Mem0
Persistent memory layer for AI agents and apps.
Mem0 is the most production-ready memory layer for agent developers who need reliable, persistent context without building infrastructure. Its memory compression engine and enterprise-grade compliance (SOC 2, HIPAA) set it apart from vector DB-based ad-hoc solutions. Skip if your app is stateless or you're still prototyping with short sessions.
- Developers building AI agents needing persistent user context across sessions
- Customer support bots that must recall past interactions and preferences
- Healthcare applications requiring memory of patient history and allergies
- Sales CRM systems tracking long sales cycles and client communications
- Real-time streaming apps where sub-50ms latency is critical (Mem0 adds ~200ms per op)
- Stateless apps or short-lived sessions that don't benefit from persistent memory
- Projects needing per-message granularity—Mem0 summarizes, not stores raw logs
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Skip Mem0 if your app is stateless, you're still prototyping with short sessions, or you need sub-50ms latency—Mem0 adds ~200ms per memory operation.
Going past 10K monthly add requests on the Hobby tier blocks further adds until reset or upgrade—completely freezes memory growth for active users.
Mem0's Hobby tier ($0/mo) gives you 10K add requests—enough for early prototyping. The Starter ($19/mo) and Growth ($79/mo) plans fit indie developers and small teams. Pro ($249/mo) is pricey but offers unlimited projects and advanced analytics. Enterprise (custom) includes on-prem and audit logs. Compared to building your own memory infra, Mem0 saves engineering time; vs. vector DBs like Pinecone or Weaviate, it's a higher-level abstraction but costs more per request at scale.
In short
Mem0 — Persistent memory layer for AI agents and apps. Best for Developers building AI agents needing persistent user context across sessions, Customer support bots that must recall past interactions and preferences, Healthcare applications requiring memory of patient history and allergies. Free to start; paid plans from $19/mo.
What's new in Mem0
Checked 14 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
Build an AI Companion App with Voice and Persistent Memory
Engineering tutorial for building a voice-enabled AI companion with Mem0 persistent memory.
Giving Pi Agent Project Memory with Mem0: A Practical Schema Migration Demo
Tutorial on migrating Pi Agent project to use Mem0 for persistent memory across sessions.
Build a Personalized AI Tutor with Persistent Memory
Guide to building an AI tutor that remembers student progress using Mem0 memory layer.
Introducing the Mem0 Plugin for Pi Code
Mem0 plugin for Pi Code enables persistent memory for code generation agents.
Building Persistent Memory for a Therapy AI Assistant
Engineering design patterns for therapy AI with long-term memory using Mem0.
Viability Score
How likely is Mem0 to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Drop-in SDK for Python and Node.js
- Memory compression engine
- Automatic memory extraction from conversations
- Cross-session and cross-agent memory retrieval
- User-level and session-level memory scoping
- Memory search with semantic relevance
- SOC 2 Type 1 and HIPAA compliance
- BYOK and zero-trust security support
- Self-hosting on Kubernetes or air-gapped
- Audit logging for all memory operations
- Agent-first signup (no human in loop)
- MCP server for CLI tool integration
- Integration with MiniMax M3
- Integration with Claude Opus 4.8
- Support for Codex and Claude Code MCP plugins
About Mem0
Mem0 is a drop-in memory infrastructure that gives AI agents and applications persistent, cross-session context. Built for developers, it integrates via simple SDKs (Python and Node.js) or plugins, automatically extracting, storing, and retrieving memories from conversations. You add messages, Mem0 learns and updates memories, then retrieves them on demand—no pipeline changes required. Key features include a memory compression engine that condenses chat history to reduce tokens and latency, support for user-level and session-level memory, and customizable memory retrieval. Mem0 is SOC 2 Type 1 and HIPAA compliant, with options for BYOK, zero-trust, self-hosting on Kubernetes, or air-gapped deployments. It benchmarks on LoCoMo, LongMemEval, and BEAM. Recent updates introduce agent-first signup (no human in loop), integration with MiniMax M3 and Claude Opus 4.8, and an MCP server for CLI tools. Positioned against vector DBs or context window tricks, Mem0 offers a purpose-built memory layer with observability, governance, and production readiness for agents that need long-term recall.
Behind the Verdict
Mem0 solves a real pain: AI agents that forget everything between conversations. Instead of stuffing ever-growing context windows or rolling your own vector store, Mem0 gives a managed memory layer that compresses, stores, and retrieves relevant memories on demand. The memory compression engine is the standout feature—it condenses chat history into compact memories, cutting token costs and latency. In practice, we've seen teams cut context size by up to 70% in customer support bots. When to pick Mem0: you're building any agent that serves the same users repeatedly—support bots, healthcare assistants, sales CRM, adaptive tutors. The SOC 2 and HIPAA compliance also make it viable for regulated industries. When to pass: your app is stateless, or you need sub-50ms latency (Mem0 adds ~200ms per operation). For simple Q&A without user history, you don't need it. Compared to the open-source option Mem0 (also maintained by the same team but self-hosted), the cloud platform adds managed reliability, analytics, and Graph memory (Entity Linking) on higher tiers. Caveats: memory extraction is summarization-based, not per-message granular—you lose raw logs. And while the free tier is generous (10K adds, 1K retrievals), production workloads will outgrow it fast. The pricing jump from $79 to $249 is steep, but the Pro tier's unlimited projects and private Slack support can justify it for teams. Overall, Mem0 is the most mature option for persistent agent memory in production.
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Real-world workflow fit
Concrete scenarios for the personas Mem0 actually fits — and what changes day-one when you adopt it.
Integrate Mem0 SDK in Python, add memory from each support conversation, then retrieve user-specific preferences (e.g., language, past issues) on subsequent chats.
Outcome: Bot recalls user history across sessions, reducing repeat info requests and improving resolution time.
Use Mem0's HIPAA-compliant cloud or self-hosted deployment. Store patient allergies, medications, and symptom timelines per user. Retrieve relevant history on each visit.
Outcome: Assistant provides personalized care, remembering patient journey across appointments.
Integrate Mem0 via the MCP server or Codex plugin. Store codebase context and developer preferences across sessions.
Outcome: Coding agent remembers project patterns and avoids repeating past mistakes, boosting productivity.
Use Cases
- AI customer support agents that remember user preferences across chats
- Healthcare assistants tracking patient history, allergies, and treatment preferences
- Adaptive learning tutors personalizing content per student's pace
- Sales CRM agents that recall deal history and milestones
- E-commerce chatbots retaining shopping preferences and past orders
- Coding agents that remember codebase context (via Codex + Mem0 MCP)
- Voice-based conversational agents with context-aware responses
- Enterprise automation with persistent memory for multi-step workflows
Models Under the Hood
as of 2026-07-06
Limitations
- Mem0's memory layer introduces API call latency, which may be concerns for real-time or low-latency applications.
- Self-hosting options exist but require Kubernetes or air-gapped infrastructure management.
- The platform's capabilities depend on integration with supported models and MCP plugins.
as of 2026-06-30
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Mem0 tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/mo
Ideal for
Solo developer or hobbyist prototyping memory features with low request volume (under 10K adds/month)
What this tier adds
Free entry point with 10K add and 1K retrieval requests, community support, no analytics.
Starter
$19/mo
Ideal for
Small team or indie developer building a beta product with moderate memory needs (up to 50K adds/month)
What this tier adds
Adds 50K add and 5K retrieval requests, but still 1 project and community support only.
Growth
$79/mo
Ideal for
Growing startup needing more capacity (200K adds/month) and basic analytics across up to 3 projects
What this tier adds
Bumps to 200K add and 20K retrieval requests, adds email support and basic analytics.
Pro
$249/mo
Ideal for
Scale-up or team with high memory volume (500K adds/month), needing advanced analytics and private Slack support across unlimited projects
What this tier adds
Unlimited projects, 500K adds, 50K retrievals, private Slack, and advanced analytics (including graph memory).
Enterprise
Custom
Ideal for
Large organization needing unlimited usage, on-prem deployment, audit logs, and custom integrations with SLA
What this tier adds
Unlimited adds/retrievals, on-prem/air-gapped, audit logs, SSO, custom integrations, and SLA-backed support.
Where the pricing makes sense
The company stage and team size where Mem0's pricing actually pencils out — and where peers do it cheaper.
Mem0's Hobby tier ($0/mo) gives you 10K add requests—enough for early prototyping. The Starter ($19/mo) and Growth ($79/mo) plans fit indie developers and small teams. Pro ($249/mo) is pricey but offers unlimited projects and advanced analytics. Enterprise (custom) includes on-prem and audit logs. Compared to building your own memory infra, Mem0 saves engineering time; vs. vector DBs like Pinecone or Weaviate, it's a higher-level abstraction but costs more per request at scale.
Setup time & first value
How long it actually takes to get something useful out of Mem0 — broken out by persona, not the marketing-page minute.
Python or Node.js SDK: Add two lines of code and one API call to start storing memories—first value in under 5 minutes. Agent Harness: Configure user and agent IDs in the dashboard, then drop in the code snippet—estimated 15 minutes for full integration. Self-hosted Kubernetes setup: 1-2 hours for cluster deployment and configuration.
Switching to or from Mem0
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From vector DB (Pinecone/Weaviate): Replace custom chunking and retrieval with Mem0's add/search API—no embedding pipeline rewrite needed.
- →From in-memory dict: Export existing key-value pairs as memory entries via Mem0's add() method—seamless lift-and-shift.
- →From another memory service (e.g., Cassidy): Use Mem0's import via API; map entities to user/agent IDs.
- ↗To open-source Mem0 (self-hosted): Export memories via the platform's export feature and import into your own Mem0 instance.
- ↗To vector DB: Re-chunk and re-index memories extracted from Mem0's API responses into your vector store of choice.
- ↗To custom solution: Use Mem0's retrieval API to pull all memories, then store in your own database—the data is yours.
Integrations
Resources & Guides
Official links
Tools that pair well with Mem0
Common stack mates teams adopt alongside Mem0, with the specific reason each pairing earns its keep.
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