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AegisMCP 🛡️

AegisMCP is an opinionated, high-performance Model Context Protocol (MCP) framework designed for enterprise scale.

If other minimal frameworks are like Flask, AegisMCP is like Django. It provides a robust kernel, integrated zero-dependency abstractions, and high-performance asynchronous orchestration to scale your multi-agent architecture.

The Enterprise Standard

Other MCP libraries are built for weekend prototypes—they give you a tool in 5 lines of code, but leave you completely exposed in production.

AegisMCP is built for Fortune 500 deployments. We don't bolt security on at the end. Aegis provides a deeply integrated, mathematically safe ExecutionPipeline that natively handles complex enterprise requirements.

graph LR
    A[LLM Agent] -->|JSON-RPC| B(Transport)
    B --> C{Middleware Pipeline}
    C -->|Auth Fail| D[Reject]
    C -->|Rate Limit| D
    C -->|Success| E[Tool Executor]
    E --> F((Saga Engine))

Integrated Security out-of-the-box

  • Role-Based Access Control (RBAC) to restrict sensitive tool usage.
  • Rate Limiting to prevent LLMs from running up your API bills.
  • Deterministic Sagas to automatically roll back multi-step AI tasks when failures occur.
  • Multi-Agent Meshes so intelligent agents can serve tools to other agents.

If you are building a weekend toy, use a minimal lightweight framework. If you are deploying an AI Agent into a secure corporate network, use AegisMCP.

Core Features

  • Parallel Tool Execution: Native asyncio.gather tool orchestrations vastly reduce latency during dense multi-tool generation.
  • AegisContext Everywhere: Explicit context propagation eliminates race conditions and ties every tool call to standard IDs (request_id, trace_id, span_id).
  • Zero Mandatory Dependencies: The core framework requires only pydantic and anyio. Run a basic stdio server in a 50MB container.
  • First-Class Security: Customizable Auth, RBAC policy gating, Rate Limiting, and Audit Logging right out of the box.
  • Built-In Agent Runtime: Scale your AI via hierarchical sub-agents, deterministic Saga workflows, and multi-tool selection logic via the built-in Layer 5/6 engines.