Local Server (OpenAI-Compatible)

Overview

mx.serve() starts a local FastAPI server that exposes OpenAI-compatible chat and completion endpoints. This makes it straightforward to use Axionic-hosted or locally loaded models with any tool or library that speaks the OpenAI API format, without changing your existing code.

`serve()`

string

The model name to serve. Uses the currently loaded local model if omitted, or the default remote model.

string

default:"0.0.0.0"

Host address to bind the server to.

integer

default:"8000"

Port to listen on.

boolean

default:"false"

If true, uses vLLM as the inference backend instead of the default PyTorch / remote path.

list[str]

Behavior names to monitor and auto-correct for all requests handled by this server instance.

Endpoints

Once the server is running:

Endpoint	Method	Description
`/v1/chat/completions`	POST	OpenAI chat format (`messages` array)
`/v1/completions`	POST	OpenAI completion format (`prompt` string)

Both accept standard OpenAI request bodies.

Axionic Extensions

Pass Axionic-specific parameters via extra_body on the OpenAI Python client, or include them directly in the JSON request body:

Field	Type	Description
`steering_vector_id`	string	ID of a steering vector to apply during generation
`steering_strength`	float	Multiplier for the steering vector magnitude
`policy`	object	Inline runtime policy to apply during generation
`policy_id`	string	Saved policy ID to apply during generation
`behavior_names`	list[str]	Behaviors to monitor; correction applied if drift detected
`force_steering`	list[str]	Behaviors to steer toward unconditionally

mx.serve(corrected_behaviors=[...]) applies those behavior monitors to every request handled by that server instance. The CLI form, mechanex serve, exposes --host, --port, and --use-vllm; use Python when you need process-wide corrected_behaviors.

Example

import threading
import mechanex as mx
from openai import OpenAI

mx.set_key("ax_your_key_here")

# Start the server in a background thread
server_thread = threading.Thread(
    target=mx.serve,
    kwargs={"port": 8000, "corrected_behaviors": ["safety"]},
    daemon=True,
)
server_thread.start()

# Use the standard OpenAI client pointed at the local server
client = OpenAI(
    api_key="ax_your_key_here",
    base_url="http://localhost:8000/v1",
)

response = client.chat.completions.create(
    model="mechanex-mini",
    messages=[{"role": "user", "content": "Explain gradient descent."}],
    extra_body={
        "steering_vector_id": "sv_abc123",
        "steering_strength": 1.2,
    },
)
print(response.choices[0].message.content)

Getting Started

SDK Reference

CLI

Local Server (OpenAI-Compatible)

Overview

`serve()`

Endpoints

Axionic Extensions

Example

​Overview

​serve()

​Endpoints

​Axionic Extensions

​Example

Overview

`serve()`

Endpoints

Axionic Extensions

Example