Recalld vs Recurse
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Recalld compared with Recurse
| Detail | Recalld | Recurse |
| What it does | The memory layer for AI Agents
| Build and deploy Agent MCP Apps
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| Stage | Growing
| Growing
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| Founded | Not given
| Not given
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| Based in | Not given
| Not given
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| Pricing model | freemium
| usage-based
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| Pricing | Not given
| Not given
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| Key features | - Long-term memory for AI agents
- Automatic fact extraction and reconciliation
- Curated recall retrieval
- Raw vector search capability
- Model Context Protocol integration
- Regional data residency
- End-to-end encryption
- Right to erasure
- Bring-your-own-key model support
- Recalld Chat assistant
| - Run agents with Python tools
- Deploy agents as MCP servers
- Validate structured inputs and outputs
- Inspect run results and artifacts
- Secrets management
- 24-hour result retention
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| What makes it different | - Curated recall uses 6.7× fewer tokens than raw search while maintaining accuracy
- Automatic fact updating and reconciliation without manual deduplication pipelines
- Verified on open benchmark maintained by independent company
- Native MCP server requiring no SDK or glue code
- GDPR-compliant by design with transparent data handling
| - No subscription or idle charges for usage-based pricing
- Zero markup on model tokens
- Hosting included with deployment
- Available through MCP clients like Claude and Codex
- Full visibility into agent execution and results
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| Free plan | Not given
| Not given
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| Open source | Not given
| Not given
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| Platforms | Not given
| Not given
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| Public API | Not given
| Not given
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| Community votes | 0 | 0 |
| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 35 ● no change since last check |
| Trust Flow (Majestic) | 0 | 11 |
| Citation Flow (Majestic) | 15 | 20 |
| Referring domains (Majestic) | 10 | 39 |
5 details filled in for both companies.
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