| What it does | One memory, every AI tool. Write once, recall anywhere.
| Interactive worlds for people and AI to build, learn and solve problems together
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| Stage | Not given
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| Founded | Not given
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| Based in | Not given
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| Pricing model | Freemium
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| Pricing | Free: $0 with 1,000 queries/day, 10,000 atoms, 60 req/min. Pro: $29/mo with 50,000 queries/day, 500,000 atoms, 600 req/min. Team: $149/mo with 500,000 queries/day, 5,000,000 atoms, 3,000 req/min. Enterprise: Custom limits and pricing.
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| Key features | - Persistent cross-tool memory API working with Claude, Cursor, VS Code, ChatGPT, Python, and any HTTP client
- Hebbian associations that strengthen links between concepts recalled together
- Outcome feedback that re-ranks recall based on usefulness
- Automatic query expansion along learned associations
- Python SDK with typed errors for write, read, feedback, and stats
- REST API for any HTTP client
- Per-tenant org isolation and SHA-256 hashed API keys
| - Photo to brick set conversion with 3D preview and parts list
- Workbench for collaborative brick-by-brick editing
- MCP server integration with Claude Code, Codex, Cursor and more
- Build structure checking and validation
- OAuth sign-in with no token storage
- Offline build access with local browser storage
- Export digital manuals and wanted parts lists
- Interactive environments for AI training
- Consent-based human demonstration recording
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| What makes it different | - Learns from outcomes and improves over time, unlike vector databases that only perform similarity search
- Works across every AI tool with a single API key, not locked to one platform
- Complementary to RAG: remembers outcomes and lessons, not just document similarity
- Free tier with full API access and MCP server, no credit card required
| - Checked outcomes - part connections and build validity are verified by software before delivery
- Real objects from photos - converts pictures into buildable brick sets with actual parts that exist
- People and AI work on the same design simultaneously within the workbench
- Measurable AI learning with transfer testing across different worlds
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| Free plan | Not given
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| Open source | Not given
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| Platforms | Not given
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| Public API | Not given
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 40 ▼ −1 (down) since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 7 | 0 |
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| Citation Flow (Majestic) | 37 | 13 |
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| Referring domains (Majestic) | 200 | 2 |
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Mnemoverse