Aegis is a local-first credential isolation proxy for AI agents. Its core idea is: do not let AI agents directly hold API keys; instead, let agents make HTTP calls through a local proxy, and Aegis performs authentication, injects keys, and enforces security policies at the network boundary. In this way, the agent itself never sees, stores, or transmits real credentials.
Purpose and Problems Solved The material points out that AI agents (such as Claude, GPT, Cursor, and custom bots) increasingly call real APIs, such as Slack, GitHub, Stripe, and databases. Current common practices have four types of risks: agents can see raw API keys, and a single prompt injection can exfiltrate them; there is no domain protection, so a compromised agent can send a Slack token to evil.com; there is no audit record, so it is impossible to see what the agent did with credentials; and there is no access control, so every agent can use every credential. Aegis claims to solve these four problems at the same time: agents make HTTP calls through a local proxy, and Aegis handles authentication, enforces domain restrictions, and logs everything.
Main Capabilities
- Credential isolation: keys are injected at the proxy layer, and agents do not come into contact with real credentials.
- Domain protection: domain restrictions are enforced for every request, and the material says there is "no bypass."
- Agent authentication: supports issuing tokens for agents; tokens are stored as SHA-256 hashes, cannot be recovered, and can only be regenerated.
- Request body inspection: performs credential pattern detection on request bodies.
- Rate limiting: provided as part of the security pipeline.
- Audit logs: records agent call behavior.
- Encrypted storage: data at rest is encrypted using AES-256-GCM and ChaCha20-Poly1305.
- MCP integration: Aegis is a first-class MCP server, and any MCP-compatible AI agent can use it natively without making HTTP calls itself. The MCP server exposes three tools and replicates the complete Gate security pipeline: domain protection, agent authentication, request body inspection, rate limiting, and audit logs.
How to Use Deployment paths given in the material include: production environment setup (with agent authentication), MCP integration, and OpenClaw (a personal AI assistant, HTTP proxy + skills), among others. For MCP integration, configuration can be generated for your AI host; the material shows a comparison between "before (in the configurationplaintext keys)" and "after (Aegis, keys invisible)," but does not provide the specific configuration code content. The project also provides example integrations, with step-by-step guides containing configuration files and policies: Slack Bot (using a domain-restricted proxy to access and protect Slack bot tokens), GitHub integration (using per-agent authorization and read-only policies to protect GitHub PATs), Stripe backend (using request body inspection and rate limiting to isolate Stripe API keys), and OpenClaw skills.
Security and Compliance The project has published a STRIDE threat model, and the material says it analyzes 28 threats, with 0 severe/high-risk unmitigated findings; it also provides complete security architecture documentation covering trust boundaries, encryption pipelines, and data flows. The project is open source and uses the Apache 2.0 license.
Applicable Scenarios Suitable for developers and teams that need to let AI agents call real third-party APIs (such as Slack, GitHub, Stripe, and databases) but want to avoid key exposure, restrict accessible domains, control each agent's permissions, and retain audit records.
Limitations and Requirements Requires Node.js >= 20 (can be checked with node -v). The material does not provide specific installation commands, configuration code, pricing, or fee information; these contents are not yet provided.
