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theskyisveryblue/meta-model-cookbook

Analysis updated 2026-05-18

0PythonAudience · developerComplexity · 2/5Setup · easy

TLDR

A collection of copy-paste Python and notebook recipes for building AI agents and applications on Meta's Model API, from basic calls to full multi-agent systems.

Mindmap

mindmap
  root((Meta Model Cookbook))
    What it does
      API fundamentals recipes
      Agent building patterns
      Full use case examples
    Tech stack
      Python
      Jupyter notebooks
      OpenAI compatible SDK
    Use cases
      Learn to call the Meta Model API
      Build a coding agent loop
      Try multi agent orchestration
    Audience
      Developers
      AI researchers
      Vibe coders

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What do people build with it?

USE CASE 1

Learn how to call Meta's Model API using existing OpenAI or Anthropic SDK code.

USE CASE 2

Copy a starting recipe for streaming, tool calling, or structured output.

USE CASE 3

Build a multi-agent system or an autonomous GitHub bot using the provided patterns.

What is it built with?

PythonJupyter NotebookOpenAI SDKAnthropic SDK

How does it compare?

theskyisveryblue/meta-model-cookbook0xallam/my-recipe0xhassaan/nn-from-scratch
Stars00
LanguagePythonPythonPython
Last pushed2022-11-22
MaintenanceDormant
Setup difficultyeasymoderatemoderate
Complexity2/52/54/5
Audiencedevelopergeneraldeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

Requires a Meta Model API account and key, set as an environment variable.

License details are provided in a separate LICENSE file in the repository.

In plain English

Meta Model API Cookbook is a collection of example code showing how to build with Meta's Model API, which serves an AI model called Muse Spark. The main selling point is compatibility: since the API is built to work like the OpenAI and Anthropic APIs, existing code written for those services can often be pointed at Meta's servers with only small changes, like swapping the base web address and API key. Muse Spark itself can read very large amounts of text at once, and a preview version is currently free to try. The repository is organized as a set of copy paste starting points, split into three sections. The first section covers basic building blocks: making a first API call, streaming responses as they are generated, calling external tools, getting reliably structured JSON output, reusing prompts to save cost, controlling how much the model reasons before answering, sending images, working with very long text inputs, handling errors safely, and grounding answers in live web search results. The second section shows patterns for building AI agents, programs that can plan, take actions, and check their own work, including a basic agent loop, managing a growing conversation over many turns, and making careful in place file edits. The third section is a set of full example applications: reading data out of chart images, fixing bugs from a screenshot of an error, controlling smart glasses hands free, generating slide decks, building websites and browser games with a coding agent that checks its own results in a real browser, running model generated code safely in a sandbox, coordinating a team of several specialized AI agents on one shared task board, and controlling a computer's desktop directly from screenshots. Each recipe is provided as a self contained Python file or notebook meant to be copied and adapted, and the license terms are included separately in the repository.

Copy-paste prompts

Prompt 1
Walk me through the quickstart recipe for calling the Meta Model API with the OpenAI SDK.
Prompt 2
Explain the difference between the agent patterns and use cases sections of this cookbook.
Prompt 3
Help me adapt the multi-agent product studio recipe to my own project.
Prompt 4
Based on this cookbook's error handling recipe, suggest a retry strategy for my own API client.

Frequently asked questions

What is meta-model-cookbook?

A collection of copy-paste Python and notebook recipes for building AI agents and applications on Meta's Model API, from basic calls to full multi-agent systems.

What language is meta-model-cookbook written in?

Mainly Python. The stack also includes Python, Jupyter Notebook, OpenAI SDK.

What license does meta-model-cookbook use?

License details are provided in a separate LICENSE file in the repository.

How hard is meta-model-cookbook to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is meta-model-cookbook for?

Mainly developer.

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