Trust scores, cryptographic proof, and risk assessment for AI agents.

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February 24, 2026

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Mnemomv2

US Mnemom Agent integrity monitoring

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Trust scores, cryptographic proof, and risk assessment for AI agents.

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Overview

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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 cryptographically attested: Ed25519 signatures, SHA-256 hash chains, Merkle trees, and zero-knowledge proofs.

Every agent gets a Trust Score — a bond-rating-style score from AAA to CCC computed from five weighted signals. Fleet operators get coherence analysis, team risk modeling (CoVaR, Markowitz, DebtRank), and real-time containment with automatic pause/kill/resume.

Works with Anthropic, OpenAI, and Gemini. Open protocols (AAP/AIP) on npm and PyPI. Apache-licensed.

Key Features

Mnemom Trust Score(tm) - Bond-rated Agent Reputation (aaa-ccc)
Cryptographic Integrity Certificates (ed25519, Hash Chained, Merkle Trees, Zk Proofs)
Real-time Agent Containment Engine (auto-pause/kill/resume)
Team Risk Assessment (covar, Markowitz, Debtrank)
Drop-in Gateway — Zero Agent Code Changes
Multi-provider Support (anthropic/openai/gemini)
Multi-agent Coherence Monitoring
Real-time Agent Reasoning Analysis
Webhook Notifications On Violations
Interactive Verification Showcase
Open Telemetry Support

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Mnemom v2

Feb 24, 2026

Alex Garden

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- 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

2 0

Author

Alex Garden

@alexgarden-mnemom

Founded Relic Entertainment at 22 (Homeworld, Company of Heroes). Scaled Xbox LIVE at Microsoft. Led Zynga Studios. Now building trust infrastructure for AI agents at Mnemom (mnemom.ai).

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Alex Garden

🛠️ 1 tool

Feb 24, 2026

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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.

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Pros and Cons

Pros

Mnemom Trust Score(TM) - Bond-rated agent reputation (AAA-CCC)

Works with Anthropic, OpenAI, Gemini

Real-time containment (auto pause/kill/resume)

Team Risk Assessment (CoCAR, Markowitz, DebtRank)

Cryptographic integrity certificates (Ed25519 signed)

Zero-knowledge verdict verification

Merkle inclusion proofs

Tamper-evident hash chain

Independent verification API (/v1/verify)

Multi-agent coherence system

Real-time behavior monitoring

Flags boundary violations

Open-source integrity gateway

Usage-based pricing

Dashboard included

Free SDKs

Compliance exports for EU

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Cons

Requires API key per provider

No native mobile app

No offline operability

Limited dashboard customization

Lack of multi-language support

Q&A

What is Mnemom's main function?

Mnemom's main function is to develop a comprehensive trust infrastructure for AI agents by fostering transparency and accountability in the AI ecosystem.

How does Mnemom's alignment verification feature work?

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.

What is the importance of behavioral drift detection in Mnemom?

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.

How does Mnemom promote transparency and accountability in the AI ecosystem?

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.

Can you explain the accountability protocols implemented by Mnemom?

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.

What role does Mnemom play in AI ethics and governance?

Mnemom plays a significant role in AI ethics and governance through its accountability protocols that enforce compliance with ethical standards and guidelines. By encouraging transparency, it promotes responsible behavior and decision-making in AI systems.

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How does Mnemom maintain the safety in AI operation?

Mnemom maintains the safety in AI operation by implementing behavioral drift detection to keep track of AI agent's actions, identify deviations from their original programming, and take necessary actions to resolve inconsistencies or problems.

How does Mnemom contribute to AI system efficiency and reliability?

Mnemom contributes to AI system efficiency and reliability through its alignment verification feature that checks the compatibility of AI agents with their expected tasks, hence, reducing errors and improving the overall performance of the AI system.

How can Mnemom detect deviations from initial programming in AI agents?

Mnemom can detect deviations from initial programming in AI agents using its behavioral drift detection feature. It continuously monitors the AI agent's behaviour for changes that might deviate from its expected function.

Why is Mnemom significant for organizations employing AI systems?

Mnemom is significant for organizations employing AI systems because it brings transparency and accountability to AI operations, ensuring coherence of AI actions with intended functionalities and ethical guidelines, and maintaining consistent performance to avoid unexpected issues or problems.

What are the innovative techniques used by Mnemom?

The innovative techniques used by Mnemom include alignment verification, behavioral drift detection, and implementation of accountability protocols to facilitate transparency, ensure conformity with ethical standards and enhance overall AI system performance.

How does Mnemom check the compatibility of the AI agents with the designed tasks?

Mnemom checks the compatibility of the AI agents with the designed tasks through its alignment verification feature. It ensures that AI behavior is in line with its expected functionality.

Why is the trust infrastructure by Mnemom important for AI agents?

The trust infrastructure by Mnemom is important for AI agents as it fosters transparency, ensures alignment with intended tasks, detects and handles deviations, and maintains ethical standards and accountability protocols, altogether facilitating a reliable and safe AI operational environment.

How does Mnemom ensure coherence between AI agent functionality and the expected outcome?

Mnemom ensures coherence between AI agent functionality and the expected outcome by continually conducting alignment verification. It checks for compatibility and coherence to facilitate the minimization of errors and more efficient handling of AI systems.

What is the result of Mnemom's behavioral drift detection on AI system's performance?

The result of Mnemom's behavioral drift detection on AI system's performance is maintaining the consistency of operations. It identifies changes that deviate from initial programming and takes suitable measures to prevent major inconsistencies or operation problems.

What happens when the AI agents do not comply with Mnemom's accountability protocols?

If AI agents do not comply with Mnemom's accountability protocols, it indicates potential issues and discrepancies within the AI operations. The system will take necessary actions to correct these discrepancies or alter the behavior of the AI agent to comply with the protocols.

Can Mnemom detect minor inconsistencies in AI agent's actions?

Mnemom can detect minor inconsistencies in AI agent's actions through its behavioral drift detection feature. This ability allows it to prevent significant operation problems by addressing potential issues in their early stages.

How does Mnemom contribute to the assurance of reliability and governance of AI agents?

Mnemom contributes to the assurance of reliability and governance of AI agents by applying its unique features including alignment verification, behavioral drift detection, and implementation of accountability protocols. These ensure compatibility, continuity and compliance with ethical standards, hence promoting a trustworthy AI operation environment.

What kind of ethical standards and guidelines does Mnemom use for AI action compliance?

Mnemom uses ethical standards and guidelines that align with the best practices in AI ethics and governance, although the specific standards and guidelines are not specified on their website.

What is Smoltbot?

Smoltbot is an open-source integrity gateway for AI agents. It acts as a conduit for agents like Anthropic, OpenAI, or Gemini, reading the agent's reasoning in real-time. It has the ability to flag boundary violations, value drift, and unsafe decision-making before actions are implemented in production.

What is Mnemom?

Mnemom is a tool that specializes in constructing trust infrastructures for Artificial Intelligence agents. It achieves this through its key features: transparent alignment verification, behavioral drift detection, and accountability protocols. These features enhance the transparency, predictability, and accountability in the AI system’s function and interactions.

How does Mnemom build trust in AI agents?

Mnemom builds trust in AI agents by providing transparent alignment verification, detecting behavioral drift, and implementing accountability protocols. It ensures that the AI agents operate in accordance with their intended design and user-defined goals.

What is the purpose of Smoltbot's Alignment Card?

The purpose of Smoltbot's Alignment Card is to define what an AI agent is allowed to do, what activities are prohibited, and what ought to be escalated to human attention. It is a JSON config used to set rules and guidelines for the AI agent’s functions.

What kind of alerts does Smoltbot provide during the integrity check?

Smoltbot provides three types of alerts during the integrity check: a clear verdict means the action of the agent is in line with the sets rules; a review needed verdict indicates that the action needs to be examined by a human; a boundary violation verdict shows that the action has overstepped the boundary set by the rules established.

Can Smoltbot work with various AI agents, like OpenAI, Gemini, or Anthropic?

Yes, Smoltbot can work with various AI agents like OpenAI, Gemini, and Anthropic. It serves as a gateway, interpreting these agents' reasoning and actions in real-time.

What's the difference between a clear, review-needed, and boundary violation verdict in Smoltbot?

In Smoltbot’s integrity check system, a clear verdict indicates that the agent's action is within the defined duties, review needed verdict means the action requires human review for decision and a boundary violation verdict implies that the action has exceeded the boundaries set by the rules and needs immediate attention.

How does Mnemom's Transparent Alignment Verification feature work?

Mnemom's Transparent Alignment Verification feature works by ensuring that AI agents are functioning in accordance with their original programming or set objectives. This process entails systematic tracking of any deviances in AI behaviour from its intended design.

What is the Behavioral Drift Detection feature in Mnemom designed to do?

The Behavioral Drift Detection feature in Mnemom is designed to monitor the AI agents' behavior over time and identify any significant behavioural changes. These shifts can affect the agent's accuracy and dependability, hence early detection and rectification is critical.

What is the role of Mnemom's Accountability Protocols?

Mnemom's Accountability Protocols are aimed at enhancing the reliability and credibility of AI agents. They establish a system of checks, balances, and procedural oversight for AI behaviour, which is crucial in creating a responsible and effective AI ecosystem.

How does Smoltbot prevent unsafe decision making before actions reach production?

Smoltbot prevents unsafe decision making before actions reach production by reading the agent's reasoning in real-time and identifying potential issues like boundary violations, value drift, and other unsafe behaviours. After identifying these threats, they are flagged for review or action.

What are the functions of the Smoltbot dashboard?

The functions of the Smoltbot dashboard include providing real-time tracking and analysis of the AI agent's actions, helping users to understand the reasoning of the AI, and flagging potential boundary violations, value drifts, and unsafe behaviours.

What are the compliance exports for EU AI Act Article 50 provided by Smoltbot?

Smoltbot provides compliance exports in accordance with EU AI Act Article 50. These include compliance reports and datasets that help in adhering to the standards and requirements of the EU AI Act Article 50, which regulates the use of AI systems within the European Union.

What is the multi-agent coherence system in Smoltbot used for?

The multi-agent coherence system in Smoltbot is used for effective management of multiple AI agents or fleets. It ensures synchronization in the functioning of various agents, enhancing their collective efficiency and coherence.

How much does it cost to use Smoltbot?

Smoltbot has a usage-based pricing model that starts from $0.01 per check. However, it also provides free and open-source SDKs.

Is it possible to define forbidden actions for an AI agent using Smoltbot?

Yes, it is indeed possible to define forbidden actions for an AI agent using Smoltbot. This is accomplished through the use of an Alignment Card, which allows users to set the boundaries and rules for AI agent behaviour.

How can Smoltbot increase confidence in AI agent solutions?

Smoltbot increases confidence in AI agent solutions by continuously monitoring AI agents for boundary violations, value drift, and unsafe decision-making. It reads and interprets the agent's reasoning in real-time, ensuring actions align with the established rules before they go into production.

What is the OpenTelemetry integration in Smoltbot used for?

The OpenTelemetry integration in Smoltbot is utilized for observing and debugging AI agents. It aids in the collection and analysis of metrics, logs, and traces from the agents, which can enhance our understanding of their operation and improve their overall performance.

What do the free and open-source SDKs in Smoltbot include?

Smoltbot's free and open-source SDKs include software development kits that are freely available and open to modification. This allows developers to freely implement and customize Smoltbot within their own AI projects or platforms.

Can Smoltbot detect value drift in AI agents?

Yes, Smoltbot can detect value drift in AI agents. Value drift, a deviation from the original values or goals, is identified through real-time analysis of the agent’s reasoning, and appropriately flagged for action.

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