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Agent integrity monitoring vv2.0 2026 Rank: #967

Mnemom ✓

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

Source rating 5.0 (2) 21k views 9 saves 1 comments Freemium
Agent Monitoring AI Compliance AI Safety
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API Cryptography Developer Tools Agent integrity monitoring
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Free + from $0.01/mo

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Verdict

A quick read before you dig into the full page.

Best if
Trust scores, cryptographic proof, and risk assessment for AI agents
Skip if
Limited dashboard customization
Pick this when
Mnemom Trust Score(TM) - Bond-rated agent reputation (AAA-CCC)

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Overview

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.
  • 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
Mnemom website

Best for

Short scenarios where Mnemom tends to shine.

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

Example outputs 1

Sample generations and before/after results for Mnemom.

Mnemom screenshot
Mnemom screenshot Mnemom website

Access & connections

Where you can use Mnemom.

Platforms

Web

API

Public API available

Browse other tools with an API

Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • API
  • Code
  • Tabular
  • Text

Output

  • Code
  • Tabular
  • API
  • Text

Highlights

  • Agents
  • API
  • Local
  • Open Source
  • No Signup
  • Use code TAAFT
  • 25% OFF

Supported features

  • Agents
  • API
  • Runs Locally
  • Open Source
  • No Signup

Pros & 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
Show 9 more
  • 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

Cons

  • Requires API key per provider
  • No native mobile app
  • No offline operability
  • Limited dashboard customization
  • Lack of multi-language support

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Releases 2

Mnemom v2 Feb 24, 2026 Current

  • 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 February 19, 2026
Initial release of Mnemom.

Imported reviews 1

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Alex Garden Feb 24, 2026 [email protected]

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.

FAQ 12

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.
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.
Mnemom Free + from $0.01/mo
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