declaw 1.1.4

Secure runtime for AI agents with built-in guardrails -- PII scanning, prompt injection defense, network isolation, and egress filtering.

declaw

Secure runtime for AI agents. Spin up isolated sandboxes in milliseconds with built-in guardrails — PII scanning, prompt injection defense, network isolation, and egress filtering.

Install

pip install declaw

Quick Start

from declaw import Sandbox

sandbox = Sandbox.create(api_key='your-api-key', template='base', timeout=60)

# Run commands
result = sandbox.commands.run('echo "Hello from a secure sandbox"')
print(result.stdout)

# Read/write files
sandbox.files.write('/tmp/hello.txt', 'Hello World')
content = sandbox.files.read('/tmp/hello.txt')

# Clean up
sandbox.kill()

Async

from declaw import AsyncSandbox

sandbox = await AsyncSandbox.create(api_key='your-api-key', template='python', timeout=60)
result = await sandbox.commands.run('python3 -c "print(1+1)"')
await sandbox.kill()

Why Declaw?

AI agents need to execute code, call APIs, and interact with the world. Declaw gives them a secure sandbox to do it — with built-in guardrails that protect your users and infrastructure.

• Sub-10ms sandbox creation — pre-warmed VM pool, no cold starts
• Network isolation — per-sandbox firewall with domain and CIDR rules
• Full file system — read, write, upload, download files in the sandbox

Security & Guardrails

Every outbound request from the sandbox passes through a configurable security pipeline.

PII Scanning

Detect and redact sensitive data before it leaves the sandbox.

from declaw import Sandbox, SecurityPolicy, PIIConfig

sandbox = Sandbox.create(
    security=SecurityPolicy(
        pii=PIIConfig(
            enabled=True,
            types=['ssn', 'credit_card', 'email', 'phone', 'api_key'],
            action='redact',
        ),
    ),
)

Prompt Injection Defense

Block prompt injection attempts in agent outputs.

from declaw import SecurityPolicy, InjectionDefenseConfig

sandbox = Sandbox.create(
    security=SecurityPolicy(
        injection_defense=InjectionDefenseConfig(
            enabled=True,
            action='block',
            threshold=0.85,
        ),
    ),
)

Toxicity, Code Security & Invisible Text

sandbox = Sandbox.create(
    security=SecurityPolicy(
        toxicity=ToxicityConfig(enabled=True, action='block', threshold=0.7),
        code_security=CodeSecurityConfig(enabled=True, action='log'),
        invisible_text=InvisibleTextConfig(enabled=True, action='block'),
    ),
)

Network Policies

from declaw import Sandbox, NetworkPolicy

# Allow only specific domains
sandbox = Sandbox.create(
    network=NetworkPolicy(allow_out=['api.openai.com', 'huggingface.co']),
)

# Block all egress
isolated = Sandbox.create(
    network=NetworkPolicy(deny_out=['ALL_TRAFFIC']),
)

Data Transformation

Transform sensitive values in-flight.

from declaw import SecurityPolicy, TransformationRule

sandbox = Sandbox.create(
    security=SecurityPolicy(
        transformations=[
            TransformationRule(
                pattern=r'sk-[a-zA-Z0-9]+',
                replacement='[API_KEY]',
                direction='egress',
            ),
        ],
    ),
)

Combining Guardrails

All guardrails compose — enable multiple and they run in sequence:

sandbox = Sandbox.create(
    api_key='your-api-key',
    template='ai-agent',
    timeout=300,
    network=NetworkPolicy(allow_out=['api.openai.com', 'api.anthropic.com']),
    security=SecurityPolicy(
        pii=PIIConfig(enabled=True, action='redact', types=['ssn', 'credit_card']),
        injection_defense=InjectionDefenseConfig(enabled=True, action='block'),
        toxicity=ToxicityConfig(enabled=True, action='log'),
        invisible_text=InvisibleTextConfig(enabled=True, action='block'),
    ),
)

Templates

Template	Description
base	Minimal Linux
python	Python 3.12 with pip
node	Node.js 22 LTS with npm
code-interpreter	Python with data science libraries
ai-agent	Python + Node.js + AI/ML tools
mcp-server	MCP server runtime
web-dev	Node.js + browser testing
devops	Docker, Terraform, kubectl

API

# Create sandbox
sandbox = Sandbox.create(template, api_key, timeout, network, security)

# Commands
result = sandbox.commands.run('ls -la')
for chunk in sandbox.commands.stream('python script.py'):
    print(chunk)

# Files
sandbox.files.write(path, content)
data = sandbox.files.read(path)
entries = sandbox.files.list('/')

# PTY (interactive terminal)
pty = sandbox.pty.create(cols=80, rows=24)

# Lifecycle
sandbox.kill()

License

Apache-2.0