Skip to main content

Shrike SDK for AI agent action governance — drop-in wrapper for OpenAI, Anthropic, and Gemini that governs every tool call, prompt, and response server-side. Allow / approve / block. Free tier available.

Project description

Shrike Guard

PyPI version Python 3.8+ License: Apache 2.0

Shrike Guard is a Python SDK for the Shrike platform — AI governance for every AI interaction. It wraps OpenAI, Anthropic (Claude), and Google Gemini clients to automatically evaluate all prompts against policy before they reach the LLM. Govern LangChain agents, RAG pipelines, FastAPI chatbots, and any Python AI application with the same multi-layered cognitive pipeline.

Features

  • Drop-in replacement for OpenAI, Anthropic, and Gemini clients
  • Automatic prompt scanning for:
    • Prompt injection attacks
    • PII/sensitive data leakage
    • Jailbreak attempts
    • SQL injection
    • Path traversal
    • Malicious instructions
  • Fail-safe modes: Choose between fail-open (default) or fail-closed behavior
  • Async support: Works with both sync and async clients
  • Zero code changes: Just replace your import

What Shrike Detects

Shrike's backend runs a multi-stage detection pipeline with security rules across 7 compliance frameworks:

Framework Coverage
GDPR EU personal data — names, addresses, national IDs
HIPAA Protected health information (PHI)
ISO 27001 Information security — passwords, tokens, certificates
SOC 2 Secrets, credentials, API keys, cloud tokens
NIST AI risk management (IR 8596), cybersecurity framework (CSF 2.0)
PCI-DSS Cardholder data — PAN, CVV, expiry, track data
WebMCP MCP tool description injection, data exfiltration

Plus built-in detection for prompt injection, jailbreaks, social engineering, and dangerous requests.

Tiers

Detection depth depends on your tier. All tiers get the same SDK wrappers — tiers control which backend layers run.

Anonymous Community Pro Enterprise
Detection Layers L1-L5 L1-L7 L1-L8 L1-L9
API Key Not needed Free signup Paid Paid
Rate Limit 10/min 100/min 1,000/min
Scans/month 1,000 25,000 1,000,000

Anonymous (no API key): Pattern-based detection (L1-L5). Community (free): Adds LLM-powered semantic analysis. Register at shrikesecurity.com/signup — instant, no credit card.

Installation

pip install shrike-guard                      # OpenAI (included by default)
pip install shrike-guard[anthropic]            # + Anthropic Claude
pip install shrike-guard[gemini]               # + Google Gemini
pip install shrike-guard[all]                  # All providers

Quick Start

OpenAI

from shrike_guard import ShrikeOpenAI

# Replace 'from openai import OpenAI' with this
client = ShrikeOpenAI(
    api_key="sk-...",           # Your OpenAI API key
    shrike_api_key="shrike-...", # Your Shrike API key
)

# Use exactly like the regular OpenAI client
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)

print(response.choices[0].message.content)

Anthropic (Claude)

from shrike_guard import ShrikeAnthropic

client = ShrikeAnthropic(
    api_key="sk-ant-...",
    shrike_api_key="shrike-...",
)

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.content[0].text)

Google Gemini

from shrike_guard import ShrikeGemini

client = ShrikeGemini(
    api_key="AIza...",
    shrike_api_key="shrike-...",
)

model = client.GenerativeModel("gemini-pro")
response = model.generate_content("Hello!")

print(response.text)

Async Usage

import asyncio
from shrike_guard import ShrikeAsyncOpenAI

async def main():
    client = ShrikeAsyncOpenAI(
        api_key="sk-...",
        shrike_api_key="shrike-...",
    )

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

    print(response.choices[0].message.content)
    await client.close()

asyncio.run(main())

Configuration

Fail Modes

Choose how the SDK behaves when the security scan fails (timeout, network error, etc.):

# Fail-open (default): Allow requests if scan fails
# Best for: Most applications where availability is important
client = ShrikeOpenAI(
    api_key="sk-...",
    shrike_api_key="shrike-...",
    fail_mode="open",  # This is the default
)

# Fail-closed: Block requests if scan fails
# Best for: Security-critical applications
client = ShrikeOpenAI(
    api_key="sk-...",
    shrike_api_key="shrike-...",
    fail_mode="closed",
)

Timeout Configuration

client = ShrikeOpenAI(
    api_key="sk-...",
    shrike_api_key="shrike-...",
    scan_timeout=2.0,  # Timeout in seconds (default: 10.0)
)

Custom Endpoint

For self-hosted Shrike deployments:

client = ShrikeOpenAI(
    api_key="sk-...",
    shrike_api_key="shrike-...",
    shrike_endpoint="https://your-shrike-instance.com",
)

SQL and File Scanning

from shrike_guard import ScanClient

with ScanClient(api_key="shrike-...") as scanner:
    # Scan SQL queries for injection attacks
    sql_result = scanner.scan_sql("SELECT * FROM users WHERE id = 1")
    if not sql_result["safe"]:
        print(f"SQL threat: {sql_result['reason']}")

    # Scan file paths for path traversal
    file_result = scanner.scan_file("/app/data/output.csv")

    # Scan file content for secrets/PII
    content_result = scanner.scan_file("/tmp/config.py", "api_key = 'sk-...'")

Error Handling

from shrike_guard import ShrikeOpenAI, ShrikeBlockedError, ShrikeScanError

client = ShrikeOpenAI(
    api_key="sk-...",
    shrike_api_key="shrike-...",
    fail_mode="closed",  # To see scan errors
)

try:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": "Some prompt..."}]
    )
except ShrikeBlockedError as e:
    # Prompt was blocked due to security threat
    print(f"Blocked: {e.message}")
    print(f"Threat type: {e.threat_type}")
    print(f"Confidence: {e.confidence}")
except ShrikeScanError as e:
    # Scan failed (only raised with fail_mode="closed")
    print(f"Scan error: {e.message}")

Low-Level Scan Client

For more control, use the scan client directly:

from shrike_guard import ScanClient

with ScanClient(api_key="shrike-...") as scanner:
    result = scanner.scan("Check this prompt for threats")

    if result["safe"]:
        print("Prompt is safe!")
    else:
        print(f"Threat detected: {result['reason']}")

Compatibility

  • Python: 3.8+
  • LLM SDKs:
    • OpenAI SDK >=1.0.0
    • Anthropic SDK >=0.18.0 (optional: pip install shrike-guard[anthropic])
    • Google Generative AI >=0.3.0 (optional: pip install shrike-guard[gemini])
  • Works with:
    • OpenAI API
    • Azure OpenAI
    • OpenAI-compatible APIs (Ollama, vLLM, etc.)

Environment Variables

You can configure the SDK using environment variables:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export SHRIKE_API_KEY="shrike-..."
export SHRIKE_ENDPOINT="https://your-shrike-instance.com"

Scope and Limitations

Scanned Not Scanned
Input prompts (user messages) Streaming output from LLM
System prompts Image/audio content
Multi-modal text content Non-chat API calls
SQL queries
File paths and content

Why Input-Only Scanning?

Shrike Guard focuses on pre-flight protection — blocking malicious prompts BEFORE they reach the LLM. This:

  • Prevents prompt injection attacks at the source
  • Has zero latency impact on LLM responses
  • Catches the vast majority of threats at the input layer

Other Integration Surfaces

Shrike Guard is one of several ways to integrate with the Shrike platform:

  • MCP Servernpx shrike-mcp (GitHub)
  • TypeScript SDKnpm install shrike-guard (GitHub)
  • REST APIPOST https://api.shrikesecurity.com/agent/scan
  • LLM Gateway — Change one URL, scan everything
  • Browser Extension — Chrome/Edge for ChatGPT, Claude, Gemini
  • Dashboardshrikesecurity.com

Use Cases

Scenario How Shrike Guard Helps
LangChain / CrewAI agents Wrap your LLM client. Every agent action scanned before execution.
RAG pipelines Scan retrieved context + user queries for PII leakage and injection.
FastAPI chatbot Middleware-style integration. Scan every request before it hits the model.
Internal AI tools Protect Slack bots, email assistants, and internal AI applications.

Alternatives

Looking for a Python AI security SDK? Here's how Shrike Guard compares:

Feature Shrike Guard Lakera Prompt Armor
Drop-in OpenAI/Anthropic/Gemini wrapper Yes No No
Multi-layered evaluation pipeline Yes Limited Limited
PII detection + redaction Yes Partial No
Async support Yes Partial No
Free tier (no API key) Yes No No
Open source client Yes (Apache 2.0) No No

License

Apache 2.0

Support

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

shrike_guard-1.1.2.tar.gz (28.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

shrike_guard-1.1.2-py3-none-any.whl (35.4 kB view details)

Uploaded Python 3

File details

Details for the file shrike_guard-1.1.2.tar.gz.

File metadata

  • Download URL: shrike_guard-1.1.2.tar.gz
  • Upload date:
  • Size: 28.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.3.4 CPython/3.11.4 Darwin/25.4.0

File hashes

Hashes for shrike_guard-1.1.2.tar.gz
Algorithm Hash digest
SHA256 dbca90a98f86054004b8139c8a1c03da670f9a69880eb6dfe01c488263afdf57
MD5 513948bdb50d1f3d518feb7f0c31f7ad
BLAKE2b-256 a1b695dc4c2f50f18e0fdd81682a17186180fc4f93402599043af889f17bcd70

See more details on using hashes here.

File details

Details for the file shrike_guard-1.1.2-py3-none-any.whl.

File metadata

  • Download URL: shrike_guard-1.1.2-py3-none-any.whl
  • Upload date:
  • Size: 35.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.3.4 CPython/3.11.4 Darwin/25.4.0

File hashes

Hashes for shrike_guard-1.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 3c3b62653d6dbf5ebac2d411a4db2b5e9afb8113c733a9a9aefe27515450123b
MD5 2967b22f38104cf8018ed726ea29e287
BLAKE2b-256 b94a3b8407d1dd58b9d05a8b2383fad30411bb6a83a01ff1f7fa88364a515fc2

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page