Mem0 vs MemOS

Mem0

Agents

MemOS

Agents

Tagline

Mem0

Persistent memory layer for AI agents and LLM apps

MemOS

—

Pricing

Mem0

Freemium· Free tier

MemOS

Freemium· Free tier

Free trial

Mem0

Yes

MemOS

Yes

API

Mem0

Yes

MemOS

Yes

Platforms

Mem0

—

MemOS

windowsmacoslinux

Open source

Mem0

Yes

MemOS

Yes · Apache-2.0

GitHub stars

Mem0

—

MemOS

12,162

checked 2026-10-29

Last GitHub push

Mem0

—

MemOS

2026-10-29

First commit

Mem0

—

MemOS

2025-07

Hacker News stories

Mem0

4

checked 2026-10-16

MemOS

0

checked 2026-10-16

Earliest Wayback capture

Mem0

2024-08-01

earliest archived capture of the site, not a launch date

MemOS

2025-07-07

earliest archived capture of the site, not a launch date

Company

Mem0

—

MemOS

MemTensor

Model used

Mem0

Model-agnostic — pluggable via OpenAI, Anthropic, Gemini, Ollama, LiteLLM (no in-house LLM)

MemOS

Multi-model

Best for

Mem0

Teams shipping stateful AI agents, copilots, or long-running chat apps where cross-session recall of user facts, preferences, and history is a product requirement rather than a nice-to-have.

MemOS

—

Not for

Mem0

One-off single-turn chatbots, stateless RAG-over-docs apps, or hobby projects where a short conversation buffer inside the prompt is already enough — the extra infra is not worth it.

MemOS

—

Editorial score

Mem0

—

MemOS

6.9 / 10

Use cases

Mem0

Long-lived customer support copilotsPersonal AI companions and journaling agentsHealthcare intake and follow-up assistantsSales and CRM enrichment agentsEducation tutors with per-student progressMulti-session coding agentsAutonomous research agentsE-commerce shopping assistants

MemOS

AI assistants with consistent, context-rich conversationsCustomer support that recalls past tickets and user historyPersonalized agents that adapt to individual preferencesMulti-agent collaboration with shared or isolated memoryIntelligent application ecosystems

Pros

Mem0

• Open-source core with a permissive Apache-2.0 license and a large community — you can self-host end to end if you don't want a managed dependency
• Model-agnostic and vector-store-agnostic: works with OpenAI, Anthropic, Gemini, Ollama, Qdrant, Pgvector, Chroma, Weaviate, Neo4j and more via drop-in providers
• Genuine token savings — the hierarchical distillation writes compact facts instead of raw transcripts, which shrinks retrieval prompts and speeds up long-lived agents
• Both hosted and self-hosted paths, so you can prototype on the free tier and later move to Kubernetes / air-gapped / on-prem without rewriting
• Enterprise-grade governance on paid tiers: SOC 2 Type I, HIPAA, audit logs, SSO, project isolation — rare in the memory-layer space
• Simple, well-documented Python and TypeScript SDKs with a small surface (add / search / update / delete) that drops into existing agent frameworks

MemOS

• Open-source core available under the Apache 2.0 License.
• Structured memory architecture with dynamic retrieval and updates.
• Cross-model and cross-device memory sharing via MIP.
• Flexible deployment options including public cloud, private cloud, on-premises, and hybrid.
• Scalable memory infrastructure designed for high availability.

Cons

Mem0

• Extraction quality is only as good as the LLM you point it at — cheap models produce shallow or noisy memories and can miss nuance
• Managed pricing scales quickly if your agent writes memory aggressively; the $19 Starter's 50k add-cap is easy to blow past on chatty workloads
• Adds an extra hop and its own vector/graph infra to reason about — for small single-session chatbots it is real overkill
• Graph memory features are newer and less battle-tested than the flat vector-memory path; expect some rough edges on complex ontologies
• Self-hosted setup with graph + vector + LLM providers has meaningful ops surface if you were hoping for a one-line install

MemOS

—

Website

Mem0

mem0.ai

MemOS

memos.openmem.net

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.