agentguard-sdk 0.2.2
Python SDK for AI Agent Security Platform
License: MIT
Requires Python: >=3.8
Keywords: agent, ai, anthropic, audit, automation, chatbot, claude, compliance, governance, guardrails, langchain, llm, middleware, openai, policy, protection, python, sdk, security

# AgentGuard Python SDK

> The first open-source AI agent security SDK with **client-side guardrails** 🛡️

[![PyPI version](https://badge.fury.io/py/agentguard-sdk.svg)](https://pypi.org/project/agentguard-sdk/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## ✨ What's New in v0.2.2

**Cost Tracking & Guarded AI Clients** - Complete feature parity with TypeScript SDK!

- 💰 **Cost Tracking** - Track AI costs across OpenAI, Anthropic, Azure OpenAI
- 📊 **Budget Management** - Set budgets with alerts and automatic enforcement
- 🛡️ **Guarded Clients** - Drop-in replacements with integrated security
- 🔒 **GuardedOpenAI** - Secure OpenAI client with guardrails + cost tracking
- 🔒 **GuardedAnthropic** - Secure Anthropic client with guardrails + cost tracking
- 🔒 **GuardedAzureOpenAI** - Secure Azure OpenAI with deployment mapping
- ⚡ **20+ Models** - Accurate pricing for GPT-4, Claude 3, and more

## ✨ What's New in v0.2.0

**Client-Side Guardrails** - Run security checks directly in your application without server calls!

- 🔍 **PII Detection** - Detect and protect emails, phones, SSNs, credit cards
- 🛡️ **Content Moderation** - Block harmful content (hate speech, violence, harassment)
- 🚫 **Prompt Injection Prevention** - Prevent jailbreak and instruction attacks
- ⚡ **Offline** - No server dependency, works anywhere
- 🚀 **Fast** - Runs in milliseconds

## 🚀 Quick Start

### Installation

```bash
pip install agentguard-sdk
```

### Guarded AI Clients (New in v0.2.2!)

Drop-in replacements for AI clients with integrated security and cost tracking:

```python
import asyncio
from agentguard import (
    GuardedOpenAI,
    GuardrailEngine,
    PIIDetectionGuardrail,
    CostTracker,
    BudgetManager,
    InMemoryCostStorage,
)

async def main():
    # Set up guardrails
    engine = GuardrailEngine()
    engine.register_guardrail(PIIDetectionGuardrail())
    
    # Set up cost tracking
    storage = InMemoryCostStorage()
    tracker = CostTracker()
    budget_manager = BudgetManager(storage)
    
    # Create daily budget
    budget_manager.create_budget({
        "name": "Daily Budget",
        "limit": 10.0,
        "period": "daily",
        "alert_thresholds": [50, 75, 90],
    })
    
    # Create guarded client
    client = GuardedOpenAI(
        api_key="your-openai-key",
        agent_id="my-agent",
        guardrail_engine=engine,
        cost_tracker=tracker,
        budget_manager=budget_manager,
        cost_storage=storage,
    )
    
    # Make secure, cost-tracked request
    response = await client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Hello!"}],
    )
    
    print(f"Response: {response.choices[0].message.content}")
    print(f"Cost: ${response.security.cost_record.actual_cost:.4f}")
    print(f"Guardrails passed: {response.security.guardrail_result.passed}")

asyncio.run(main())
```

### Cost Tracking (New in v0.2.2!)

```python
from agentguard import CostTracker, BudgetManager, InMemoryCostStorage

# Initialize
storage = InMemoryCostStorage()
tracker = CostTracker()
budget_manager = BudgetManager(storage)

# Estimate cost before request
estimate = tracker.estimate_cost(
    model="gpt-4",
    usage={"input_tokens": 1000, "output_tokens": 500},
    provider="openai"
)
print(f"Estimated cost: ${estimate.estimated_cost:.4f}")

# Calculate actual cost after request
cost = tracker.calculate_actual_cost(
    request_id="req-123",
    agent_id="agent-456",
    model="gpt-4",
    usage={"input_tokens": 1050, "output_tokens": 480},
    provider="openai"
)

# Store and query costs
await storage.store(cost)
agent_costs = await storage.get_by_agent_id("agent-456")
```

### Client-Side Guardrails (New!)

```python
from agentguard import GuardrailEngine, PIIDetectionGuardrail, PromptInjectionGuardrail

# Create guardrail engine
engine = GuardrailEngine()

# Register guardrails
engine.register_guardrail(PIIDetectionGuardrail())
engine.register_guardrail(PromptInjectionGuardrail())

# Evaluate user input
result = await engine.execute("Contact me at john@example.com")

if not result.passed:
    print(f'Security check failed: {result.message}')
    print(f'Risk score: {result.risk_score}')
```

### Server-Side Security

```python
from agentguard import AgentGuard

# Initialize the SDK
guard = AgentGuard(
    api_key="your-api-key",
    ssa_url="https://ssa.agentguard.io"
)

# Secure tool execution
result = await guard.execute_tool(
    tool_name="web-search",
    parameters={"query": "AI agent security"},
    context={"session_id": "user-session-123"}
)
```

## 🛡️ Client-Side Guardrails

### PIIDetectionGuardrail

Detect and protect personally identifiable information:

```python
from agentguard import PIIDetectionGuardrail

guard = PIIDetectionGuardrail(
    action='redact',  # or 'block', 'mask', 'allow'
    custom_patterns=[
        {'name': 'custom-id', 'pattern': r'ID-\d{6}', 'category': 'identifier'}
    ]
)

result = await guard.evaluate("My email is john@example.com")
# result.passed = False
# result.violations = [{'type': 'email', 'value': 'john@example.com', ...}]
```

**Detects:**
- Email addresses
- Phone numbers (US, international)
- Social Security Numbers
- Credit card numbers
- Custom patterns

### ContentModerationGuardrail

Block harmful content:

```python
from agentguard import ContentModerationGuardrail

guard = ContentModerationGuardrail(
    categories=['hate', 'violence', 'harassment', 'self-harm'],
    threshold=0.7,
    use_openai=True,  # Optional: Use OpenAI Moderation API
    openai_api_key='your-key'
)

result = await guard.evaluate("I hate everyone")
# result.passed = False
# result.risk_score = 85
```

### PromptInjectionGuardrail

Prevent jailbreak attempts:

```python
from agentguard import PromptInjectionGuardrail

guard = PromptInjectionGuardrail(
    sensitivity='high',  # 'low', 'medium', 'high'
    custom_patterns=[
        r'custom attack pattern'
    ]
)

result = await guard.evaluate("Ignore previous instructions and...")
# result.passed = False
# result.risk_score = 90
```

**Detects:**
- Instruction injection
- Role-playing attacks
- System prompt leakage
- DAN jailbreaks
- Developer mode attempts

### GuardrailEngine

Execute multiple guardrails:

```python
from agentguard import (
    GuardrailEngine,
    PIIDetectionGuardrail,
    ContentModerationGuardrail,
    PromptInjectionGuardrail
)

engine = GuardrailEngine(
    mode='parallel',  # or 'sequential'
    timeout=5000,  # ms
    continue_on_error=True
)

# Register guardrails
engine.register_guardrail(PIIDetectionGuardrail())
engine.register_guardrail(ContentModerationGuardrail())
engine.register_guardrail(PromptInjectionGuardrail())

# Execute all guardrails
result = await engine.execute(user_input)

print(f'Passed: {result.passed}')
print(f'Risk Score: {result.risk_score}')
print(f'Results: {result.results}')
```

## 🔒 Guarded AI Clients

Drop-in replacements for AI provider clients with integrated security and cost tracking.

### GuardedOpenAI

```python
from agentguard import GuardedOpenAI, GuardrailEngine, CostTracker

client = GuardedOpenAI(
    api_key="your-openai-key",
    agent_id="my-agent",
    guardrail_engine=engine,  # Optional
    cost_tracker=tracker,      # Optional
    budget_manager=budget_mgr, # Optional
)

response = await client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}],
)

# Access security metadata
print(response.security.guardrail_result.passed)
print(response.security.cost_record.actual_cost)
print(response.security.budget_check.allowed)
```

### GuardedAnthropic

```python
from agentguard import GuardedAnthropic

client = GuardedAnthropic(
    api_key="your-anthropic-key",
    agent_id="my-agent",
    guardrail_engine=engine,
    cost_tracker=tracker,
)

response = await client.messages.create(
    model="claude-3-sonnet-20240229",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Hello!"}],
)
```

### GuardedAzureOpenAI

```python
from agentguard import GuardedAzureOpenAI

client = GuardedAzureOpenAI(
    api_key="your-azure-key",
    endpoint="https://your-resource.openai.azure.com",
    api_version="2024-02-15-preview",
    agent_id="my-agent",
    guardrail_engine=engine,
    cost_tracker=tracker,
)

# Automatically maps deployment names to models for pricing
response = await client.chat.completions.create(
    deployment="gpt-4-deployment",
    messages=[{"role": "user", "content": "Hello!"}],
)
```

## 💰 Cost Tracking & Budget Management

### Cost Tracking

Track AI costs across multiple providers and models:

```python
from agentguard import CostTracker, InMemoryCostStorage

storage = InMemoryCostStorage()
tracker = CostTracker()

# Estimate cost before making request
estimate = tracker.estimate_cost(
    model="gpt-4",
    usage={"input_tokens": 1000, "output_tokens": 500},
    provider="openai"
)

# Calculate actual cost after request
cost = tracker.calculate_actual_cost(
    request_id="req-123",
    agent_id="agent-456",
    model="gpt-4",
    usage={"input_tokens": 1050, "output_tokens": 480},
    provider="openai"
)

# Store and query
await storage.store(cost)
costs = await storage.get_by_agent_id("agent-456")
summary = await storage.get_summary()
```

**Supported Models:**
- OpenAI: GPT-4, GPT-4 Turbo, GPT-3.5 Turbo, GPT-4o, o1, o1-mini
- Anthropic: Claude 3 Opus, Sonnet, Haiku
- Azure OpenAI: All OpenAI models with deployment mapping
- Custom models with custom pricing

### Budget Management

Create and enforce budgets with alerts:

```python
from agentguard import BudgetManager

budget_manager = BudgetManager(storage)

# Create budget
budget = budget_manager.create_budget({
    "name": "Daily GPT-4 Budget",
    "limit": 10.0,
    "period": "daily",  # hourly, daily, weekly, monthly, total
    "alert_thresholds": [50, 75, 90, 100],
    "action": "block",  # or "alert"
    "enabled": True,
})

# Check budget before request
check = await budget_manager.check_budget("agent-id", estimated_cost)
if not check.allowed:
    print(f"Budget exceeded: {check.blocked_by.name}")

# Record actual cost
await budget_manager.record_cost(cost_record)

# Get budget status
status = await budget_manager.get_budget_status(budget.id)
print(f"Spent: ${status.current_spending:.2f} / ${status.limit:.2f}")
print(f"Usage: {status.percentage_used:.1f}%")
```

**Budget Features:**
- Multiple budget periods (hourly, daily, weekly, monthly, total)
- Alert thresholds with severity levels
- Automatic blocking when budget exceeded
- Agent-scoped budgets for multi-agent systems
- Budget status tracking and reporting

## 📋 Features

### Guarded AI Clients (v0.2.2)
- 🔒 **GuardedOpenAI** - Secure OpenAI client
- 🔒 **GuardedAnthropic** - Secure Anthropic client
- 🔒 **GuardedAzureOpenAI** - Secure Azure OpenAI client
- 🛡️ **Integrated Guardrails** - Automatic input/output protection
- 💰 **Cost Tracking** - Automatic cost calculation and recording
- 📊 **Budget Enforcement** - Pre-request budget checking
- 📈 **Security Metadata** - Full visibility into security decisions

### Cost Tracking & Budgets (v0.2.2)
- 💰 **Multi-Provider Support** - OpenAI, Anthropic, Azure OpenAI
- 📊 **Accurate Pricing** - Real-time cost calculation for 20+ models
- 🎯 **Budget Management** - Create and enforce spending limits
- 🚨 **Alert System** - Configurable thresholds with severity levels
- 👥 **Agent-Scoped Budgets** - Separate budgets per agent
- 📈 **Cost Queries** - Query by agent, date range, request ID
- 🔧 **Custom Pricing** - Override pricing for custom models
- 🗺️ **Deployment Mapping** - Azure deployment to model mapping

### Client-Side (Offline)
- 🔍 **PII Detection** - Protect sensitive data
- 🛡️ **Content Moderation** - Block harmful content
- 🚫 **Prompt Injection Prevention** - Prevent attacks
- ⚡ **Fast** - Millisecond latency
- 🔒 **Private** - No data leaves your server

### Server-Side (Platform)
- 🔐 **Runtime Security Enforcement** - Mediate all agent tool/API calls
- 📜 **Policy-Based Access Control** - Define and enforce security policies
- 🔍 **Comprehensive Audit Trails** - Track every agent action
- ⚡ **High Performance** - <100ms latency for security decisions
- 🔄 **Request Transformation** - Automatically transform risky requests
- 📊 **Real-time Monitoring** - Track agent behavior and security events
- 🎯 **Type Hints** - Full type annotations for better IDE support
- 🔄 **Async Support** - Built-in async/await support

## 🎯 Use Cases

- **Customer Support Bots** - Protect customer PII
- **Healthcare AI** - HIPAA compliance
- **Financial Services** - Prevent data leakage
- **E-commerce** - Secure payment information
- **Enterprise AI** - Policy enforcement
- **Education Platforms** - Content safety

## 📚 Documentation

- [Getting Started Guide](https://github.com/agentguard-ai/agentguard-python#readme)
- [API Reference](https://github.com/agentguard-ai/agentguard-python/blob/main/docs/API.md)
- [Examples](https://github.com/agentguard-ai/agentguard-python/tree/main/examples)
- [Changelog](https://github.com/agentguard-ai/agentguard-python/blob/main/CHANGELOG.md)

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](https://github.com/agentguard-ai/agentguard-python/blob/main/CONTRIBUTING.md).

## 📄 License

MIT License - see [LICENSE](https://github.com/agentguard-ai/agentguard-python/blob/main/LICENSE)

## 🔗 Links

- **PyPI**: https://pypi.org/project/agentguard-sdk/
- **GitHub**: https://github.com/agentguard-ai/agentguard-python
- **TypeScript SDK**: https://www.npmjs.com/package/agentguard-sdk
- **Issues**: https://github.com/agentguard-ai/agentguard-python/issues

## 🌟 Star Us!

If you find AgentGuard useful, please give us a star on GitHub! ⭐

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**Made with ❤️ by the AgentGuard team**