Agents Quickstart

Build and run your first autonomous agent in under 5 minutes.

Prerequisites

FlyMy.AI account with API key -- get one at app.flymy.ai/profile
Python 3.9+ or Node.js 18+

Install the SDK

Python
JavaScript

pip install flymyai

npm install flymyai-js-client

Scenario A: Agent for Yourself

Create an agent, run it, get results -- all from your code.

1. Create Tools and an Agent

Python
JavaScript

from flymyai import AgentClient

client = AgentClient(api_key="fly-***")

# Register tools first — each returns a Tool object with an integer .id
web_search = client.tools.create(mcp_tool="tavily")
browser = client.tools.create(mcp_tool="browser")

agent = client.agents.create(
    name="Web Researcher",
    goal="Search the web for the latest breakthroughs in quantum computing and return a concise summary with sources.",
    tools=[web_search.id, browser.id]
)

print(f"Agent created: {agent.id}")

import { AgentClient } from "flymyai-js-client";

const client = new AgentClient({ apiKey: "fly-***" });

// Register tools first — each returns a Tool object with an integer .id
const webSearch = await client.tools.create({ mcpTool: "tavily" });
const browser = await client.tools.create({ mcpTool: "browser" });

const agent = await client.agents.create({
    name: "Web Researcher",
    goal: "Search the web for the latest breakthroughs in quantum computing and return a concise summary with sources.",
    tools: [webSearch.id, browser.id]
});

console.log(`Agent created: ${agent.id}`);

2. Start a Run

Python
JavaScript

run = client.runs.create(agent_id=agent.id)

print(f"Run started: {run.id} (status: {run.status})")

const run = await client.runs.create({ agentId: agent.id });

console.log(`Run started: ${run.id} (status: ${run.status})`);

3. Stream Events and Get Results

Python
JavaScript

# Watch execution in real-time
for event in client.runs.stream_events(run.id):
    print(f"[{event.type}] {event.message}")

# [declared_functions] Registered 3 tool functions
# [tool_called] tavily_search("quantum computing breakthroughs")
# [tool_called] browser_navigate("https://example.com/quantum-2025")
# [task_cancelled] — only if the run is cancelled

# Get final output
result = client.runs.wait(run.id)
print(result.output)

for await (const event of client.runs.streamEvents(run.id)) {
    console.log(`[${event.type}] ${event.message}`);
}

const result = await client.runs.get(run.id);
console.log(result.output);

Run Statuses

A run progresses through these statuses:

Status	Meaning
`pending`	Run created, waiting to start
`running`	Agent is actively executing
`completed`	Finished successfully
`failed`	Encountered an error
`cancelled`	Cancelled by the user

Using Handlebars Templating

You can use {{ variable }} syntax in the goal to make agents reusable. Variables are rendered from the agent's context at runtime.

agent = client.agents.create(
    name="Lead Profiler",
    goal="""Create a sales lead profile for {{ name }} at {{ company }}.
    {{#if job_title}}Their job title is {{ job_title }}.{{/if}}
    {{#if website}}Review the company website: {{ website }}.{{/if}}

    Return bullet points covering:
    - Background and responsibilities
    - Recent company initiatives
    - Potential buying triggers
    - Recommended outreach angle""",
    tools=[web_search.id, browser.id]
)

Planned Feature — Coming Soon

Scenario B: Agent for Marketplace

Build an agent that others can run on app.flymy.ai. You define the inputs -- FlyMy.AI generates the form. Marketplace features -- including input_schema, output_schema, visibility, pricing, and client.agents.publish() -- are not yet available in the API. The examples below describe planned functionality.

1. Create with Input Schema and Templating

The input schema will define what data users provide. Handlebars templating ({{ variable }}) will inject those values into the agent's goal at runtime.

Python
JavaScript

# Planned API — not yet available
from flymyai import AgentClient

client = AgentClient(api_key="fly-***")

web_search = client.tools.create(mcp_tool="tavily")
browser = client.tools.create(mcp_tool="browser")

agent = client.agents.create(
    name="Sales Lead Profiler",
    goal="""Create a concise sales lead profile for {{ name }} at {{ company }}.
    {{#if job_title}}Their job title is {{ job_title }}.{{/if}}
    {{#if website}}Review the company website ({{ website }}) for recent news.{{/if}}
    {{#if linkedin_url}}Check their LinkedIn profile: {{ linkedin_url }}.{{/if}}

    Return bullet points covering:
    - Background and responsibilities
    - Recent company initiatives
    - Potential buying triggers
    - Recommended outreach angle""",

    tools=[web_search.id, browser.id],

    # Planned — JSON Schema defining form fields on app.flymy.ai
    input_schema={
        "type": "object",
        "properties": {
            "name": {
                "type": "string",
                "description": "Person's full name"
            },
            "company": {
                "type": "string",
                "description": "Company they work at"
            },
            "job_title": {
                "type": "string",
                "description": "Job title (optional)"
            },
            "website": {
                "type": "string",
                "description": "Company website URL (optional)"
            },
            "linkedin_url": {
                "type": "string",
                "description": "Public LinkedIn profile URL (optional)"
            }
        },
        "required": ["name", "company"]
    },

    # Planned — structured output for programmatic consumers
    output_schema={
        "type": "object",
        "properties": {
            "background": {"type": "string"},
            "company_initiatives": {"type": "array", "items": {"type": "string"}},
            "buying_triggers": {"type": "array", "items": {"type": "string"}},
            "outreach_angle": {"type": "string"}
        }
    },

    # Planned — marketplace settings
    visibility="public",
    pricing={"per_run": 0.25}
)

// Planned API — not yet available
import { AgentClient } from "flymyai-js-client";

const client = new AgentClient({ apiKey: "fly-***" });

const webSearch = await client.tools.create({ mcpTool: "tavily" });
const browser = await client.tools.create({ mcpTool: "browser" });

const agent = await client.agents.create({
    name: "Sales Lead Profiler",
    goal: `Create a concise sales lead profile for {{ name }} at {{ company }}.
    {{#if job_title}}Their job title is {{ job_title }}.{{/if}}
    {{#if website}}Review the company website ({{ website }}) for recent news.{{/if}}
    {{#if linkedin_url}}Check their LinkedIn profile: {{ linkedin_url }}.{{/if}}

    Return bullet points covering background, initiatives, buying triggers, outreach angle.`,

    tools: [webSearch.id, browser.id],

    // Planned — marketplace settings
    inputSchema: {
        type: "object",
        properties: {
            name: { type: "string", description: "Person's full name" },
            company: { type: "string", description: "Company they work at" },
            job_title: { type: "string", description: "Job title (optional)" },
            website: { type: "string", description: "Company website URL (optional)" },
            linkedin_url: { type: "string", description: "Public LinkedIn profile URL (optional)" }
        },
        required: ["name", "company"]
    },

    outputSchema: {
        type: "object",
        properties: {
            background: { type: "string" },
            company_initiatives: { type: "array", items: { type: "string" } },
            buying_triggers: { type: "array", items: { type: "string" } },
            outreach_angle: { type: "string" }
        }
    },

    visibility: "public",
    pricing: { perRun: 0.25 }
});

2. Test Before Publishing

# Planned API — not yet available
test_run = client.runs.create(agent_id=agent.id)

result = client.runs.wait(test_run.id)
print(result.output)

3. Publish to Marketplace

# Planned API — not yet available
client.agents.publish(agent_id=agent.id)

After publishing, your agent will be live at app.flymy.ai/agents/your-username/sales-lead-profiler:

FlyMy.AI generates an input form from your input_schema
Required fields are marked, optional fields are collapsible
Field descriptions become placeholder text/tooltips
Users click "Run Agent", pay per execution, and see results in the UI

How It Looks for Buyers

When someone visits your agent page on FlyMy.AI:

┌──────────────────────────────────────────────────┐
│  Sales Lead Profiler               by @you       │
│                                                  │
│  ┌────────────────────────────────────────────┐  │
│  │  Name *               [Dana Lee         ]  │  │
│  │  Company *            [Acme Robotics    ]  │  │
│  │  Job title            [VP Engineering   ]  │  │
│  │  Company website      [acmerobotics.com ]  │  │
│  │  LinkedIn URL         [                 ]  │  │
│  └────────────────────────────────────────────┘  │
│                                                  │
│  ┌─────────────────────┐                         │
│  │   > Run Agent $0.25 │                         │
│  └─────────────────────┘                         │
└──────────────────────────────────────────────────┘

What's Next

Agents Overview -- architecture and key capabilities
Tools & MCP -- add tools and external services

Prerequisites​

Install the SDK​

Scenario A: Agent for Yourself​

1. Create Tools and an Agent​

2. Start a Run​

3. Stream Events and Get Results​

Run Statuses​

Using Handlebars Templating​

Scenario B: Agent for Marketplace​

1. Create with Input Schema and Templating​

2. Test Before Publishing​

3. Publish to Marketplace​

How It Looks for Buyers​

What's Next​