Initializing Python runtime...
Tip: Aegis evaluates policies in under 1ms
AI agent governance in your browser. No install needed.
Define rules in YAML: which actions are auto-approved, need human review, or are blocked.
Send agent actions (navigate, read, write, delete) through the policy engine.
Instantly see risk level, approval decision, matched rule, and full audit trail.
# 50+ lines of DIY governance... per action type
if action.type == "delete":
if action.risk > THRESHOLD:
logger.warning(f"High-risk: {action}")
if not await ask_human_approval(action):
raise PermissionError("Denied")
# No audit trail
# No policy hot-reload
# Breaks when you add a new action type
result = await executor.run(action)Scan MCP tool definitions for poisoning patterns. Ported from Aegis ToolDescriptionScanner with 10 regex detection patterns and Unicode normalization.
Simulate LLM cost tracking with budget limits and threshold transitions. Ported from Aegis CostTracker with real model pricing data.
Interactive hash-chain audit log using Web Crypto SHA-256. Ported from Aegis CryptoAuditChain for tamper-evident logging.
Map Aegis features to regulatory requirements across 5 frameworks. Ported from Aegis ComplianceMapper.
Real-time PII detection and masking. Ported from Aegis PIIGuardrail with Luhn validation for credit cards, Korean RRN/phone patterns, and API key detection.
Real-time prompt injection detection across 8 categories. Ported from Aegis InjectionGuardrail with multi-language support (Korean, Chinese, Japanese) and configurable sensitivity.
agent.run(action) # 💥 anything goes
rt.run_one(action) # 🛡️ policy-checked
rt.run_one(action)auto / approve / blockDefine rules for each action type: auto-approve safe reads, require human review for writes, block dangerous operations.
Your AI agent (LangChain, CrewAI, OpenAI, etc.) sends each action through Aegis before executing it.
Aegis evaluates risk in <1ms: auto-approve, require human approval, or block. Every decision is audit-logged.
Every action is evaluated, logged, and auditable — zero blind spots.
$ pip install agent-aegisversion: "1"
rules:
- name: read_auto
approval: auto
- name: write_review
approval: approve
- name: delete_block
approval: blockfrom aegis import Policy, Runtime
policy = Policy.from_yaml("policy.yaml")
async with Runtime(executor, policy) as rt:
result = await rt.run_one(action)
# result.is_allowed → True/False$ docker run -p 8000:8000 \
-v ./policy.yaml:/app/policy.yaml \
ghcr.io/acacian/aegis:latest
# REST API at http://localhost:8000See it in action — no install required
Click any scenario to load its policy and test actions
The difference between hoping your AI agent behaves and knowing it does
| Without Governance | With Aegis | |
|---|---|---|
| Policy changes | Redeploy code | Edit YAML, hot-reload |
| Risk evaluation | Manual if/else chains | <1ms, declarative rules |
| Audit trail | Build your own logging | Built-in, compliance-ready |
| Human approval | Custom workflow code | One-line approval handler |
| Framework support | Build per framework | 7 adapters, one policy |
| Setup time | Days to weeks | 5 minutes |
Add governance to any Python AI agent in 5 minutes. One pip install, one YAML file.