media pipeline infrastructure

Build AI media pipelines once. Run them anywhere via API.

Build agentic image, video, audio, editing, and stitching workflows on discounted model infrastructure, then freeze them into reusable templates to launch directly from chat, an API, or your B2B SaaS, agency workflow, and content engine.

Explore live templates See embed path

Model cost

up to 25% off Google-family

Chain

image → video → stitch

Freeze

chat into template

Deploy

API or SaaS embed

workflow command deck

input

brief + references

agent

models + tools

output

reusable media API

Example workflow

Generate 8 images → create 8 videos → stitch into one reel → call via API.

Workflow library, not a prompt dump.

Each template opens a real FlyMyAI agent. Clone it, adapt inputs, then freeze it into a callable workflow for your own product.

Compose → Freeze → Embed → Scale

Instead of every team wiring model providers, storage, retries, billing, and batch orchestration, FlyMyAI lets teams compose model workflows in chat, validate them with real media, and turn the successful run into a repeatable production workflow.

01 compose

Pick models and tools. Chain image, video, audio, research, editing and stitching.

02 run & iterate

Use the agent chat to test prompts, attach references, and validate outputs.

03 freeze

Convert the working chat into a stable template with typed inputs and outputs.

04 embed

Call the workflow from your SaaS, internal tool, or content pipeline.

embed surface

From agent chat to production API

Your team can prototype in chat or open the live agent in the workspace to adapt it, save the successful workflow, and freeze it to expose as a repeatable API endpoint - perfect for calling from your own B2B surface, SaaS features, creative dashboards, and high-volume media ops.

frozen_workflow.py compiled #231

# pip install flymyai
from flymyai import AgentClient

client = AgentClient(api_key="fly-***")
frozen_id = 231  # frozen in FlyMyAI UI

run = client.compilations.run_instruction_and_wait(
    frozen_id,
    variables={
        "prompt": "Turn this reference into 8 campaign-ready variants",
        "model": "GPT Image 2",
        "aspect_ratio": "1:1",
        "image_urls": ["https://example.com/reference.jpg"],
    },
)

print("Status:", run.status)
image_url = run.output["image_url"]
summary = run.output["summary"]