Self-Managed

The self-managed motus exposes any agent as an HTTP server with session-based conversations using motus serve. Each message spawns a fresh worker subprocess, so your agent runs in complete isolation. No shared state between requests, and a crash in one turn never affects another.

Quick start

Define your agent in a Python file, then start the server:

myapp.py

from motus.agent import ReActAgent
from motus.models import AnthropicChatClient

client = AnthropicChatClient()

agent = ReActAgent(
    client=client,
    model_name="claude-opus-4-6",
    system_prompt="You are a helpful assistant.",
)

# Start the server
motus serve start myapp:agent --port 8000

# Chat interactively in a new terminal
motus serve chat http://localhost:8000

Agent types

Every agent type follows the same turn contract: receive a ChatMessage and the session’s prior state, return a response ChatMessage and updated state. All agent types run in worker subprocesses and must be importable from the module level.

Parameter	Type	Description
`message`	`ChatMessage`	The new user message (constructed by the server from the HTTP request).
`state`	`list[ChatMessage]`	The session’s state from the previous turn (empty list on first turn).

Return value: tuple[ChatMessage, list[ChatMessage]] the response message (surfaced to the HTTP client) and the updated state (stored in the session). The agent owns the state and can append, compact, or restructure it freely.

ServableAgent
Google ADK
Anthropic SDK
OpenAI Agents SDK
Callable function

Any object with a conforming run_turn method can be served directly. This is a runtime checkable. Protocolinheritance is not required.

from motus.serve import ServableAgent
from motus.models.base import ChatMessage

class MyAgent(ServableAgent):
    async def run_turn(
        self,
        message: ChatMessage,
        state: list[ChatMessage],
    ) -> tuple[ChatMessage, list[ChatMessage]]:
        response = ChatMessage.assistant_message(content="hello")
        return response, state + [message, response]

Built-in implementations include AgentBase and all of its subclasses (such as ReActAgent).

Google ADK agents are supported via motus.google_adk.agents.Agent, a subclass of the ADK Agent that implements ServableAgent. Session history is replayed automatically each turn.

from motus.google_adk.agents.llm_agent import Agent

agent = Agent(
    model="gemini-2.0-flash",
    name="my_agent",
    instruction="You are a helpful assistant.",
)

motus serve start myapp:agent

Requires the optional google-adk dependency.

Anthropic SDK tool runners are supported via motus.anthropic.ToolRunner. Define tools with the @beta_async_tool decorator and pass the runner directly. A fresh runner is created per turn.

from motus.anthropic import ToolRunner, beta_async_tool

@beta_async_tool
async def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return f"Sunny in {city}"

runner = ToolRunner(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    tools=[get_weather],
    system="You are a helpful assistant.",
)

motus serve start myapp:runner

Requires anthropic>=0.49.0. Pass max_iterations to limit the tool-use loop.

OpenAI Agents SDK agents are supported via auto-detection — no adapter import needed. Guardrail tripwire exceptions are caught and returned as refusal messages. Structured output is serialized to JSON.

from agents import Agent

agent = Agent(
    name="my_agent",
    instructions="You are a helpful assistant.",
)

motus serve start myapp:agent

Requires the optional openai-agents dependency.

Plain functions with the signature (message, state) -> (response, state) are supported. Both sync and async functions work:

from motus.models.base import ChatMessage

# Sync
def my_agent(message, state):
    response = ChatMessage.assistant_message(content="hello")
    return response, state + [message, response]

# Async
async def my_agent(message, state):
    result = await some_api_call(message.content)
    response = ChatMessage.assistant_message(content=result)
    return response, state + [message, response]

Session lifecycle

Each conversation is a session. A session moves through the following states:

Status	Description
`idle`	Waiting for input. Initial state after creation.
`running`	Processing a message. Concurrent sends are rejected with `409`.
`error`	Agent raised an exception. The `error` field contains the message.

idle ──POST /messages──▶ running ──success──▶ idle
                               └──failure──▶ error
error ──POST /messages──▶ running

A session in error state can receive new messages and transitions back to running. Sessions are held in memory and do not persist across server restarts.

When --ttl is set, idle and errored sessions whose last activity exceeds the TTL are swept by a background task. When --timeout is set, agent turns that exceed the limit are killed and the session transitions to error with an "Agent timed out" message.

Architecture

Each message spawns a fresh subprocess via multiprocessing.Process with pipe-based IPC. An asyncio.Semaphore limits concurrency to max_workers. Processes are not reused: each one starts, runs the agent function, sends the result over the pipe, and exits. On timeout or cancellation, the process is killed immediately. This subprocess isolation model means:

A crash in one agent turn never affects other sessions or the server itself.
No shared state leaks between requests.
Resource cleanup is automatic — when the process exits, all memory is reclaimed.

File structure

serve/
├── __init__.py       # Public exports: AgentServer, ServableAgent
├── protocol.py       # ServableAgent runtime-checkable protocol
├── server.py         # AgentServer class (FastAPI routes, background tasks)
├── worker.py         # WorkerExecutor, subprocess execution, agent type dispatch
├── schemas.py        # Pydantic models (SessionStatus, request/response types)
├── session.py        # Session dataclass and in-memory SessionStore
└── cli.py            # CLI (start, chat, health, create, sessions, get, delete, messages, send)

For detailed CLI usage and all available flags, see the CLI reference for motus serve.

Server options

Start options for motus serve start:

Flag	Default	Description
`--host`	`0.0.0.0`	Bind address
`--port`	`8000`	Port
`--workers`	CPU count	Max concurrent worker processes
`--ttl`	`0`	TTL for idle/error sessions in seconds (`0` = disabled)
`--timeout`	`0`	Max seconds per agent turn before the worker is killed (`0` = no limit)
`--max-sessions`	`0`	Maximum concurrent sessions (`0` = unlimited)
`--shutdown-timeout`	`0`	Seconds to wait for in-flight tasks on shutdown before cancelling (`0` = wait indefinitely)
`--allow-custom-ids`	`false`	Enable `PUT /sessions/{id}` for client-specified session IDs
`--log-level`	`info`	Log verbosity: `debug`, `info`, `warning`, `error`

motus serve start myapp:agent --port 8080 --workers 8 --ttl 3600 --timeout 60

CLI reference

`motus serve chat`

Send a message or enter an interactive REPL:

# Interactive REPL (creates and cleans up a session automatically)
motus serve chat http://localhost:8000

# Single message
motus serve chat http://localhost:8000 "hello"

# Keep the session after exit for later resumption
motus serve chat http://localhost:8000 --keep

# Resume an existing session
motus serve chat http://localhost:8000 --session 550e8400-e29b-41d4-a716-446655440000

Flag	Default	Description
`--session`	—	Resume an existing session instead of creating a new one
`--keep`	`false`	Preserve the session on exit (prints session ID)
`--param`	—	`KEY=VALUE` per-request parameter passed as `user_params` (repeatable)

Other commands

Command	Description
`motus serve health <url>`	Check server status and worker counts
`motus serve create <url>`	Create a new session
`motus serve sessions <url>`	List all active sessions
`motus serve get <url> <id> [--wait]`	Get session details; `--wait` blocks until not running
`motus serve delete <url> <id>`	Delete a session
`motus serve messages <url> <id>`	Get the full conversation history
`motus serve send <url> <id> <msg> [--wait]`	Send a message; `--wait` blocks for completion

Python API

Use AgentServer to embed the server in your own Python application:

from motus.serve import AgentServer

server = AgentServer(my_agent, ttl=3600, timeout=30)
server.run(host="0.0.0.0", port=8000)

Constructor parameters (all except agent_fn are keyword-only):

Parameter	Type	Default	Description
`agent_fn`	`Callable	str`	required	A `ServableAgent`, OpenAI Agent, callable, or import path string (e.g., `"myapp:my_agent"`)
`max_workers`	`int	None`	`None`	Max concurrent worker processes. Defaults to `os.cpu_count()`, fallback `4`.
`ttl`	`float`	`0`	TTL for idle/error sessions in seconds. `0` disables expiry.
`timeout`	`float`	`0`	Max seconds per turn before the worker is killed. `0` disables.
`max_sessions`	`int`	`0`	Maximum concurrent sessions. `0` means unlimited.
`shutdown_timeout`	`float`	`0`	Seconds to wait for in-flight tasks on shutdown. `0` waits indefinitely.
`allow_custom_ids`	`bool`	`False`	Enable `PUT /sessions/{id}` for client-specified IDs.

run(host, port, log_level) -> None — starts the server (blocking).

Parameter	Type	Default	Description
`host`	`str`	`"0.0.0.0"`	Bind address
`port`	`int`	`8000`	Port
`log_level`	`str`	`"info"`	Uvicorn log level

The server.app property exposes the underlying FastAPI application for testing or mounting in a larger application.

Get Started

Run and Deploy

Motus Cloud

Motus Library

Integrations

Contributing

Self-Managed

Quick start

Agent types

Session lifecycle

Architecture

Server options

CLI reference

`motus serve chat`

Other commands

Python API

Get Started

Run and Deploy

Motus Cloud

Motus Library

Integrations

Contributing

​Quick start

​Agent types

​Session lifecycle

​Architecture

​Server options

​CLI reference

​motus serve chat

​Other commands

​Python API

Quick start

Agent types

Session lifecycle

Architecture

Server options

CLI reference

`motus serve chat`

Other commands

Python API