billing?
An add request stores new information from conversations, counting toward your 10,000 monthly memory allowance. A retrieval API call fetches stored memories back using semantic search, counting toward your 1,000 monthly retrieval allowance. Both meters run separately, so each type has its own monthly cap.
What happens if I reach the 10,000 memory or 1,000 retrieval limit before month end?
The Hobby plan caps you at 10,000 memories and 1,000 retrieval API calls each month. These are the only allowances on AWS Marketplace. There are no add-ons or overage tiers to select here. Both limits reset on your monthly cycle rather than scaling automatically.
Does adding more end users increase what I pay under this plan?
No. The Hobby plan bills on the two monthly usage meters, not per user. You can serve many end users, but their combined activity draws from the same 10,000 memory and 1,000 retrieval allowances. Pricing does not scale by seat or user count.
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Vendor refund policy
Refund requests are reviewed on a case-by-case basis. To request a refund, please contact the Mem0 team at support@mem0.ai with your AWS Marketplace order details and the reason for the request.
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Mem0 provides support for AWS Marketplace customers through email and online documentation.
Support includes assistance with account setup, onboarding, product configuration, API integration, billing questions related to the listing, and guidance for self-hosted deployments.
Useful resources:
- Documentation:
For support, contact the Mem0 team at:
AWS infrastructure support
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Memori is agent-native memory infrastructure: an LLM-agnostic layer that transforms agent execution and conversation history into structured, persistent state for production systems. Unlike conversational memory wrappers or vector retrieval, Memori captures tool calls, decisions, traces, and user context as durable, queryable state that agents rely on across sessions, models, and workflows.
Let Memori handle agent memory so your team can improve accuracy and cut inference costs instead of building and operating a memory system themselves.
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