Comparison
mem0 vs MemOS
Verdict
Pick mem0 if mem0 is a Python-based memory infrastructure for AI agents and applications, offering a persistent context layer suitable for production environments; pick MemOS if memOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks.
Markdown twin · mem0 alternatives · MemOS alternatives
GraphCanon updated Sep 20, 2026
19views this month
Trust & integrity
| Signal | mem0 | MemOS |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Aug 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- mem0
- Drop-in memory infrastructure for AI agents and apps
- MemOS
- Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse
Stars
- mem0
- 66k
- MemOS
- 11k
Forks
- mem0
- 7.7k
- MemOS
- 994
Open issues
- mem0
- 754
- MemOS
- 84
Language
- mem0
- Python
- MemOS
- TypeScript
Adopt for
- mem0
- mem0 is a Python-based memory infrastructure for AI agents and applications, offering a persistent context layer suitable for production environments.
- MemOS
- MemOS is a self-evolving operating system tailored for LSTM systems and AI agents, offering ultra-persistent memory, hybrid retrieval technologies, and skill reuse across tasks.
Persona
- mem0
- -
- MemOS
- -
Runtime
- mem0
- -
- MemOS
- -
License
- mem0
- mem0 is distributed under the Apache-2.0 license, allowing for broad usage and modification with attribution.
- MemOS
- MemOS is available under the Apache-2.0 license, allowing you to use it freely with certain conditions.
Last pushed
- mem0
- Sep 18, 2026
- MemOS
- Aug 18, 2026
Categories
- mem0
- AI Agents, Data & Retrieval
- MemOS
- AI Agents, Data & Retrieval
Trust and health
Open issues (now)
- mem0
- 754
- MemOS
- 84
Stars delta
- mem0
- +2.8k (30d)
- MemOS
- +497 (30d)
Open issues delta
- mem0
- +63 (30d)
- MemOS
- +4 (30d)
Full report
- mem0
- Trust report
- MemOS
- Trust report
Typed relationship
mem0 alternative MemOSBoth Mem0 and MemOS offer solutions for ultra-persistent memory needs in AI Agents and LLMs, positioning them as alternative tools.
Shared compatibility
- Node.js · mem0: Node.js runtime · MemOS: Node.js runtime
Choose mem0 if…
- mem0 is primarily Python; MemOS is TypeScript.
- Pricing: The core functionality of mem0 is free to use under the Apache-2.0 license, but additional features or support might come at a cost..
- Requirements: Min 2 GB RAM; Python environment is required for installation and use..
- Both Mem0 and MemOS offer solutions for ultra-persistent memory needs in AI Agents and LLMs, positioning them as alternative tools.
- Tags unique to mem0: agentic-memory, ai-agents, genai, python.
- When you need a persistent memory layer for AI agents that can be easily integrated into existing applications without significant changes.
When NOT to use mem0
- If your project is not in Python, as mem0 is specifically designed for Python environments.
- When you require a more customizable memory management system that allows for extensive configuration beyond what mem0 offers.
- If your application does not need a persistent memory layer or if the existing memory management solutions are sufficient.
Choose MemOS if…
- MemOS is primarily TypeScript; mem0 is Python.
- Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs..
- Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components..
- Both Mem0 and MemOS offer solutions for ultra-persistent memory needs in AI Agents and LLMs, positioning them as alternative tools.
- Tags unique to MemOS: agent, agentic-ai, llm.
- MemOS ships Docker support for self-hosted deployment.
- If you require ultra-persistent memory management within your application for long-term storage.
When NOT to use MemOS
- If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud).
- When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance.
- In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mem0ai/mem0) · observed Sep 20, 2026
- GitHub forks (mem0ai/mem0) · observed Sep 20, 2026
- Last push (mem0ai/mem0) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (MemTensor/MemOS) · observed Sep 20, 2026
- GitHub forks (MemTensor/MemOS) · observed Sep 20, 2026
- Last push (MemTensor/MemOS) · observed Aug 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mem0 66k · MemOS 11k (synced Sep 20, 2026).
Common questions
- What is the difference between mem0 and MemOS?
- mem0: Drop-in memory infrastructure for AI agents and apps. MemOS: Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse. See the comparison table for live GitHub stats and shared categories.
- When should I choose mem0 over MemOS?
- Choose mem0 over MemOS when mem0 is primarily Python; MemOS is TypeScript; Pricing: The core functionality of mem0 is free to use under the Apache-2.0 license, but additional features or support might come at a cost.; Requirements: Min 2 GB RAM; Python environment is required for installation and use.; Both Mem0 and MemOS offer solutions for ultra-persistent memory needs in AI Agents and LLMs, positioning them as alternative tools; Tags unique to mem0: agentic-memory, ai-agents, genai, python; When you need a persistent memory layer for AI agents that can be easily integrated into existing applications without significant changes.
- When should I choose MemOS over mem0?
- Choose MemOS over mem0 when MemOS is primarily TypeScript; mem0 is Python; Pricing: Pricing details are not clearly specified in the repository data, but the self-hosted option provides flexibility at the expense of managing infrastructure costs.; Requirements: Requires Docker; Self-hosting MemOS typically requires setting up Neo4j and Qdrant. The cloud API or OpenClaw Cloud Plugin options do not require these components.; Both Mem0 and MemOS offer solutions for ultra-persistent memory needs in AI Agents and LLMs, positioning them as alternative tools; Tags unique to MemOS: agent, agentic-ai, llm; MemOS ships Docker support for self-hosted deployment; If you require ultra-persistent memory management within your application for long-term storage.
- When should I avoid mem0?
- If your project is not in Python, as mem0 is specifically designed for Python environments. When you require a more customizable memory management system that allows for extensive configuration beyond what mem0 offers. If your application does not need a persistent memory layer or if the existing memory management solutions are sufficient.
- When should I avoid MemOS?
- If you prefer fully managed solutions and have no preference over where your data is stored (hosted in MemOS Cloud). When minimal setup overhead is a critical requirement since self-hosting MemOS necessitates Neo4j and Qdrant for optimal performance. In scenarios preferring simpler on-device solutions as MemOS could be more complex to set up compared to other lightweight plugins or services.
- Is mem0 or MemOS more popular on GitHub?
- mem0 has more GitHub stars (65,557 vs 10,758). Stars measure visibility, not whether either tool fits your constraints.
- Are mem0 and MemOS open source?
- Yes - both are open-source projects on GitHub (mem0: Apache-2.0, MemOS: Apache-2.0).
- Where can I find alternatives to mem0 or MemOS?
- GraphCanon lists graph-backed alternatives at mem0 alternatives and MemOS alternatives (mem0 markdown twin, MemOS markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, mem0 or MemOS?
- mem0: Very active. MemOS: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for mem0 and MemOS?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: mem0 trust report; MemOS trust report.