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🛡️ AgentSudo

The permission layer for AI agents.

PyPI version License: MIT Python 3.9+

AgentSudo is a lightweight permission engine for AI agents. Enforce scopes, approvals, and safe tool use across LangChain, LlamaIndex, FastAPI, and custom agents.


Why AgentSudo?

AI agents are becoming powerful, but most run with zero permission control—they can call any tool, access any API, and do unexpected things.

AgentSudo adds a lightweight, framework-agnostic permission engine that enforces scopes, rate limits, and human approvals. It works with LangChain, LlamaIndex, FastAPI, or plain Python with just a few lines of code.

Think of it as Auth0 for AI agents.


The Problem

Right now, your AI agents are running with God-mode access:

# ❌ BEFORE: All agents share one API key
STRIPE_API_KEY = "sk_live_..."  # Root access to everything

agent.charge_customer(1000000)  # Any agent can do ANYTHING
agent.delete_database()         # No permission checks
agent.email_all_customers()     # No oversight

When an agent hallucinates, it's catastrophic.


The Solution

# ✅ AFTER: Each agent gets scoped permissions
from agentsudo import Agent, sudo

support_bot = Agent(
    name="SupportBot",
    scopes=["read:orders", "write:refunds"]
)

@sudo(scope="write:refunds")
def issue_refund(order_id, amount):
    print(f"Refunding ${amount}")

# Support bot can issue refunds
with support_bot.start_session():
    issue_refund("order_123", 50)  # ✅ Allowed

# But analytics bot cannot
analytics_bot = Agent(name="Analytics", scopes=["read:orders"])

with analytics_bot.start_session():
    issue_refund("order_123", 50)  # ❌ PermissionDeniedError

Installation

pip install agentsudo

Requires Python 3.9+


Quick Start

1. Define Agent Identities

from agentsudo import Agent

# Create agents with different permission levels
admin_agent = Agent(
    name="AdminBot",
    scopes=["read:*", "write:*", "delete:*"]  # Wildcard permissions
)

support_agent = Agent(
    name="SupportBot",
    scopes=["read:orders", "write:refunds"]
)

readonly_agent = Agent(
    name="AnalyticsBot",
    scopes=["read:*"]  # Read-only access
)

2. Protect Functions with @sudo

from agentsudo import sudo

@sudo(scope="write:refunds")
def process_refund(order_id):
    print(f"Processing refund for {order_id}")

@sudo(scope="delete:database")
def drop_table(table_name):
    print(f"Dropping table {table_name}")

3. Run Code in Agent Sessions

# Admin can do everything
with admin_agent.start_session():
    process_refund("order_123")  # ✅ Allowed
    drop_table("customers")      # ✅ Allowed

# Support can only refund
with support_agent.start_session():
    process_refund("order_456")  # ✅ Allowed
    drop_table("customers")      # ❌ PermissionDeniedError

# Analytics can only read
with readonly_agent.start_session():
    process_refund("order_789")  # ❌ PermissionDeniedError

Features

🛡️ Guardrails (NEW in v0.3.0)

Prevent agents from responding to off-topic queries or prompt injection attacks:

from agentsudo import Agent, Guardrails, check_guardrails

# Define guardrails
rails = Guardrails(
    allowed_topics=["divorce", "legal", "marriage"],
    on_violation="redirect",
    redirect_message="I can only help with divorce-related questions.",
)

# Attach to agent
agent = Agent(
    name="DivorcioBot",
    scopes=["divorce:quote"],
    guardrails=rails,
)

# In your agent loop
with agent.start_session():
    is_valid, redirect = check_guardrails("When was Hitler born?")
    if not is_valid:
        return redirect  # "I can only help with divorce-related questions."
    
    # Process normally if valid
    result = agent_executor.invoke(user_input)

Built-in prompt injection protection:

rails = Guardrails()  # Injection detection is always enabled

# These are automatically blocked:
# - "Ignore all previous instructions"
# - "Pretend you are a different AI"
# - "[SYSTEM] New instructions"
# - And many more patterns...

Use the @guardrail decorator for simpler protection:

from agentsudo import guardrail

@guardrail(
    allowed_topics=["weather", "forecast"],
    on_violation="redirect",
    redirect_message="I only know about weather.",
)
def get_weather(query: str) -> str:
    return llm.invoke(query)

🔒 Audit Mode (Non-Blocking)

Perfect for rolling out to production without breaking existing systems:

@sudo(scope="write:database", on_deny="log")
def update_record(record_id):
    # Logs violation but ALLOWS execution
    pass

👤 Human-in-the-Loop (Approval Workflows)

Require human approval for high-risk actions:

def slack_approval(agent, scope, context):
    # Send Slack message to manager
    response = ask_slack(f"Approve {agent.name} for {scope}?")
    return response == "yes"

@sudo(scope="delete:customer", on_deny=slack_approval)
def delete_customer(customer_id):
    print(f"Deleting customer {customer_id}")

🎯 Pydantic Integration

Enforce permissions on data models:

from agentsudo.integrations import ScopedModel

class RefundRequest(ScopedModel):
    _required_scope = "write:refunds"
    order_id: str
    amount: float

# Raises PermissionDeniedError if agent lacks scope
request = RefundRequest(order_id="123", amount=50.0)

🔌 FastAPI Integration

Protect REST endpoints with agent-based permissions:

from fastapi import FastAPI, Depends
from agentsudo import Agent
from agentsudo.adapters.fastapi import AgentSudoMiddleware, require_scope, register_agent

app = FastAPI()

# Register agents
reader = Agent(name="ReaderBot", scopes=["read:*"])
register_agent(reader, "reader-001")

app.add_middleware(AgentSudoMiddleware, agent_header="X-Agent-ID")

@app.get("/orders")
async def get_orders(agent = Depends(require_scope("read:orders"))):
    return {"orders": [...], "agent": agent.name}

🤖 Works with Any AI Framework

The @sudo decorator works with LangChain, LlamaIndex, CrewAI, AutoGen, or any Python code:

from langchain.tools import tool

@tool
@sudo(scope="read:data")
def my_tool(query: str) -> str:
    """Search data."""
    return f"Results for {query}"

⏱️ Session Expiry

Sessions automatically expire (like JWT tokens):

agent = Agent(
    name="TempBot",
    scopes=["read:*"],
    session_ttl=3600  # 1 hour
)

🌐 Wildcard Scopes

Use wildcards for flexible permissions:

agent = Agent(
    name="PowerUser",
    scopes=[
        "read:*",        # Read anything
        "write:orders*", # Write to orders, orders_archive, etc.
    ]
)

Real-World Example

from agentsudo import Agent, sudo

# E-commerce support bot
support_bot = Agent(
    name="CustomerSupportBot",
    scopes=[
        "read:customers",
        "read:orders",
        "write:refunds",
        "send:email"
    ]
)

@sudo(scope="write:refunds")
def issue_refund(order_id, amount):
    # Call Stripe API
    stripe.Refund.create(charge=order_id, amount=amount)

@sudo(scope="send:email")
def notify_customer(customer_id, message):
    # Send email via SendGrid
    sendgrid.send(to=customer_id, body=message)

# Bot can issue refunds and notify customers
with support_bot.start_session():
    issue_refund("ch_12345", 5000)
    notify_customer("cust_67890", "Your refund is processed")

Before vs After

Without AgentSudo With AgentSudo
❌ All agents share root API keys ✅ Each agent has unique identity
❌ Can't tell which agent did what ✅ Full audit trail
❌ No permission boundaries ✅ Fine-grained scopes
❌ Agents can do anything ✅ Principle of least privilege
❌ No approval workflows ✅ Human-in-the-loop for risky actions

Documentation


Cloud Dashboard

Monitor and manage your agents with the AgentSudo Dashboard at agentsudo.dev:

  • 📊 Real-time activity feed
  • 🔐 Visual scope management
  • 📈 Analytics and audit logs
  • 🎮 AI Playground for testing

Roadmap

  • FastAPI adapter for REST APIs
  • Cloud Dashboard (hosted at agentsudo.dev)
  • Guardrails - Topic filtering & prompt injection protection (v0.3.0)
  • npm package - JavaScript/TypeScript SDK for Next.js, Node.js, Edge Runtime
  • Rate limiting per agent
  • Budget limits (cost controls)
  • Slack/Teams integration for approvals
  • Policy DSL (YAML-based allow/deny rules)
  • Semantic topic matching (embeddings-based, not just keyword)
  • Output guardrails (filter agent responses)
  • Pre-built integrations (Stripe, Salesforce, Gmail, etc.)

Contributing

Contributions welcome! Please read CONTRIBUTING.md first.


License

MIT License - see LICENSE for details.


Support


Made with ❤️ by @xywa23

⭐ Star this repo if you find it useful!

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