marona 0.7.0

Provider-neutral AI agent runtime and MCP/Skill gateway for Marona Hub connections, managed agents, and bring-your-own-agent integrations.

Marona Python SDK

Provider-neutral AI agent runtime and MCP/Skill gateway for Marona Hub connections, managed agents, and bring-your-own-agent integrations.

pip install marona

1. Marona Hub

Connect Apps and governed Skills as one neutral MCP tool collection.

from marona import Marona

marona = Marona(api_key="YOUR_MARONA_API_KEY")

# Connect every App and Skill available to this developer key.
connection = marona.hub.connect()

Select a smaller capability set when needed:

connection = marona.hub.connect(
    apps=["group-fund"],
    skills=["create-group-fund"],
)

Use the connection directly:

tools = connection.list_tools()
result = connection.call_tool(
    "skill__create_group_fund",
    {"request": "Create a family savings group fund"},
)

MCPConnection is not tied to OpenAI, LangGraph, CrewAI, or another model vendor. It exposes:

connection.list_tools()
connection.call_tool(name, arguments)
connection.server_url
connection.server_urls
connection.warnings
connection.session

An unresolved App or Skill name does not discard valid tools. Check connection.warnings for its code, selector_type, slug, and corrective message. Authentication, permission, and configured-server failures remain blocking errors.

Marona Hub owns discovery, identity, permissions, App and Skill resolution, approvals, governed execution, and online, offline, or hybrid availability. With no selectors, online and hybrid connections include every capability available to the developer key; offline connections include every capability installed on the device.

Use the asynchronous methods inside an event loop:

connection = await marona.hub.connect_async(
    apps=["group-fund"],
    skills=["create-group-fund"],
)
tools = await connection.list_tools_async()
result = await connection.call_tool_async(
    "skill__create_group_fund",
    {"request": "Create a family savings group fund"},
)

2. Marona Agent

Use Marona's Agent and Runner when you want one simple managed agent API.

from marona import Agent, Marona, Runner

marona = Marona(api_key="YOUR_MARONA_API_KEY")

tools = marona.hub.connect(
    apps=["sda-books"],
)

agent = Agent(
    name="Customer Assistant",
    model="gpt-5.6",
    instructions="Help the customer.",
    tools=tools,
)

result = await Runner.run(
    agent,
    "Download Steps to Christ",
    user_id="customer_482",
    session_id="chat_91a7",
)

print(result.final_output)

user_id is optional. session_id is also optional and defaults to default. Marona derives the developer scope from the authenticated API key and keeps conversation history isolated by developer, user, and session. In synchronous applications, use Runner.run_sync(...).

3. Marona Runtime

Use responses.create(...) when Marona should manage model reasoning, tool selection, permission and approval checks, execution, and the final response.

from marona import Marona

marona = Marona(api_key="YOUR_MARONA_API_KEY")

tools = marona.hub.connect(
    apps=["group-fund"],
    skills=["create-group-fund"],
)

response = marona.responses.create(
    model="gpt-5.6",
    tools=tools,
    input="Create a family savings group fund",
)

print(response.output_text)

The same request supports managed, direct-provider, private, and local models:

model="gpt-5.6"                    # Marona-managed model
model="openai/gpt-5.6"
model="anthropic/claude-sonnet"
model="google/gemini"
model="ollama/qwen3"
model="litellm/local-qwen"
model="local/qwen"

Register only custom providers or downloaded in-process models:

marona.models.register(
    name="office/company-assistant",
    endpoint="https://models.office.example/v1",
    model="company-assistant-v2",
    api_key="YOUR_PROVIDER_KEY",
)

marona.models.register(
    name="local/qwen",
    executor=qwen_executor,
    context_window=8192,
    max_output_tokens=512,
)

For asynchronous applications, call await marona.responses.create_async(...).

Images And Documents

response = marona.responses.create(
    model="openai/gpt-5.6",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text", "text": "Summarize this document and image."},
                {"type": "input_image", "image_url": "https://example.com/image.jpg"},
                {
                    "type": "input_file",
                    "filename": "report.pdf",
                    "file_data": "data:application/pdf;base64,...",
                    "detail": "high",
                },
            ],
        }
    ],
)

4. Bring Your Own Agent

The external framework owns its Agent, reasoning, and orchestration. Marona supplies neutral MCP tools and retains authorization, approvals, and execution.

OpenAI Agents SDK Example

from marona import Marona
from agents import Agent, Runner

marona = Marona(api_key="YOUR_MARONA_API_KEY")
connection = marona.hub.connect(
    apps=["group-fund"],
    skills=["create-group-fund"],
)

# Adapt only at the framework boundary. Marona itself remains vendor-neutral.
framework_tools = your_openai_agents_mcp_adapter(connection)

agent = Agent(
    name="Group Fund Assistant",
    model="gpt-5.6",
    instructions="Help users create and manage group funds.",
    tools=framework_tools,
)

result = Runner.run_sync(agent, "Create a family savings group fund")
print(result.final_output)

your_openai_agents_mcp_adapter(...) represents the OpenAI-specific adapter at the framework boundary; it is not part of Marona's vendor-neutral core API.

An MCP-compatible framework can map its standard tool-list and tool-call hooks directly to connection.list_tools() and connection.call_tool(...). Marona does not claim that one Python tool object automatically satisfies every agent framework's proprietary interface.

Execution Modes

Set mode when creating Marona:

marona = Marona(
    api_key="YOUR_MARONA_API_KEY",
    mode="hybrid",
)

• online: network models and online MCP targets are allowed.
• hybrid: local/private execution may fall back to online execution.
• offline: only installed local Apps, Skills, data, and local models run.

Changing model never changes App, Skill, permission, approval, or MCP rules.