Custom GPT Actions in 2026: How AI Agents Are Taking the Lead

OpenAI’s custom GPT Actions had great potential. They allowed GPTs to connect to external APIs, retrieve data, and perform tasks from within a conversation. It was an early step toward real-world automation.

However, many users found the setup too complex, the behavior unpredictable, and the support lacking. The feature is gone today, but the need for automation hasn't disappeared — it's just evolved.

In this guide, we’ll cover:

• What are GPT actions?
• How do GPT actions work? 
• Why they were deprecated in 2024
• How modern AI agents solve the same problem 
• Discover a top AI agent builder to get started today

First, let’s understand GPT actions.

What are GPT actions?

GPT actions were OpenAI's attempt to let GPTs interact with external tools. By uploading an OpenAPI schema, you could give a GPT the ability to call APIs — to fetch data, trigger workflows, or send updates during a conversation.

When configured well, they could handle real-world tasks like booking meetings, sending emails, and retrieving CRM data, providing users with useful capabilities.

It was OpenAI's first push toward making GPTs more functional, embedding OpenAI actions inside the GPT interface itself.

But how did this approach compare to what came before?

How were GPT actions different from plugins?

Plugins were public, pre-approved tools with fixed capabilities. You installed them from a store, and they worked the same for everyone.

GPT actions, by contrast, were built into custom GPTs. You could define your API endpoints and control precisely what the GPT had access to — using OpenAPI specs and your authentication methods.

That flexibility came with more power — and more complexity. Let’s see some examples.

Custom GPT examples

Most custom GPT use cases revolved around connecting GPTs to business tools — CRMs, calendars, databases, and support systems.

Some common GPT actions examples include:

• Booking meetings via Google Calendar or Outlook
• Fetching lead or company data from tools like Clearbit
• Creating tickets in Zendesk from user queries
• Sending follow-up emails through Postmark
• Querying internal pricing or inventory databases

These tasks looked simple but required detailed specs, authentication, and careful prompt design to get right. Let’s see how they achieved these capabilities.

How did custom GPT actions work?

GPT actions operated on a "trigger + action" model, meaning you defined a task — like scheduling a meeting — and the GPT would decide when to call an external API based on the flow of the conversation.

To set it up, developers had to:

• Write an OpenAPI spec (in JSON or YAML)
• Define parameters and expected responses
• Set up authentication (API keys or OAuth)

These specs were uploaded to the GPT builder, giving the GPT structured access to real-world tools. But getting them to work reliably wasn't easy. You had to test prompt behavior, handle edge cases, and debug failures where the GPT misunderstood or ignored your schema.

It made GPT actions powerful in theory — but challenging to maintain, especially for non-technical teams.

Let’s look at who used them and why they gave it a shot.

Who used GPT actions — and why it mattered

The early adopters of custom GPT actions were developers, automation pros, and no-code builders. No-code builders used visual development environments to create applications without traditional coding. They were interested in extending GPTs beyond text generation to automate workflow tasks.

You’d find them in OpenAI forums or Reddit threads, sharing demos and debugging specs. Many were already using tools like Zapier or Make and saw GPT actions as a way to streamline manual tasks with natural language.

Everyday use cases included:

• Enriching and logging lead data into CRMs
• Drafting and sending outreach emails
• Scheduling meetings directly from chat
• Surfacing internal data on demand

While the potential was obvious, so were the barriers. Expectations of an easy, plug-and-play experience quickly led to frustration with specs, keys, and unpredictable behavior.

Still, one thing was clear: People weren’t just looking to chat — they wanted automation. They eventually failed. Here’s why.

Why GPT actions didn’t stick

Despite early excitement, GPT actions never gained traction beyond a niche crowd. For most teams, the setup and maintenance costs outweighed the benefits.

Here’s what held them back:

• Too technical: Writing and maintaining OpenAPI specs required dev experience.
• No step chaining: You couldn’t link actions to a workflow — each call stood alone.
• No memory: Actions didn’t retain context between steps, which made complex tasks fragile.
• Limited integrations: No native support for tools like Slack or Salesforce — everything had to be wired manually.
• Sudden shift: In early 2024, OpenAI deprecated custom GPT Actions — a feature that let GPTs call external APIs using OpenAPI specs. While Actions were removed, OpenAI is enhancing function calling, memory, and tool use across GPT-4 Turbo, hinting at a more unified replacement coming soon.

The core idea had value. But the implementation made it hard to use — and even harder to scale.

That’s where AI agents have taken over.

AI agents are replacing GPT actions — here’s why

AI agents’ features go beyond what GPT actions could offer, like continuity, logic, and deeper integration. 

Rather than just making one-off API calls, agents manage ongoing tasks, remember context, make informed decisions, and coordinate next steps. They’re more than just chatbots and can run processes.

What sets agents apart:

• Memory: They retain state across tasks.
• Logic: They follow conditionals, handle fallbacks, and trigger new actions as needed.
• Integration: Most agent platforms connect natively with tools like Gmail, HubSpot, Slack, and Stripe.
• Autonomy: Agents don’t just call a function — they figure out what needs to be done.

The shift is already happening. OpenAI is doubling down on memory and function-calling. Tools like Hugging Face, LangChain, and Relevance AI focus on agent infrastructure. No-code platforms like Lindy are building agents into workflows, not as add-ons.

If ChatGPT actions were a prototype, agents are the production version. Next, let’s take a detailed look at Lindy and compare it with other platforms. 

How Lindy solves the problems that GPT actions couldn’t

Lindy was built to solve the struggles people faced with GPT actions including setup, scope, and orchestration, 

Instead of isolated API calls, Lindy agents handle complete workflows — with memory from a knowledge base, set logic, and deep app integration. You can build automations that don't just respond but follow through.

What Lindy enables:

• Multi-step workflows with conditions, loops, and fallback logic
• Native integrations with Gmail, Slack, Salesforce, Stripe, Google Sheets, and more
• Memory and context from the knowledge base that persists across actions and time
• No-code workflow builder with templates for outreach, lead gen, follow-ups, and support

Let’s look at an example of a Lindy workflow. A lead fills out a form → Lindy qualifies them → enriches the data → sends a reply → assigns the lead → logs to CRM — no custom code, no external API config.

These agents are modular and can collaborate, so you can assign different agents to specific roles.

While there isn’t a one-to-one replacement for GPT Actions, OpenAI continues to support function calling and tool use, including through the Assistants API — offering pathways to build similar automation through APIs or advanced custom GPT development.

‍

{{templates}}

‍

How Lindy compares to similar automation platforms

Several platforms offer alternatives to GPT actions but take different routes. Let’s take a look at a few of them:

• Zapier AI Actions let you trigger complex workflows by linking GPT outputs with multi-step automations across thousands of apps — though deep context handling still requires careful prompt design.
• Relevance AI adds a no-code UI with light orchestration but lacks depth for complex flows.
• Hugging Face Transformers Agents offer flexibility but require coding skills and infra management.

However, Lindy stands out here for combining real orchestration, built-in integrations, and no-code workflow design, making it accessible for teams that want to automate without engineering overhead. It’s built around AI agents from the ground up.

Frequently asked questions

What happened to GPT actions?

OpenAI deprecated GPT actions in early 2024. The docs are still live, but the feature is no longer available in the GPT builder.

Can you still use GPTs with APIs?

Yes — but you'll need to build your backend or use third-party tools like Zapier or LangChain.

What's the best alternative?

Platforms like Lindy offer a better experience for teams: agents, no-code logic, and built-in tool integrations.

Do I need to code to use Lindy?

No. You can build, run, and scale agent workflows without writing code.

‍

{{cta}}

‍

Move past Custom GPT actions and let Lindy be your AI-powered automation app

You can either rebuild all the connections manually using APIs and custom code — or if you want to move to affordable AI automations, go with Lindy.

You’ll find plenty of pre-built templates and there are loads of integrations to choose from.  

Here’s why Lindy is an ideal option: 

• AI Meeting Note Taker: Lindy can join meetings based on Google Calendar events, record and transcribe conversations, and generate structured meeting notes in Google Docs. After the meeting, Lindy can send Slack or email summaries with action items and can even trigger follow-up workflows across apps like HubSpot and Gmail.
• Automated CRM updates: Instead of just logging a transcript, Lindies can be set up to update CRM fields and fill in missing data in Salesforce and HubSpot — without manual input​. 
• Lead enrichment: Lindies can be configured to use a prospecting API (People Data Labs) to research prospects and to provide sales teams with richer insights before outreach. 
• Automated sales outreach: Lindy can run multi-touch email campaigns, follow up on leads, and even draft responses based on engagement signals​.
• Cost-effective: The paid version lets you automate up to 5,000 tasks per month, which is a more affordable price per automation compared to many other platforms. 

Try automating up to 400 tasks with Lindy and see how it is an excellent automation tool. Try Lindy for free.