Mem0

What Mem0 does

Mem0 provides a universal, self-improving memory layer for AI agents and LLM applications. It offers drop-in memory infrastructure that persists across sessions and agents, designed for production environments. The platform helps AI remember what matters, offering efficiency, visibility, and control through features like a Memory Compression Engine. Mem0 is built for developers and enterprises, offering governance, reliability, and full observability, with compliance certifications like SOC 2 Type 1 and HIPAA. It aims to make persistent memory a standard infrastructure for every AI developer, similar to a database.

Products

• Mem0 PlatformA cloud-managed memory layer for AI agents and applications, offering persistent context, memory compression, and various integration options. Available in Hobby, Starter, Pro, and Enterprise tiers.
• Mem0 Open-SourceA self-hostable version of Mem0 for developers who want memory without the hosted platform.

Key capabilities

• Persistent memory across sessions and agents
• Drop-in memory infrastructure
• Memory Compression Engine
• Automatic condensation of chat history
• No config or boilerplate for input data
• Extracts and updates memories automatically
• Retrieves key memories as users interact
• Adapts to specific domains
• Efficiency (less redundant context, lower token costs, faster responses)
• Visibility
• Control
• Governance (SOC 2, HIPAA, BYOK, zero-trust)
• Portable (Kubernetes, private cloud, air-gapped)
• Auditable (every read and write logged)
• Sub-150ms latency
• Plug-and-Play Integration
• Structured, Evolving Memory
• Unified Memory Layer
• Adaptive Conversations
• HIPAA-Grade Compliance
• Modular by Design
• Real-Time Recall
• Dynamic Learner Profiles
• Personalized Feedback
• Token-Efficient at Scale
• Easy Integration
• Curriculum-Aware Memory
• Scoped for Relevance
• Deal-Centric Memory
• Personalized Follow-Up Cues
• Cross-Team Continuity
• Custom Signal Capture
• CRM-Native API
• Full Issue History
• Early Pattern Detection
• Seamless Multi-Channel Context
• Personalized Responses
• Time-Aware Memory
• Drop-In Ready

Use cases

• Smart Patient Care Assistant (Healthcare)
• Chronic Condition Companion (Healthcare)
• Therapy Progress Tracker (Healthcare)
• Adaptive Learning Tutor (Education)
• Sales Assistant with Persistent Context (Sales & CRM)
• Customer Support Agents (Customer Support)
• Building personalized AI agents
• Adding memory to agent frameworks, coding tools, and voice pipelines
• Building conversational AI agents
• Building memory-enabled voice assistants
• Building customer support agents with LangGraph
• Building vision coding agents with persistent memory

AI approach

Mem0 provides a memory layer for AI agents and applications, allowing them to retain context across sessions and agents. It uses a Memory Compression Engine to condense chat history, extracts and updates memories, and retrieves key memories. The company emphasizes its research in areas like single-pass hierarchical distillation and multi-signal retrieval, benchmarked across LoCoMo, LongMemEval, and BEAM. It aims to make persistent memory a standard infrastructure for AI agents.

Tech named: Memory Compression Engine, Single-pass hierarchical distillation, Multi-signal retrieval, LoCoMo, LongMemEval, BEAM, Graph memory (entity linking)

Industries served

• Healthcare
• Education
• E-commerce
• Customer Support
• Sales & CRM

What it says sets it apart

• AI memory that persists across sessions and agents
• Drop-in memory infrastructure for AI agents and apps
• Context that persists, built for production
• Memory Compression Engine for token and latency reduction
• No pipeline changes required for persistent memory
• Automatic extraction and updating of memories
• Benchmarked across LoCoMo, LongMemEval, and BEAM
• Designed for enterprise with governance, reliability, and observability
• SOC 2 Type 1 and HIPAA compliant
• Portable across various deployment environments (Kubernetes, private cloud, air-gapped)
• Auditable logs for every read and write
• Universal, self-improving memory layer for LLM applications
• Instant memory for LLMs – better, cheaper, personal
• Memory can run where patient data already lives - no vendor lock-in
• Adapts to each student's pace and learning style
• Tracks every interaction, objection, and milestone in sales
• Remembers past conversations, problems, and resolutions in customer support
• Carries memory across chat, email, and phone interactions for customer support

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.