Constellation Gate AI
Open SiteConstellation Gate AI is a drop-in gateway for AI agents that screens prompts for injection attacks, secrets, and risky requests while also reducing token usage. It is built for developers and teams that want agent security without rewriting their model stack.
Added on July 19, 2026
Product Information
What is Constellation Gate AI?
Constellation Gate AI sits between an AI agent and the model provider to inspect requests before they reach the model. It adds prompt-injection defense, secret scanning, audit trails, prompt compression, caching, and routing while preserving existing Claude, ChatGPT, or API workflows. The product is aimed at developers, security-minded teams, and agent builders that need to reduce risk as agents touch more tools and data. Its value is a practical gateway pattern: keep the tools you already use, but add security controls and token savings in the middle.
How to use Constellation Gate AI?
- Create a Gate AI account and choose whether to bring existing provider keys or use pay-as-you-go model access.
- Install Gate Connect or point an existing SDK at the Gate endpoint.
- Route agent or coding-tool requests through Gate instead of sending them directly to the model provider.
- Review blocked prompt-injection attempts, secret findings, token savings, and audit records.
- Tune policies, routing, and integrations as the agent workflow grows.
Core Features
- Prompt-injection defense — Screens agent requests against known and emerging injection patterns.
- Secret scanning — Detects sensitive values before they are passed into an AI model.
- Audit trail — Records verifiable request history for review and governance.
- Prompt compression — Reduces token usage while preserving model output quality.
- Drop-in routing — Works by changing the endpoint or using the desktop connector instead of rewriting an app.
- Provider flexibility — Supports existing model subscriptions or pay-as-you-go access through one gateway.
Use Cases
- AI agent security — Add a defensive layer before agents call models with sensitive context.
- Coding assistant governance — Route tools such as Claude Code, Cursor, or Codex through a monitored gateway.
- Token cost control — Use compression and caching to reduce recurring model spend.
- Compliance review — Keep audit records for teams that need visibility into AI request flows.