AgentVault and Auditi solve fundamentally different problems in the AI agent lifecycle. AgentVault is a security-first tool that monitors and controls what AI agents can do at the system level, while Auditi is an observability and evaluation platform that measures how well AI agents perform their tasks. Teams concerned about agent safety and credential exposure should choose AgentVault, while teams focused on understanding and improving LLM output quality will find Auditi more valuable.
| Feature | AgentVault | Auditi |
|---|---|---|
| Best For | Teams running AI agents with system access who need real-time security monitoring and dangerous command blocking | AI developers who need combined LLM tracing and automated evaluation with built-in LLM-as-Judge scoring |
| Architecture | Self-hosted proxy layer between AI agents and system resources, built in TypeScript with RESTful API | Self-hosted Python SDK with FastAPI backend and React frontend, deployable via Docker Compose |
| Pricing Model | Free self-hosted (MIT license), Starter $0, Pro $49/month, Enterprise $199/month | Free and open source |
| Ease of Use | Proxy-based setup with CLI interface and real-time dashboard for monitoring agent activity | Two-line auto-instrumentation that monkey-patches OpenAI, Anthropic, and Google API calls automatically |
| Scalability | Supports enterprise deployments with rate limiting, credential scanning, and multi-agent environments | Handles production-scale trace collection with span-level evaluation across multi-step agents |
| Community/Support | Open-source MIT project on GitHub with 2 stars; community-driven with priority email support on paid plans | Open-source MIT project on GitHub with 4 stars; documentation and live online training available |
| Metric | AgentVault | Auditi |
|---|---|---|
| GitHub stars | 2 | 4 |
| Product Hunt votes | 2 | 4 |
As of 2026-05-04 — updated weekly.
| Feature | AgentVault | Auditi |
|---|---|---|
| Core Monitoring | ||
| Real-Time Dashboard | Live dashboard showing all AI agent activity and security events | Trace visualization with span trees, token usage, and cost tracking |
| Audit Trails | Full audit trails for compliance investigation and security review | Complete span trees with full request/response logging per API call |
| Alert System | Dangerous command blocking with permission approval workflows | Automated LLM-as-Judge evaluators flag quality issues on every trace |
| Cost Tracking | Not a primary focus; centered on security monitoring instead | Real-time cost tracking with token usage extraction from streamed responses |
| Security Features | ||
| Credential Protection | AES-256-GCM encryption for secret storage with credential scanning | API key-based authentication for SDK instrumentation access |
| Access Control | Permission approvals and controlled access management for agent actions | Human annotation workflows for controlling evaluation quality gates |
| Network Monitoring | Tracks all agent communications and network activity in real time | Captures all outbound LLM API calls via auto-instrumentation patches |
| Rate Limiting | Built-in rate limiting to prevent agent abuse and resource overuse | No built-in rate limiting; focused on observation rather than restriction |
| Evaluation & Quality | ||
| Automated Evaluation | No built-in evaluation; focuses on security monitoring and blocking | Seven or more LLM-as-Judge evaluators run automatically on traces |
| Human Review | Manual permission approvals for dangerous commands and actions | Dedicated human annotation queues when AI judges are insufficient |
| Quality Metrics | Security-oriented metrics like blocked commands and credential scans | Hallucination, relevance, correctness, and toxicity scoring per span |
| Integration & Deployment | ||
| Deployment Model | Self-hosted security proxy with MIT-licensed open-source codebase | Self-hosted via Docker Compose with Python SDK and React frontend |
| LLM Provider Support | Works with any AI agent framework; proxy intercepts system-level calls | Auto-instruments OpenAI, Anthropic, and Google Gemini API calls natively |
| Data Export | Audit log forwarding to Splunk and cloud secret manager integrations | Annotated traces export as JSONL, CSV, and Parquet for fine-tuning |
| Cloud Integrations | AWS, Azure, GCP secret managers and HashiCorp Vault integration | Self-hosted focused; no native cloud service integrations listed |
| Developer Experience | ||
| Setup Complexity | CLI-based installation with modular Go monorepo powered by Nx framework | Two lines of code to instrument; docker compose up for full deployment |
| API Access | RESTful API and CLI for programmatic access to vault functionality | Python SDK with FastAPI backend exposing evaluation and trace endpoints |
| Language Support | TypeScript codebase with CLI tools and RESTful API endpoints | JavaScript and Python SDK with React frontend for the dashboard |
Real-Time Dashboard
Audit Trails
Alert System
Cost Tracking
Credential Protection
Access Control
Network Monitoring
Rate Limiting
Automated Evaluation
Human Review
Quality Metrics
Deployment Model
LLM Provider Support
Data Export
Cloud Integrations
Setup Complexity
API Access
Language Support
AgentVault and Auditi solve fundamentally different problems in the AI agent lifecycle. AgentVault is a security-first tool that monitors and controls what AI agents can do at the system level, while Auditi is an observability and evaluation platform that measures how well AI agents perform their tasks. Teams concerned about agent safety and credential exposure should choose AgentVault, while teams focused on understanding and improving LLM output quality will find Auditi more valuable.
Choose AgentVault if:
Choose Auditi if:
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
AgentVault and Auditi solve fundamentally different problems in the AI agent lifecycle. AgentVault is a security-first tool that monitors and controls what AI agents can do at the system level, while Auditi is an observability and evaluation platform that measures how well AI agents perform their tasks. Teams concerned about agent safety and credential exposure should choose AgentVault, while teams focused on understanding and improving LLM output quality will find Auditi more valuable.
Choose AgentVault when you need You need real-time security monitoring with dangerous command blocking for AI agents with system access, Your team requires credential scanning, secret storage encryption, and integrations with cloud secret managers like AWS, Azure, or HashiCorp Vault.
Choose Auditi when you need You need automated LLM output evaluation with built-in judges for hallucination, relevance, and correctness scoring, You want minimal-effort instrumentation that captures all OpenAI, Anthropic, and Google API calls with just two lines of code.
AgentVault: Freemium with MIT license; free self-hosted, Pro at $49/month, Enterprise at $199/month. Auditi: Completely free and open-source under MIT license with no paid tiers.