netzilo 4.3.14

Netzilo AI Detection & Response (AIDR) — governance for Python AI agents.

netzilo (Python)

Netzilo AI Detection & Response(AIDR) for Python

Provides full governance for custom AI agents written in Python

How it works

The Netzilo client is compiled to a native shared library and loaded in-process via ctypes. Every LLM prompt, model response, and tool call your agent makes can be evaluated — allowed, blocked, or redacted — against policy pulled live from your Netzilo management server. No daemon, no sidecar — nothing to stand up.

Install

pip install netzilo

Wheels are published per platform (Linux, macOS, Windows) and are independent of your Python version.

Core API

import netzilo

netzilo.start(server="https://srv.netzilo.com",   # management server
              pat="nzl_...",                       # or setup_key="..."
              agent_name="my-agent")               # non-blocking

netzilo.is_running()                       # -> bool: True once policy has synced (ready to govern)
netzilo.mcp_gateway_port()                 # -> int: scanner gateway port in use (auto-picked)
allowed, reason = netzilo.is_allowed("Bash", {"command": "rm -rf /"})
netzilo.report_result("Bash", "<stdout>")  # post-tool observability
netzilo.flush()                            # force-deliver buffered events to management
netzilo.snapshot()                         # force AIDR behavior-graph snapshot + delivery (else hourly/at-stop)
netzilo.stop()                             # graceful stop (snapshots graph + flushes events; auto-registered atexit)

Parameters may be passed as keyword args (above) or a single config dict. config_path defaults to ~/.netzilo-sdk/config.json; ports auto-pick free ports (governance on by default). is_running() returns True only after the initial policy sync — poll it before kicking off work.

Evaluation runs inside the process — evaluate() calls the embedded engine directly, with no HTTP roundtrip and no local port to manage.

Advanced governance (enable_advanced_governance=True)

Framework adapters (below) govern at the tool/LLM-hook layer. Advanced governance adds deep inspection of the agent's own outbound traffic — covering every prompt, response, and tool call (including from subprocesses and libraries you have no hook for), with full content analysis and semantic classification:

netzilo.start(server=..., pat=..., agent_name=..., enable_advanced_governance=True)

It is fully automatic and requires no root and no changes to the host — the SDK prepares the process so the agent's traffic is inspected and continues to work normally. Inspected prompts/responses are analyzed and classified, emitting semantic.event records to your dashboard alongside the usual tool/LLM events. Default off; the framework adapters govern without it.

Framework adapters

Adapters are submodules of the one netzilo package — no separate installs. Each is a single call. The framework is imported lazily inside the adapter, so pip install netzilo never pulls crewai/langgraph/autogen unless you use it.

# CrewAI — registers before/after tool and LLM hooks process-wide
from netzilo.crewai import govern
govern(config={...})

# LangGraph — wrap nodes before compile()
import netzilo.langgraph
netzilo.langgraph.govern(graph, config={...})

# AutoGen — intervention handler
from netzilo.autogen import handler
runtime = SingleThreadedAgentRuntime(intervention_handlers=[handler(config={...})])

CrewAI: local vs CrewAI AMP

Local / self-hosted — put govern() once at the top of your entrypoint, before kickoff(). That's it.

import netzilo.crewai
netzilo.crewai.govern(config={"server": "...", "pat": "...", "agent_name": "my-agent"})

MyCrew().crew().kickoff(inputs={...})   # governed

CrewAI AMP — AMP's managed runtime only executes Flow @start/@listen methods; it skips module-level code, lifecycle hooks, and tool bodies. So the same govern() call goes inside a Flow @start step, then your crew runs in @listen. Deploy with [tool.crewai] type = "flow" in pyproject.toml, and always create the automation fresh as a flow (the type is locked at creation — re-pushing cannot convert an existing crew automation).

from crewai.flow import Flow, listen, start
import time

class MyFlow(Flow):

    @start()
    def init_governance(self):
        import netzilo, netzilo.crewai
        netzilo.crewai.govern(config={"server": "...", "pat": "...", "agent_name": "my-agent"})
        while not netzilo.is_running():   # wait until policy has synced
            time.sleep(0.5)

    @listen(init_governance)
    def run(self):
        return MyCrew().crew().kickoff(inputs={...})   # governed

Optional convenience extras pull a framework alongside netzilo: pip install netzilo[crewai], netzilo[langgraph], netzilo[autogen].

Configuration

start() / govern() accept a config dict. Common keys:

Key	Description
server	Your Netzilo management server URL. (management_url is a legacy alias.)
pat / setup_key	Credential used to enroll the agent.
agent_name	Identifier this agent reports as (used for event attribution).
config_path	Local client state (default: ~/.netzilo-sdk/config.json).
mcp_gateway_port	Optional; defaults to an auto-picked free port. mcp_gateway_port=0 disables governance.
enable_advanced_governance	Optional (default off). Deep inspection of the agent's own outbound traffic with semantic classification.
log_level / log_file	Logging verbosity and destination.

Notes

The native library is bundled inside the wheel; pip selects the correct one for your platform automatically. Adapters ship inside the same wheel as submodules — a single distribution, not per-framework packages.

Netzilo is a commercial product. See https://www.netzilo.com.