Did you know that you can make multiple GPT’s work together?

Whether you’re wrangling content, automating customer support, or building your next AI sidekick, GPT chaining (aka linking) is how you take the output of one GPT to feed another.

Let’s break it down like you’re building your own AI-powered workflow from scratch, because, well, you are.

What Is GPT Chaining or Linking?

GPT chaining is exactly what it sounds like: connecting multiple GPTs in a sequence where the output of one feeds directly into the next. Think of it like a relay race, but with AI models handing off text instead of batons.

Each GPT in the chain can specialize in a different task such as research, summarizing, writing, editing, or formatting. By chaining them together you turn one step into a smooth, multi-step process.

Why would you need to do this?

• To break down complex tasks into bite-sized, manageable actions.
• Improve accuracy by letting each GPT do what it does best.
• Save time by automating the entire process, start to finish.
• Mix and match strengths of different GPTs and use them as if they are a team of experts.

How to Chain GPTs (GPT Plus subscription is required)

Option 1- Step-by-Step:

1. Log into ChatGPT.
2. Type @YourCustomGPT and send your first instruction.
3. Wait for the response.
4. Use @AnotherCustomGPT to process that output through that GPTs functionality.

Example:

Let’s say you have multiple GPTs. One helps you write content, the other focuses on creating social media posts and strategies.
You can start by writing some random thoughts into GPT #1, have it organize and fine tune those thoughts. Then without leaving that GPT, have it ask GPT #2 to repurpose that content as 5 different social media posts by simple give it a command like, “Ask @gpt#2 to create 5 LinkedIn post ideas from this idea.”.
That’s it!

Can you use GPTs that aren’t yours?

Yes. You can summon the power of any public GPT that you have access to by invoking the “@” syntax, even if you didn’t build it
The pros are: using already existing GPTs without needing to build your own.
The cons are: you didn’t build it so you have no control over quality, how it was trained or whether or not it disappears one day.

IMO this works best to chain together GPTs that you’ve created. basically building your own network of AI tools.

What is the max number of GPTs that you can use in a chain?

There’s no hard limit, but ChatGPT itself says three to five steps is ideal before it gets messy or context starts to degrade.

Option 2- Using APIs for automation

Use the OpenAI API (or any LLM API) to create a backend workflow that chains GPTs together automatically

Basic Flow:

1. GPT A completes Task 1.
2. Output is passed to GPT B via API.
3. GPT B completes Task 2.
4. Continue chaining as needed.

This is highly depending on how the API can be used, and will probably require a little fine tunning and testing to make it work. I’ll be working on this and will share the results.

3rd party tools that can help automate processes between your GPT and other applications:

• Zapier: No-code tool for chaining web apps and GPT calls.
• Make (formerly Integromat): For slightly more complex workflows.
• Custom scripts (Python, Node.js, etc.): For full control and flexibility.

Option 3- Using Voice in the ChatGPT App

On the go? You can speak your prompts and keep the chain going just by talking to ChatGPT.

How:

• Tap the microphone icon in the mobile app.
• Say: “Summarize this paragraph… now take that and write an email intro for it.”
• Keep the conversation going like you would with a person—it remembers context across steps.

Note: You can’t invoke “@” GPTs via voice (yet), so this is best for chaining tasks within a single GPT.

Limitations

• No direct API access to Custom GPTs: If you’re using ChatGPT’s built-in GPT builder, those aren’t yet callable via API. You’ll need to simulate their logic in code or re-create it in your backend.
• Context bleed: Each GPT only remembers so much. Long chains can lose track of earlier steps unless you pass data cleanly between them.
• Error handling: If GPT 1 gives a weird answer, GPT 2 is going to roll with it. Build in checks and fallbacks.
• Cost: Every API call to a GPT counts toward your usage. More links = more tokens = more money.

Real-World Examples of GPT Chaining

• Content Creation:
  @TrendSniffer → @OutlineArchitect → @ContentWriter → @SEOFormatter → @EditorBot
• Customer Support:
  GPT A analyzes the customer message.
  GPT B searches your internal docs.
  GPT C generates a clear, friendly response.
  GPT D logs it into your CRM.
• Research Assistant:
  GPT A fetches current stats.
  GPT B interprets and visualizes the data.
  GPT C writes a summary for your deck.

Final word on this, for now

GPT chaining isn’t just a theory, it’s how you make these tools actually work together for you. Whether you’re building workflows inside ChatGPT, through APIs, or by just talking to the app, you’re essentially designing your own AI-powered assistant team. Once you get the hang of it you’ll wonder you ever lived without it.