Mem0
A persistent memory layer that enables AI agents to retain and learn from interactions across sessions and tools
- Also known as
- mem0
What is Mem0?
Mem0 provides AI agents and LLMs with durable memory that persists across conversations, reducing redundant context and token costs. It stores facts, preferences, and events keyed by users or agents, enabling personalized, context-aware interactions that improve over time.
Mem0 pricing
We don't have Mem0's full plan breakdown yet (its pricing page resisted automated reading). Here's what we could confirm. Always check live pricing for exact numbers.
Free tier available; paid plans exist but specific pricing not disclosed in provided text
What Mem0 does
The capabilities that matter for agent memory, normalised so it lines up with every alternative. “-” means we haven't confirmed it, not that it's missing.
- Architecture model
- Open source self hosted
- Graph based entity relationship extraction
- ✓
- Automatic context window rolling summarization
- ✓
- Semantic vs episodic event chronological memory
- ✓
- Cross session and multi thread user persistence
- ✓
- Memory conflict resolution and fact updating
- -
- Multi tenant user and agent isolation
- ✓
- Native langchain and llamaindex memory classes
- -
- Time decay and recency weighted retrieval
- ✓
- Zero data retention and pii redaction layer
- -
- SOC2 type ii
- ✗
- Mit or apache permissive oss license
- -
- Pricing model
- -
Platform & deployment
Independently observed- CLI
- Web
- Cloud / SaaS
- Air-gapped
- Self-hosted
Integrations (16)
Independently observed- Slack
- Claude Code
- Linear
- Zapier
- n8n
- ChatGPT
- Perplexity
- Vercel AI SDK
- DeepSeek
- OpenAI
- HuggingFace
- Cursor
- Windsurf
- Codex
- OpenCode
- OpenClaw
Mem0 alternatives
Other agent memory we track, ranked by the same independent score.
Compare Mem0
Side by side against other agent memory, attribute by attribute, with a source on every value.
The Vioscale score: one lens on the evidence
Not user reviews and not a paid placement: a confidence-weighted blend of the independent signals below (adoption, activity, security posture, and more), which you can sort and re-weight yourself. Vendors can correct their listing but can never move their rank, and stars are weighted low as a vanity metric. It is one way to read the evidence for Mem0, not the verdict.
| Signal | Score | Weight | Contribution | Evidence |
|---|---|---|---|---|
| Price level | 80 | 0.05 | 4.2 | ✓ |
| Capabilities | 81 | 0.05 | 4.0 | ✓ |
| Security posture | 55 | 0.07 | 4.0 | ✓ |
| Pricing transparency | 25 | 0.08 | 2.1 | ✓ |
| Integrations | 35 | 0.04 | 1.4 | ✓ |
| Reliability | 0 | 0.07 | 0.0 | - |
Computed . Re-weight it by intent, or see the full method.
All data & sourcesshow ↓
Every value we hold, with its source, retrieval date, and confidence. This is the evidence behind the score: don't trust it, verify it.
Features
| Attribute | Value | Evidence |
|---|---|---|
| Capabilities | Soc2 type ii: No · Architecture model: open_source_self_hosted · Multi tenant user and agent isolation: Yes · Time decay and recency weighted retrieval: Yes · Graph based entity relationship extraction: Yes · Automatic context window rolling summarization: Yes | mediumsource · 2026-08-25 · 60% |
Integrations
| Attribute | Value | Evidence |
|---|---|---|
| Count | 16 | mediumsource · 2026-08-25 · 60% |