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AI infrastructure · Tool
Mem0
ResearchedDrop-in memory infrastructure for AI agents and apps that adds persistent context across sessions. Offers Python and Node.js SDKs, 25+ integrations, SOC-2/HIPAA compliance, and vector-plus-graph retrieval.
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In one minute
Start here for the decision-making essentials: what Mem0 does, who it is for, how it is accessed, and the first-party sources behind this profile.
Pricing2 options
Custom pricing available with the ability to book a call and a 'Start Free' option
Free tier available
Platforms
Python SDK / Python integration
API accessNot public
FoundedNot disclosed by source
AvailabilityWeb / remote
LicenseProprietary
Best suited to
Source-backed fit Developers building AI agents that need persistent cross-session memory Enterprise teams deploying memory infrastructure at scale Teams building customer support AI assistants across chat, email, and phone Engineers integrating with LangChain, CrewAI, OpenAI Agents SDK, Vercel AI… Developers building AI agents Used by 100,000+ developers
Decision support
6 sourced answersCommon questions and adoption checks
Short answers to the questions buyers and builders commonly ask about Mem0. Each answer cites the shared ledger below, where every source is listed once.
01What does Mem0 say it can do?
AI memory that persists across sessions and agents · Mem0 provides drop-in memory infrastructure for AI agents and apps, with persistent contex · Mem0 provides persistent long-term memory for AI agents · Mem0 serves as a dedicated memory layer that adds statefulness to stateless API-based agen
AI memory that persists across sessions and agents
02Who is Mem0 intended for?
Developers building AI agents · Used by 100,000+ developers · Enterprise teams needing memory infrastructure for AI agents · Developers and enterprises building agents and personalized AI applications
DEVELOPERS PRICING USECASES RESOURCES DOCS
03What use cases does Mem0 describe?
AI agents and apps that learn from past user interactions · Building production stateful agents on top of the OpenAI Responses API · Customer support AI memory layer for agents · Persistent memory for customer service chatbots handling billing questions, support ticket
Mem0 enables AI agents & apps to continuously learn from past user interactions, enhancing their intelligence and personalization.
04What should teams verify before adopting Mem0?
Most current AI agents lack proactive memory: retrieval only occurs after the user sends a
Most AI agents only retrieve memory when asked
05What pricing information is available for Mem0?
Custom pricing available with the ability to book a call and a 'Start Free' option · Free tier available
Custom Pricing Book a Call with us
06What integrations does Mem0 document?
Installable via pip as mem0ai · Mem0 integrates with the OpenAI Responses API · Mem0 integrates with LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Vercel AI SDK, Claud · Mem0 offers more than 25 integrations across agent frameworks, coding tools, and voice pip
#pip install mem0ai
Decision guide
AI infrastructureCapabilities and operating fit
This profile connects the jobs Mem0 is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
Common use cases
- Persistent memory layer for OpenAI Responses API agents
- Customer service chatbots handling billing, tickets, and account changes
- Multi-channel customer support with carried context across chat, email, and phone
- Agentic applications requiring identity-aware, time-aware memory storage
- Cloud-managed memory for ChatDev multi-agent workflows via YAML
- AI agents and apps that learn from past user interactions
Topics mapped
Verified capabilities
- AI memory that persists across sessions and agents
- Mem0 provides drop-in memory infrastructure for AI agents and apps, with persistent contex
- Mem0 provides persistent long-term memory for AI agents
- Mem0 serves as a dedicated memory layer that adds statefulness to stateless API-based agen
- Provides persistent memory across chat, email, and phone interactions
- Sub-150ms latency for real-time interactions
- Adds persistent memory to AI agents with a single drop-in integration.
- Provides a dedicated memory layer that stores and retrieves customer context for AI agents
Recorded integrations
Installable via pip as mem0aiMem0 integrates with the OpenAI Responses APIMem0 integrates with LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Vercel AI SDK, ClaudMem0 offers more than 25 integrations across agent frameworks, coding tools, and voice pipProvides an integration with AWS Bedrock using OpenSearch Service for cloud-native persistOffers a Claude Code (and Claude Cowork) plugin via MCP server, lifecycle hooks, and SDK sIntegrates with the OpenAI API for language generationIntegrates with Claude Code to add persistent memoryIntegrates with Ollama to build local coding agents with persistent memoryIntegrates with xAI's Grok API as an external memory layer
Intended audiences
Developers building AI agentsUsed by 100,000+ developersEnterprise teams needing memory infrastructure for AI agentsDevelopers and enterprises building agents and personalized AI applicationsDevelopers building multi-turn Claude agentsMem0 targets developers building AI agents.Developers building with LLMs who need to manage token and API costs
Access signals
- Pricing model
- Custom pricing available with the ability to book a call and a 'Start Free' option · Free tier available
- API
- Not publicly listed
- Source links
- 16 recorded
Source-backed
Updated July 22, 2026Verified facts
Each fact points to a recorded source, making it easy to distinguish verified product information from claims that need checking.
Mem0 - AI Memory Layer for your Agents & Apps | Persistent Context
Drop-in memory infrastructure for AI agents and apps with persistent context · Built for production · AI memory that persists across sessions and agents
AI memory that persists across sessions and agents
AI agents and apps that learn from past user interactions
Python and Node.js SDKs
Installable via pip as mem0ai
Built for production
View 32 more verified facts
Backed by Combinator
Mem0 provides drop-in memory infrastructure for AI agents and apps, with persistent context built for production.
On-prem deployment is offered on the Enterprise plan and is not available on Hobby, Starter, or Pro tiers.
Pro includes private Slack support, while Enterprise adds SLA support on top of private Slack; Hobby and Starter tiers use community support only.
Mem0 provides persistent long-term memory for AI agents
Mem0 integrates with the OpenAI Responses API
Building production stateful agents on top of the OpenAI Responses API
Mem0 serves as a dedicated memory layer that adds statefulness to stateless API-based agent workflows
Developers building AI agents
Mem0 documentation/examples are provided in Python
Customer support AI memory layer for agents
Provides persistent memory across chat, email, and phone interactions
SOC-2 and HIPAA compliant with secure storage
Sub-150ms latency for real-time interactions
Used by 100,000+ developers
Adds persistent memory to AI agents with a single drop-in integration.
Mem0 integrates with LangGraph, LangChain, CrewAI, OpenAI Agents SDK, Vercel AI SDK, Claude Code, Cursor, LiveKit, ElevenLabs, and AWS Bedrock, among others.
Mem0 offers more than 25 integrations across agent frameworks, coding tools, and voice pipelines.
Provides an integration with AWS Bedrock using OpenSearch Service for cloud-native persistent semantic memory storage.
Offers a Claude Code (and Claude Cowork) plugin via MCP server, lifecycle hooks, and SDK skill.
Provides cloud-managed persistent memory, including for ChatDev multi-agent workflows configured via YAML.
Provides a dedicated memory layer that stores and retrieves customer context for AI agents
Supports identity-aware storage and retrieval, flexible schemas, time-aware behavior (prioritizing recent events), and fine-grained control over what is stored, updated, or forgotten
Persistent memory for customer service chatbots handling billing questions, support tickets, account changes, and multi-session context continuity
Integrates with the OpenAI API for language generation
Python SDK / Python integration
Stores structured memory objects for customer service including customer profiles, interaction history, operational state, and soft signals
Persistent memory layer for AI agents
SOC 2 (Type 1) and HIPAA compliant, with BYOK and zero-trust governance
Kubernetes, private cloud, or air-gapped deployment with a single API
Enterprise teams needing memory infrastructure for AI agents
Adding persistent memory to agentic applications
Practical capabilities
8 documented areasWhat it helps with
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
Persistent memory layer for OpenAI Responses API agents
Customer service chatbots handling billing, tickets, and account changes
Multi-channel customer support with carried context across chat, email, and phone
Agentic applications requiring identity-aware, time-aware memory storage
Cloud-managed memory for ChatDev multi-agent workflows via YAML
AI agents and apps that learn from past user interactions
Building production stateful agents on top of the OpenAI Responses API
Customer support AI memory layer for agents
Availability
Source checkedWhere it runs and where to get it
Documented product formats, platforms and official distribution destinations. Availability can vary by region and plan.
Cost / license
Custom pricing available with the ability to book a call and a 'Start Free' option · Free tier available
Platforms
Python and Node.js SDKsPython SDK / Python integration
Adoption notes
DeploymentBuilt for production · On-prem deployment is offered on the Enterprise plan and is not available on Hobby, Starte · Provides cloud-managed persistent memory, including for ChatDev multi-agent workflows conf · Kubernetes, private cloud, or air-gapped deployment with a single API
LicenseNot disclosed by source
Model supportNot disclosed by source
Data controlSOC-2 and HIPAA compliant with secure storage · SOC 2 (Type 1) and HIPAA compliant, with BYOK and zero-trust governance · Production memory layer systems require compliance controls for GDPR, HIPAA, and SOC 2
Learning curveIntermediate
Primary use casesPersistent memory layer for OpenAI Responses API agents, Customer service chatbots handling billing, tickets, and account changes, Multi-channel customer support with carried context across chat, email, and phone, Agentic applications requiring identity-aware, time-aware memory storage, Cloud-managed memory for ChatDev multi-agent workflows via YAML, AI agents and apps that learn from past user interactions, Building production stateful agents on top of the OpenAI Responses API, Customer support AI memory layer for agents, Persistent memory for customer service chatbots handling billing questions, support ticket, Adding persistent memory to agentic applications, Graph-based conversational memory that stores context as nodes and relationships across se, Tracking entity relationships, preferences, and timelines for AI agents rather than simila, Building AI agents for customer service that retain customer context across interactions, Customer Support, Healthcare, Education, Sales & CRM, and E-Commerce, Replacing full conversation history sent into every LLM call to reduce token costs in prod, Persistent memory layer for multi-turn AI agents and LLM applications
What to verify before adopting
- Most current AI agents lack proactive memory: retrieval only occurs after the user sends a
Evolution and major updates
1 dated updateMem0 timeline
A concise history of software releases and material product changes. Events appear only when a dated source supports what changed.
Latest first · exact dates
Showing the newest updates and meaningful milestones. Open an entry for its summary and source.
Published engineering case study 'How Mem0 Cut Claude Code's Memory Footprint by 97%'
Open detailsMem0 published an engineering blog post on the Mem0 blog reporting results of two side-by-side Claude Code v2.1.209 experiments on the same repository, demonstrating that integrating Mem0 as a memory layer reduced memory footprint by 97% and enabled stated…
View source [17]
Citation ledger
17 unique pagesRecorded sources
Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
- 1mem0.ai 11 facts · 3 answers · Official site
- 2mem0.ai/blog/how-to-add-memory-to-openai-responses-api-agents 6 facts · 4 answers · Documentation
- 3mem0.ai/blog/customer-service-chatbots-with-persistent-memory-2 6 facts · 2 answers · Official site
- 4mem0.ai/custom-pricing 6 facts · 2 answers · Pricing
- 5mem0.ai/usecase/customer-support 5 facts · 3 answers · Official site
- 6mem0.ai/blog/proactive-memory-in-ai-agents-a-developer-s-guide 6 facts · 1 answer · Official site
- 7mem0.ai/blog/xai-grok-api-pricing 6 facts · 1 answer · Pricing
- 8mem0.ai/integrations 6 facts · 1 answer · Official site
- 9mem0.ai/about-us 5 facts · 1 answer · Official site
- 10mem0.ai/blog/ai-memory-layer-guide 6 facts · Official site
- 11mem0.ai/blog/openai-responses-api-and-realtime-agents-with-memory 6 facts · Documentation
- 12mem0.ai/blog 5 facts · Official site
- 13mem0.ai/blog/graph-memory-solutions-ai-agents 5 facts · Official site
- 14mem0.ai/blog/llm-api-cost-breakdown-claude-gemini-openai-compared 4 facts · Documentation
- 15mem0.ai/pricing 3 facts · 1 answer · Pricing
- 16mem0.ai/blog/anthropic-claude-pricing 2 facts · Pricing
- 17mem0.ai/blog/how-mem0-cut-claude-code-s-memory-footprint-by-97 1 milestone
Research status86 substantive facts · 16 source pages · quality score 98/100
Connected directory
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