agent-airlock 0.1.5

The Pydantic-based Firewall for MCP Servers. Stops hallucinated tool calls, validates schemas, and sandboxes dangerous operations.

The Open-Source Firewall for AI Agents

One decorator. Zero trust. Full control.

Get Started in 30 Seconds · Why Airlock? · All Frameworks · Docs

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┌────────────────────────────────────────────────────────────────┐
│  🤖 AI Agent: "Let me help clean up disk space..."            │
│                           ↓                                    │
│               rm -rf / --no-preserve-root                      │
│                           ↓                                    │
│  ┌──────────────────────────────────────────────────────────┐  │
│  │  🛡️ AIRLOCK: BLOCKED                                     │  │
│  │                                                          │  │
│  │  Reason: Matches denied pattern 'rm_*'                   │  │
│  │  Policy: STRICT_POLICY                                   │  │
│  │  Fix: Use approved cleanup tools only                    │  │
│  └──────────────────────────────────────────────────────────┘  │
└────────────────────────────────────────────────────────────────┘

---

🎯 30-Second Quickstart

pip install agent-airlock

from agent_airlock import Airlock

@Airlock()
def transfer_funds(account: str, amount: int) -> dict:
    return {"status": "transferred", "amount": amount}

# LLM sends amount="500" (string) → BLOCKED with fix_hint
# LLM sends force=True (invented arg) → STRIPPED silently
# LLM sends amount=500 (correct) → EXECUTED safely

That's it. Your function now has ghost argument stripping, strict type validation, and self-healing errors.

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🧠 The Problem No One Talks About

The Hype "MCP has 16,000+ servers on GitHub!" "OpenAI adopted it!" "Linux Foundation hosts it!"

The Reality LLMs hallucinate tool calls. Every. Single. Day. Claude invents arguments that don't exist GPT-4 sends "100" when you need 100 Agents chain 47 calls before one deletes prod data

Enterprise solutions exist: Prompt Security ($50K/year), Pangea (proxy your data), Cisco ("coming soon").

We built the open-source alternative. One decorator. No vendor lock-in. Your data never leaves your infrastructure.

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✨ What You Get

Ghost Args Strip LLM-invented params

Strict Types No silent coercion

Self-Healing LLM-friendly errors

E2B Sandbox Isolated execution

RBAC Role-based access

PII Mask Auto-redact secrets

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📋 Table of Contents

Click to expand full navigation

• 30-Second Quickstart
• The Problem
• What You Get
• Core Features
  • E2B Sandbox
  • Security Policies
  • Cost Control
  • PII Masking
• Framework Compatibility
• FastMCP Integration
• Comparison
• Installation
• OWASP Compliance
• Performance
• Documentation
• Contributing
• Support

---

🔥 Core Features

🔒 E2B Sandbox Execution

from agent_airlock import Airlock, STRICT_POLICY

@Airlock(sandbox=True, sandbox_required=True, policy=STRICT_POLICY)
def execute_code(code: str) -> str:
    """Runs in an E2B Firecracker MicroVM. Not on your machine."""
    exec(code)
    return "executed"

Feature	Value
Boot time	~125ms cold, <200ms warm
Isolation	Firecracker MicroVM
Fallback	sandbox_required=True blocks local execution

---

📜 Security Policies

from agent_airlock import (
    PERMISSIVE_POLICY,      # Dev - no restrictions
    STRICT_POLICY,          # Prod - rate limited, agent ID required
    READ_ONLY_POLICY,       # Analytics - query only
    BUSINESS_HOURS_POLICY,  # Dangerous ops 9-5 only
)

# Or build your own:
from agent_airlock import SecurityPolicy

MY_POLICY = SecurityPolicy(
    allowed_tools=["read_*", "query_*"],
    denied_tools=["delete_*", "drop_*", "rm_*"],
    rate_limits={"*": "1000/hour", "write_*": "100/hour"},
    time_restrictions={"deploy_*": "09:00-17:00"},
)

---

💰 Cost Control

A runaway agent can burn $500 in API costs before you notice.

from agent_airlock import Airlock, AirlockConfig

config = AirlockConfig(
    max_output_chars=5000,    # Truncate before token explosion
    max_output_tokens=2000,   # Hard limit on response size
)

@Airlock(config=config)
def query_logs(query: str) -> str:
    return massive_log_query(query)  # 10MB → 5KB

ROI: 10MB logs = ~2.5M tokens = $25/response. Truncated = ~1.25K tokens = $0.01. 99.96% savings.

---

🔐 PII & Secret Masking

config = AirlockConfig(
    mask_pii=True,      # SSN, credit cards, phones, emails
    mask_secrets=True,  # API keys, passwords, JWTs
)

@Airlock(config=config)
def get_user(user_id: str) -> dict:
    return db.users.find_one({"id": user_id})

# LLM sees: {"name": "John", "ssn": "[REDACTED]", "api_key": "sk-...XXXX"}

12 PII types detected · 4 masking strategies · Zero data leakage

---

🔌 Framework Compatibility

The Golden Rule: @Airlock must be closest to the function definition.

@framework_decorator    # ← Framework sees secured function
@Airlock()             # ← Security layer (innermost)
def my_function():     # ← Your code

LangChain / LangGraph from langchain_core.tools import tool from agent_airlock import Airlock @tool @Airlock() def search(query: str) -> str: """Search for information.""" return f"Results for: {query}"	OpenAI Agents SDK from agents import function_tool from agent_airlock import Airlock @function_tool @Airlock() def get_weather(city: str) -> str: """Get weather for a city.""" return f"Weather in {city}: 22°C"
PydanticAI from pydantic_ai import Agent from agent_airlock import Airlock @Airlock() def get_stock(symbol: str) -> str: return f"Stock {symbol}: $150" agent = Agent("openai:gpt-4o", tools=[get_stock])	CrewAI from crewai.tools import tool from agent_airlock import Airlock @tool @Airlock() def search_docs(query: str) -> str: """Search internal docs.""" return f"Found 5 docs for: {query}"

More frameworks: LlamaIndex, AutoGen, smolagents, Anthropic

LlamaIndex

from llama_index.core.tools import FunctionTool
from agent_airlock import Airlock

@Airlock()
def calculate(expression: str) -> int:
    return eval(expression, {"__builtins__": {}})

calc_tool = FunctionTool.from_defaults(fn=calculate)

AutoGen

from autogen import ConversableAgent
from agent_airlock import Airlock

@Airlock()
def analyze_data(dataset: str) -> str:
    return f"Analysis of {dataset}: mean=42.5"

assistant = ConversableAgent(name="analyst", llm_config={"model": "gpt-4o"})
assistant.register_for_llm()(analyze_data)

smolagents

from smolagents import tool
from agent_airlock import Airlock

@tool
@Airlock(sandbox=True)
def run_code(code: str) -> str:
    """Execute in E2B sandbox."""
    exec(code)
    return "Executed"

Anthropic (Direct API)

from agent_airlock import Airlock

@Airlock()
def get_weather(city: str) -> str:
    return f"Weather in {city}: 22°C"

# Use in tool handler
def handle_tool_call(name, inputs):
    if name == "get_weather":
        return get_weather(**inputs)  # Airlock validates

Complete Examples

Framework	Example	Key Features
LangChain	langchain_integration.py	@tool, AgentExecutor
LangGraph	langgraph_integration.py	StateGraph, ToolNode
OpenAI Agents	openai_agents_sdk_integration.py	Handoffs, manager pattern
PydanticAI	pydanticai_integration.py	Dependencies, structured output
LlamaIndex	llamaindex_integration.py	ReActAgent
CrewAI	crewai_integration.py	Crews, roles
AutoGen	autogen_integration.py	ConversableAgent
smolagents	smolagents_integration.py	CodeAgent, E2B
Anthropic	anthropic_integration.py	Direct API

---

⚡ FastMCP Integration

from fastmcp import FastMCP
from agent_airlock.mcp import secure_tool, STRICT_POLICY

mcp = FastMCP("production-server")

@secure_tool(mcp, policy=STRICT_POLICY)
def delete_user(user_id: str) -> dict:
    """One decorator: MCP registration + Airlock protection."""
    return db.users.delete(user_id)

---

🏆 Why Not Enterprise Vendors?

	Prompt Security	Pangea	Agent-Airlock
Pricing	$50K+/year	Enterprise	Free forever
Integration	Proxy gateway	Proxy gateway	One decorator
Self-Healing	❌	❌	✅
E2B Sandboxing	❌	❌	✅ Native
Your Data	Their servers	Their servers	Never leaves you
Source Code	Closed	Closed	MIT Licensed

We're not anti-enterprise. We're anti-gatekeeping. Security for AI agents shouldn't require a procurement process.

---

📦 Installation

# Core (validation + policies + sanitization)
pip install agent-airlock

# With E2B sandbox support
pip install agent-airlock[sandbox]

# With FastMCP integration
pip install agent-airlock[mcp]

# Everything
pip install agent-airlock[all]

# E2B key for sandbox execution
export E2B_API_KEY="your-key-here"

---

🛡️ OWASP Compliance

Agent-Airlock mitigates the OWASP Top 10 for LLMs (2025):

OWASP Risk	Mitigation
LLM01: Prompt Injection	Strict type validation blocks injected payloads
LLM05: Improper Output Handling	PII/secret masking sanitizes outputs
LLM06: Excessive Agency	Rate limits + RBAC prevent runaway agents
LLM09: Misinformation	Ghost argument rejection blocks hallucinated params

---

📊 Performance

Metric	Value
Tests	629 passing
Coverage	86% (80% enforced in CI)
Validation overhead	<50ms
Sandbox cold start	~125ms
Sandbox warm pool	<200ms
Framework integrations	9
Core dependencies	0 (Pydantic only)

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📖 Documentation

Resource	Description
Examples	9 framework integrations with copy-paste code
Security Guide	Production deployment checklist
API Reference	Every function, every parameter

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👤 About

Built by Sattyam Jain — AI infrastructure engineer.

This started as an internal tool after watching an agent hallucinate its way through a production database. Now it's yours.

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🤝 Contributing

We review every PR within 48 hours.

git clone https://github.com/sattyamjjain/agent-airlock
cd agent-airlock
pip install -e ".[dev]"
pytest tests/ -v

• Bug? Open an issue
• Feature idea? Start a discussion
• Want to contribute? See open issues

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💖 Support

If Agent-Airlock saved your production database:

• ⭐ Star this repo — Helps others discover it
• 🐛 Report bugs — Open an issue
• 📣 Spread the word — Tweet, blog, share

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⭐ Star History

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Built with 🛡️ by Sattyam Jain

_{Making AI agents safe, one decorator at a time.}

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Sources: This README follows best practices from awesome-readme, Best-README-Template, and the GitHub Blog.