Long-term memory layer for AI apps and agents, helps LLM-powered tools remember user context over time to personalize and cut costs.
Founded by: Taranjeet Singh (Co-Founder & CEO) • Deshraj Yadav (Co-Founder & CTO) in 2023
Long-term memory layer for AI apps and agents, helps LLM-powered tools remember user context over time to personalize and cut costs.
Founded by: Taranjeet Singh (Co-Founder & CEO) • Deshraj Yadav (Co-Founder & CTO) in 2023
You can use Mem0 when you build AI apps that need to remember things—preferences, past conversations, user details—so they become more helpful and less repetitive. It works via a lightweight API/SDK that compresses memory, reduces token usage, and preserves relevant context across sessions. Ideal for AI tutors, customer support bots, wellness/health assistants, or any LLM agent where personalization matters and keeping full context every time is too expensive.
Is this a good fit for my stack? Use Cases AI tutors that adapt to student learning style and past mistakes
Customer support bots that recall previous tickets or user preferences
Wellness or health companions that remember user history (preferences, medication, routines)
Sales / CRM assistants that track interactions and follow-ups across sessions
Personal assistants that avoid re-asking what you already told them
Any LLM agent with multi-session / long lifecycle where cost and memory matter
Standout Features Memory Compression Engine that cuts prompt tokens up to ~80%
Hybrid datastore: vector, graph, key-value stores to balance speed, relevance, and structure
Zero-trust deployment options: hosted, on-prem, private cloud
Traceability / observability: TTL, versions, exportable memory records
OpenMemory MCP: local memory management and syncing across tools with privacy control
Benchmarking shows strong accuracy gains + latency reduction vs full-context or memory-less LLMs
Tasks it helps with Store and retrieve user information, preferences and past interactions
Compress and maintain memory to reduce token usage and latency
Support hybrid memory datastores (vector, graph, key-value)
Allow memory to be versioned, traced, and exported for audits
Provide both hosted and self-hosted (on-prem/private cloud) deployment modes
Integrate with existing LLMs and platforms via SDK/API quickly
Who is it for? AI Engineer, Full-Stack Developer, Product Manager, CTO, Start-up Founder
Overall Web Sentiment People love it
Time to value Quick Setup (< 1 hour to integrate basic memory layer) AI memory layer, long-term memory, LLMs, vector memory, graph memory, context persistence, token savings, personalized agents
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