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Acacian/aegis

by Various

LLM guardrails & prompt injection detection for Python. Auto-instruments LangChain, CrewAI, OpenAI, LiteLLM + 8 more frameworks. PII masking, toxicity detection, policy CI/CD. One

A

MCP

Acacian/aegis

Added 1 June 2026

#agent-security #ai-agent-security #ai-agents #ai-governance #ai-safety #ai-security #audit-trail #compliance

Overview

Acacian/aegis is an LLM guardrails and prompt injection detection library for Python. It auto-instruments LangChain, CrewAI, OpenAI, LiteLLM, and eight other frameworks with a single line of code, providing PII masking, toxicity detection, and policy CI/CD without code changes.

Best for

Best for
Python developers who need to add basic safety controls to existing LLM applications quickly

Use cases

  • Integrate prompt injection detection into LangChain agents
  • Add PII masking to OpenAI API calls
  • Automate policy enforcement for LLM outputs in CI/CD

How to use

Install

pip install agent-aegis

Tested with

Claude Desktop, Cursor, Windsurf, VS Code, ChatGPT

Notes

Acacian/aegis is an LLM guardrails and prompt injection detection library for Python. It auto-instruments LangChain, CrewAI, OpenAI, LiteLLM, and eight other frameworks with a single line of code, providing PII masking, toxicity detection, and policy CI/CD without code changes.

9 stars on GitHub. Last updated 2026-05-05. Licensed MIT.

Use cases

  • Integrate prompt injection detection into LangChain agents
  • Add PII masking to OpenAI API calls
  • Automate policy enforcement for LLM outputs in CI/CD

Pros

  • Minimal integration effort with one-line setup
  • Supports a wide range of popular LLM frameworks
  • Combines multiple guardrails (injection, PII, toxicity) out of the box

Cons

  • Python-only, limiting use in polyglot stacks
  • May introduce latency overhead during inference
  • Requires familiarity with guardrail configuration for custom policies

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Minimal integration effort with one-line setup
  • Supports a wide range of popular LLM frameworks
  • Combines multiple guardrails (injection, PII, toxicity) out of the box

Cons

  • Python-only, limiting use in polyglot stacks
  • May introduce latency overhead during inference
  • Requires familiarity with guardrail configuration for custom policies
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