
Overall Score
8
of AI companies
by Mnemom🇺🇸
Your AI agents are making decisions you can
Mnemom is the verification layer for AI agents — identity, integrity analysis, cryptographic proof, reputation scoring, risk assessment, and containment in a single stack. Point your agent at the gateway (one environment variable, zero code changes) and every action gets traced, analyzed, and cryp...

Overall Score
8
of AI companies
Overall Score
8
GitHub Stars
—
Monthly Visits
1K
Community Rating
5.0
Mnemom is the verification layer for AI agents — identity, integrity analysis, cryptographic proof, reputation scoring, risk assessment, and containment in a single stack. Point your agent at the gateway (one environment variable, zero code changes) and every action gets traced, analyzed, and cryp...
Latest detected signals across traffic, developer activity and community mentions.
Indexed by indexator.ai
| Keyword | Volume / Mo | CPC |
|---|---|---|
| kohärenzprüfungen | 80 | — |
| ai being melancholy | 10 | — |
| melancholy being ai | 10 | — |
Developer
—
Team
79 USD
Enterprise
—
Free trial available
Mnemom is the verification layer for AI agents — identity, integrity analysis, cryptographic proof, reputation scoring, risk assessment, and containment in a single stack. Point your agent at the gateway (one environment variable, zero code changes) and every action gets traced, analyzed, and cryp...
Get notified when score, traffic, GitHub or community signals change.
Latest detected signals across traffic, developer activity and community mentions.
Indexed by indexator.ai
Six signals, each 0–100, blended into the overall by their listed weight.
Generated from public signals and search intent.
Each dimension is weighted (traffic 20% · community 25% · growth 15% · momentum 15% · innovation 10%) and combined into the overall.
| Keyword | Volume / Mo | CPC |
|---|---|---|
| kohärenzprüfungen | 80 | — |
| ai being melancholy | 10 | — |
| melancholy being ai | 10 | — |
Developer
0.01 USD
Team
79 USD
Saves
14.7K
Rating
5.0
2 reviews
Views
700
Category
Agent integrity monitoring
Pros · 17
Cons · 5
Rating distribution
User reviews · 1
We built @Mnemom because we were shipping AI agents into production and couldn't answer a basic question: how do you prove this thing is doing what you told it to? Not monitor. Prove. So we built the proof layer. Cryptographic attestations, trust scores, risk assessment, containment — everything you need to deploy agents you can actually stand behind. It's free to start and open source. We'd love to hear what you think - new features shipping daily.
Supported features
Release history · 2+
- Trust Score™: Bond-rating-style scores (AAA–CCC) for every AI agent, computed from five weighted signals: integrity, compliance, drift, trace quality, and coherence - Zero-knowledge proofs: Every agent action gets a cryptographic attestation chain — Ed25519 signatures, SHA-256 hash chains, Merkle trees, SP1 STARK proofs - Team risk engine: CoVaR, Markowitz portfolio theory, and DebtRank applied to multi-agent fleets — know which agent is dragging quality down - Real-time containment: Pause, kill, or resume agents automatically when trust drops below threshold Smoltbot gateway: One environment variable, zero code changes — supports Anthropic, OpenAI, and Gemini - Open protocols: AAP (behavioral contracts) and AIP (runtime analysis) on npm and PyPI
Initial release of Mnemom.
Q&A · 30
Mnemom's main function is to develop a comprehensive trust infrastructure for AI agents by fostering transparency and accountability in the AI ecosystem.
Mnemom's alignment verification checks the compatibility of the AI agents with the designed tasks and ensures its coherence with the expected functionality. This feature helps to minimize errors and optimize the handling of AI systems.
Behavioral drift detection in Mnemom is important as it identifies changes in the AI agent's actions that deviate from its initial programming or expected behavior. This detection helps maintain consistency in AI performance, thereby preventing major inconsistencies or operation problems.
Mnemom promotes transparency and accountability in the AI ecosystem through alignment verification that verifies compatibility and coherence of AI functions, behavioral drift detection that ensures consistent functionality and implementing accountability protocols to ensure all AI actions align with established ethical standards and guidelines.
The accountability protocols implemented by Mnemom ensure that all AI actions comply with specific ethical standards and guidelines, and that AI agents can be held accountable for their actions. With these protocols, Mnemom creates a reliable, transparent, and safe environment for AI operation and deployment.
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