marlo-sdk 0.0.4

The official Python SDK for Marlo - the agent learning platform

marlo-sdk

The official Python SDK for Marlo - the agent learning platform.

Marlo enables AI agents to learn and improve autonomously in production. It captures agent behavior, evaluates outcomes, and turns failures into actionable learnings.

Installation

pip install marlo-sdk

Quick Start

import marlo

# Initialize
marlo.init(api_key="your-api-key")

# Register your agent
marlo.agent(
    name="support-bot",
    system_prompt="You are a customer support agent.",
    tools=[{"name": "lookup_order", "description": "Find order by ID"}]
)

# Track a task
with marlo.task(thread_id="thread-123", agent="support-bot") as task:
    task.input("Where is my order?")

    # Your agent logic here...

    task.output("Your order ships tomorrow.")

# Shutdown before exit
marlo.shutdown()

Why Marlo?

Agents fail silently in production. The same mistakes repeat because failures aren't captured in a reusable form. Marlo solves this with a learning loop:

1. Capture - Record LLM calls, tool calls, and outcomes
2. Evaluate - Score task outcomes automatically
3. Learn - Generate guidance from failures
4. Apply - Inject learnings into future tasks

API

Initialize

marlo.init(api_key)

Register Agent

marlo.agent(name, system_prompt, tools, mcp=None, model_config=None)

Parameter	Type	Description
name	str	Unique agent identifier
system_prompt	str	Agent's system prompt
tools	list[dict]	Available tools
mcp	list[dict]	MCP servers (optional)
model_config	dict	Model settings (optional)

Track Tasks

with marlo.task(thread_id, agent, thread_name=None) as task:
    task.input(text)           # User input
    task.output(text)          # Agent response
    task.llm(model=..., usage=..., messages=..., response=...)
    task.tool(name, input, output, error=None)
    task.reasoning(text)       # Chain-of-thought
    task.error(message)        # Mark as failed

Fetch Learnings

with marlo.task(thread_id="user-123", agent="support-bot") as task:
    task.input(user_message)

    learnings = task.get_learnings()

    if learnings:
        learnings_text = learnings.get("learnings_text", "")
        if learnings_text:
            system_prompt += f"\n\nLearnings:\n{learnings_text}"

Multi-Agent

with marlo.task(thread_id="thread-1", agent="orchestrator") as parent:
    parent.input("Research AI trends")

    with parent.child(agent="researcher") as child:
        child.input("Search for AI trends")
        child.output("Found 3 sources...")

    parent.output("Report complete")

Shutdown

marlo.shutdown()

Full Example

import marlo

marlo.init(api_key="your-api-key")

marlo.agent(
    name="support-bot",
    system_prompt="You are a customer support agent.",
    tools=[{"name": "lookup_order", "description": "Find order by ID"}]
)

def handle_message(user_input: str, thread_id: str) -> str:
    with marlo.task(thread_id=thread_id, agent="support-bot") as task:
        task.input(user_input)

        # Apply learnings from past interactions
        learnings = task.get_learnings()
        system_prompt = "You are a customer support agent."
        if learnings:
            learnings_text = learnings.get("learnings_text", "")
            if learnings_text:
                system_prompt += f"\n\nLearnings:\n{learnings_text}"

        # Track tool call
        task.tool("lookup_order", {"id": "123"}, {"status": "shipped"})

        # Track LLM call
        response = "Your order ships tomorrow."
        task.llm(
            model="gpt-4",
            usage={"input_tokens": 100, "output_tokens": 25},
            messages=[{"role": "user", "content": user_input}],
            response=response
        )

        task.output(response)
        return response

marlo.shutdown()

Requirements

• Python 3.11+

Links

• Documentation
• Dashboard
• TypeScript SDK

License

MIT