controlzero 1.1.0

AI agent governance: policies, audit, and observability for tool calls. Works locally with no signup.

control-zero

AI agent governance for Python. Policies, audit, and observability for tool calls. Works locally with no signup.

v1.0.0 is a complete rewrite. If you depend on control-zero<1.0.0 (the hosted-mode SDK), pin your requirement: control-zero<1.0.0 to stay on the legacy v0.3.x. The new v1.0.0+ is a local-first SDK with a different API surface; see the migration guide for details.

Hello World

from controlzero import Client

cz = Client(policy={
    "rules": [
        {"deny":  "delete_*", "reason": "Hello World: deletes are blocked"},
        {"allow": "*",        "reason": "Hello World: everything else is fine"},
    ]
})

print(cz.guard("delete_file", {"path": "/tmp/foo"}).decision)  # "deny"
print(cz.guard("read_file",   {"path": "/tmp/foo"}).decision)  # "allow"

11 lines. No API key. No signup. Run it.

Install

pip install control-zero

Why

Your AI agents call tools. Some of those tools should never be called by an agent without a human in the loop. controlzero is the policy layer between the model's output and the tool execution. Decisions are fail-closed by default.

You can use it offline with a local YAML file or Python dict. When you want to share policies across a team or get a hosted audit dashboard, sign up at controlzero.ai and set CONTROLZERO_API_KEY.

Quickstart with the CLI

# 1. Generate a starter policy file with examples and comments
controlzero init

# 2. Edit controlzero.yaml in your editor

# 3. Validate it
controlzero validate

# 4. Test a tool call against the policy
controlzero test delete_file

The generated controlzero.yaml is the tutorial. It ships with annotated rules covering the common patterns: allow lists, deny lists, wildcards, and the catch-all.

Templates available:

• controlzero init — Hello World template (default)
• controlzero init -t rag — RAG agent template (block exfiltration)
• controlzero init -t mcp — MCP server template
• controlzero init -t cost-cap — model allow-listing and cost guards

Loading a policy

Three ways:

from controlzero import Client

# From a Python dict
cz = Client(policy={
    "rules": [
        {"deny": "delete_*"},
        {"allow": "read_*"},
    ]
})

# From a YAML file
cz = Client(policy_file="./controlzero.yaml")

# From an environment variable
# (set CONTROLZERO_POLICY_FILE=./controlzero.yaml)
cz = Client()

If ./controlzero.yaml exists in the current directory, it is picked up automatically. No environment variable needed.

Policy schema

version: '1'
rules:
  # Block any tool whose name starts with "delete_"
  - deny: 'delete_*'
    reason: 'Deletes need human approval'

  # Allow specific known-good tools
  - allow: 'search'
  - allow: 'read_*'

  # tool:method syntax
  - allow: 'github:list_*'
  - deny: 'github:delete_repo'

  # Catch-all
  - deny: '*'
    reason: 'Default deny'

Rules are evaluated top to bottom. The first match wins. If no rule matches, the call is denied (fail-closed).

Local audit log

When running without an API key, every decision is written to ./controlzero.log with daily rotation and 30-day retention. Tail it:

controlzero tail

Configure rotation via the client:

cz = Client(
    policy_file="./controlzero.yaml",
    log_path="./logs/controlzero.log",
    log_rotation="10 MB",        # rotate at 10 MB, or "daily", or "1 hour"
    log_retention="30 days",
    log_compression="gz",        # gzip rotated files
    log_format="json",           # or "pretty"
)

When CONTROLZERO_API_KEY is set, audit ships to the remote dashboard and these log_* options are ignored with a warning.

Hybrid mode

If you set both an API key AND pass a local policy, the local policy overrides the dashboard policy and you get a loud WARN log on init:

WARNING: controlzero: manual policy override detected. ...

This is intentional: it makes accidental prod bypass impossible to miss. For prod environments, opt into strict mode to raise instead:

cz = Client(api_key="cz_live_...", policy=local_policy, strict_hosted=True)
# RuntimeError: manual policy override detected ...

Framework examples

Full integration guides at docs.controlzero.ai/sdk/integrations:

• LangChain
• LangGraph
• CrewAI
• OpenAI Agents SDK
• Anthropic tool use
• Pydantic AI
• AutoGen
• MCP servers
• Raw HTTP / no framework

Hosted mode

When you want a dashboard, audit search, team policies, and approval workflows, sign up at controlzero.ai and set the API key:

import os
os.environ["CONTROLZERO_API_KEY"] = "cz_live_..."

from controlzero import Client
cz = Client()  # picks up the API key from env, audit ships remote

License

Apache 2.0