Building an OpenClaw Security Vault for AI Agents
Source: Dev.to
⚠️ Collection Error: Content refinement error: Error: 429 429 Too Many Requests: you (bkperio) have reached your weekly usage limit, upgrade for higher limits: https://ollama.com/upgrade
Once AI agents start using tools and external APIs, the engineering problem changes. It is no longer only about output quality. It becomes a runtime problem: how do you inspect traffic, detect risky behavior, limit what the system can do, and keep token spend under control? That is the angle ClawVault takes. According to the current repository README, ClawVault is an open-source OpenClaw Security Vault for AI agents and AI applications, centered on three ideas: Visual Monitoring
Atomic Control
Generative Policies
What makes the repo more concrete is that it also lists the operational features around those ideas: sensitive data detection prompt injection defense dangerous command guard auto-sanitization token budget control real-time dashboard The architecture shown in the README is also useful because it makes the control path explicit: a transparent proxy gateway for AI traffic a detection engine for sensitive data, injection patterns, and dangerous commands a guard / sanitizer that can allow, block, or sanitize audit + monitoring with token budget tracking a web dashboard for configuration and review That is a stronger pattern than scattering checks across application code, because it gives you a central place to observe and govern runtime behavior. The quick-start shown in the repo today is: pip install -e . clawvault start clawvault scan “password=MySecret key=sk-proj-abc123” clawvault demo
And the sample config looks like this: proxy: port: 8765 intercept_hosts: [“api.openai.com”, “api.anthropic.com”]
guard: mode: “interactive” # interactive | strict | permissive
monitor: daily_token_budget: 50000
One detail worth noting: the README also separates what is already implemented from what is still expanding. API gateway monitoring and interception are marked as implemented, while file-side monitoring, broader agent-level atomic control, and generative policy orchestration are still in progress. That makes ClawVault interesting not because it claims to solve everything, but because it defines a clear control-layer shape for production AI systems. Repo: https://github.com/tophant-ai/ClawVault