Decision comparison
AgentVault vs Auditi
AgentVault is the targeted choice for teams whose primary risk is an AI agent executing system commands, accessing credentials, or making uncontrolled network requests. Auditi is the targeted choice for teams operating LLM applications that need trace-based evaluation, human labeling, real-time cost visibility, and fine-tuning-data creation. They address complementary layers: AgentVault governs agent security, while Auditi measures and improves model behavior.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — AI Security and Agent Observability.
Quick Comparison
| Decision factor | AgentVault | Auditi |
|---|---|---|
| Best For | Developers running system-access AI agents who need command controls, credential protection, network visibility, and investigation-ready audit records. | Teams improving LLM applications through trace collection, automated quality evaluation, human annotations, cost analysis, and fine-tuning datasets. |
| Architecture | Self-hosted TypeScript security proxy for AI agents, with monitoring, approval controls, credential scanning, and full audit-trail capabilities. | Open-source observability and evaluation stack using Python SDK, FastAPI, React, automatic provider instrumentation, and Docker Compose self-hosting. |
| Pricing Model | Free self-hosted under the MIT license. AgentVault publishes no price a buyer can read: its site markets pre-built n8n agents and a free entry point without naming a figure, agentvault.ai/pricing returns 404, and agentvault.store does not resolve. Paid Pro and Enterprise tiers appear in a plan list supplied to this catalogue earlier in 2026 at $49 and $199 per month; neither figure can be traced to a current first-party source, so they are recorded as unverified rather than published prices. | Free and open source |
| Ease of Use | Designed around a real-time dashboard and permission approvals; setup complexity depends on placing the proxy around agent workflows. | Two-line auto-instrumentation captures OpenAI, Anthropic, and Google calls, while Docker Compose enables a self-hosted deployment path. |
| Scalability | Supports operational controls such as rate limiting, network monitoring, and audit logging; deployment-scale limits are not published. | Captures production traces, tracks real-time cost, and converts trace data into fine-tuning datasets; capacity limits are not published. |
| Community/Support | MIT-licensed TypeScript repository with 2 GitHub stars; paid Starter, Pro, and Enterprise plans are published. | MIT-licensed JavaScript repository with 5 GitHub stars; documentation and live online training are listed, with online support. |
AgentVault
- Best For:
- Developers running system-access AI agents who need command controls, credential protection, network visibility, and investigation-ready audit records.
- Architecture:
- Self-hosted TypeScript security proxy for AI agents, with monitoring, approval controls, credential scanning, and full audit-trail capabilities.
- Pricing Model:
- Free self-hosted under the MIT license. AgentVault publishes no price a buyer can read: its site markets pre-built n8n agents and a free entry point without naming a figure, agentvault.ai/pricing returns 404, and agentvault.store does not resolve. Paid Pro and Enterprise tiers appear in a plan list supplied to this catalogue earlier in 2026 at $49 and $199 per month; neither figure can be traced to a current first-party source, so they are recorded as unverified rather than published prices.
- Ease of Use:
- Designed around a real-time dashboard and permission approvals; setup complexity depends on placing the proxy around agent workflows.
- Scalability:
- Supports operational controls such as rate limiting, network monitoring, and audit logging; deployment-scale limits are not published.
- Community/Support:
- MIT-licensed TypeScript repository with 2 GitHub stars; paid Starter, Pro, and Enterprise plans are published.
Auditi
- Best For:
- Teams improving LLM applications through trace collection, automated quality evaluation, human annotations, cost analysis, and fine-tuning datasets.
- Architecture:
- Open-source observability and evaluation stack using Python SDK, FastAPI, React, automatic provider instrumentation, and Docker Compose self-hosting.
- Pricing Model:
- Free and open source
- Ease of Use:
- Two-line auto-instrumentation captures OpenAI, Anthropic, and Google calls, while Docker Compose enables a self-hosted deployment path.
- Scalability:
- Captures production traces, tracks real-time cost, and converts trace data into fine-tuning datasets; capacity limits are not published.
- Community/Support:
- MIT-licensed JavaScript repository with 5 GitHub stars; documentation and live online training are listed, with online support.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | AgentVault | Auditi |
|---|---|---|
| GitHub commits, 90d(Product adoption) | 0 | 0 |
| GitHub stars(Product adoption) | 2 | 5 |
| Product Hunt comments(Community interest) | 1 | 1 |
| Product Hunt reviews(Community interest) | 0 | 0 |
| Product Hunt votes(Community interest) | 2 | 4 |
As of September 21, 2026 — updated weekly.
Feature Comparison
| Feature | AgentVault | Auditi |
|---|---|---|
| Agent Security and Control | ||
| Runtime activity visibility | Real-time dashboard monitors AI agent activity and system-facing behavior | Automatic tracing records supported LLM API calls for inspection |
| Risky action prevention | Proxy blocks dangerous commands before they create security risks | Not verified |
| Access governance | Permission approvals provide controlled access management for agent actions | Human annotation workflows review traces when AI judges are insufficient |
| Observability and Investigation | ||
| Audit records | Full audit trails support compliance reviews and incident investigation | Production traces capture model interactions for performance evaluation |
| Network and provider visibility | Network monitoring tracks AI agent communications and connections | Auto-instrumentation captures OpenAI, Anthropic, and Google API calls |
| Usage control | Rate limiting restricts agent overuse and potential abuse | Real-time cost tracking reports LLM usage spending from traces |
| Evaluation and Improvement | ||
| Automated quality evaluation | Not verified | Seven-plus LLM-as-Judge evaluators run automatically on traces |
| Human quality review | Permission approvals gate sensitive actions before execution | Human annotation workflows complement automated AI-judge evaluations |
| Training-data generation | Audit trails retain security-relevant agent activity for investigation | Production traces can be transformed into fine-tuning datasets |
| Secrets and Identity | ||
| Credential protection | Credential scanning detects sensitive information in agent environments | Not verified |
| Secret storage | AES-256-GCM encryption secures stored secrets in the vault | Not verified |
| Authentication and integrations | JWT authentication, OAuth integration, cloud managers, and HashiCorp Vault support | Provider instrumentation supports OpenAI, Anthropic, and Google API calls |
| Deployment and Extensibility | ||
| Self-hosting model | MIT-licensed self-hosted security solution for developer agent environments | MIT-licensed stack self-hosted through Docker Compose deployment |
| Developer interfaces | CLI and RESTful API manage secrets, keys, and configuration | Python SDK provides two-line automatic instrumentation for supported providers |
| Implementation stack | Modular Go monorepo powered by Nx supports customization and extensions | FastAPI backend and React interface support observability workflows |
Agent Security and Control
Runtime activity visibility
Risky action prevention
Access governance
Observability and Investigation
Audit records
Network and provider visibility
Usage control
Evaluation and Improvement
Automated quality evaluation
Human quality review
Training-data generation
Secrets and Identity
Credential protection
Secret storage
Authentication and integrations
Deployment and Extensibility
Self-hosting model
Developer interfaces
Implementation stack
Which approach fits
AgentVault is the targeted choice for teams whose primary risk is an AI agent executing system commands, accessing credentials, or making uncontrolled network requests. Auditi is the targeted choice for teams operating LLM applications that need trace-based evaluation, human labeling, real-time cost visibility, and fine-tuning-data creation. They address complementary layers: AgentVault governs agent security, while Auditi measures and improves model behavior.
When each approach fits
Choose AgentVault if:
Choose AgentVault when agents have system access and you need dangerous-command blocking, permission approvals, credential scanning, network monitoring, rate limits, and auditable security controls. It is also appropriate when the published $0 Starter, $49/month Pro, or $199/month Enterprise plans fit your operating model.
Choose Auditi if:
Choose Auditi when the priority is observing OpenAI, Anthropic, or Google calls; automatically scoring production traces with LLM judges; collecting human annotations; tracking cost; and preparing fine-tuning datasets. It suits teams comfortable self-hosting an MIT-licensed Docker Compose stack.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between AgentVault and Auditi?
AgentVault is centered on securing AI agents at runtime. Its supplied capabilities include a real-time activity dashboard, dangerous-command blocking, permission approvals, network monitoring, rate limiting, credential scanning, and full audit trails. Auditi is centered on LLM application observability and quality improvement: it auto-instruments OpenAI, Anthropic, and Google calls, runs seven or more LLM-as-Judge evaluators, supports human annotations, tracks cost, and turns traces into fine-tuning datasets. An organization can potentially use both because their primary functions are different.
Which is better for small teams?
For a small team deploying agents with shell, credential, or network access, AgentVault is the more directly relevant starting point because it provides preventive controls such as dangerous-command blocking and permission approvals. Its commercial details are explicit: free self-hosted MIT software, Starter $0, Pro $49/month, and Enterprise $199/month. For a small team building a customer-facing LLM feature and trying to improve answer quality or control spend, Auditi is likely the better fit because two-line instrumentation, automatic evaluations, and real-time cost tracking focus directly on that workflow.
Can I migrate from AgentVault to Auditi?
There is no documented direct migration path in the supplied information, because AgentVault and Auditi retain different kinds of operational data and solve different problems. AgentVault's audit trails concern agent security activity, approvals, commands, credentials, and network behavior. Auditi works from LLM API traces and evaluates them with LLM judges or human annotations. A team could introduce Auditi alongside AgentVault for LLM tracing and evaluation, but should plan any data integration around its own event schemas, retention requirements, and security policies rather than assuming an import feature exists.
What are the pricing differences?
AgentVault publishes concrete options: a free self-hosted MIT version, Starter at $0, Pro at $49 per month, and Enterprise at $199 per month. Auditi is described as free and open source under the MIT license, with self-hosting through Docker Compose. The supplied information does not list any Auditi paid tiers, metered charges, managed-cloud offering, sales quote trigger, or public dollar amount. Accordingly, its known software license cost is free, while operational costs for hosting and model-provider usage remain separate considerations not priced in the supplied data.