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Basic Information

CodeGate is an archived, privacy-first platform designed to centralize and secure AI coding assistants, agent frameworks, and model interactions. The repository provides the server and tooling to manage prompts, provider configurations, model muxing, workspaces, and histories so multiple AI tools and extensions can operate under a single, unified environment. It focuses on making AI-driven code recommendations safer by performing security-centric reviews and scanning dependencies, and by preventing accidental leakage of secrets or personal data. The project is distributed as a Docker container for local deployment and exposes a web dashboard for monitoring interactions and security findings. The README documents supported assistants and model providers and links to installation, dashboard access, and developer docs.

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App Details

Features
CodeGate offers workspace management so each project can have dedicated model and prompt configurations and chat history. It provides model muxing to route prompts to different models based on workspace or file type. Secrets redaction and PII detection automatically remove sensitive data from prompts and un-redact responses for clients. Dependency risk awareness scans package files and imports for outdated or vulnerable libraries. Security reviews analyze AI-generated code for insecure patterns. A web dashboard displays security risks and interaction history. The distribution is provided as a Docker container with documented ports and CLI/docs for advanced configuration. The README lists integrations with local and hosted model providers and several coding assistants.
Use Cases
CodeGate reduces configuration sprawl by centralizing provider credentials, prompts, and model routing so developers and teams can standardize how AI assistants interact with models. Its redaction and PII features minimize the risk of exposing secrets or personal data to third-party APIs. Dependency scanning and security reviews help catch vulnerable recommendations and insecure code patterns produced by LLMs, improving code safety before changes reach repositories. The dashboard and interaction history enable auditing and tracking of assistant behavior. Local Docker deployment and a privacy-first design mean code and telemetry can remain on the developer’s machine, supporting compliance and reducing external data sharing risk.

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