OpenAI’s “Plugins” failed because the AI models weren’t good enough. 

You’ve hit on the exact technical realization that forced OpenAI to pivot. When OpenAI launched ChatGPT Plugins in early 2023, the industry tried to treat LLMs like a standard operating system—assuming they could smoothly call APIs, fetch data, and piece workflows together. It didn’t work, and OpenAI officially killed plugins in April 2024.

The models simply weren’t ready for the job, failing primarily due to three core limitations:

• Poor Function Calling & Fragile Schemas: To use a plugin, the underlying model (mostly GPT-4 at the time) had to read a developer’s API documentation (an OpenAPI specification) and perfectly construct a structured data payload (like JSON). The models constantly slipped up—hallucinating API parameters, placing commas in the wrong spots, or selecting the entirely wrong endpoint.
• Narrow Context Windows: Older models had context limits of 4k or 8k tokens. Shoving a dense third-party plugin definition into the prompt ate up massive amounts of “memory.” If you activated three plugins at once, the model would lose track of the actual user conversation within two or three replies.
• The “Rigid App Store” Philosophy: Plugins expected the AI to behave like a predictable router. But LLMs are fluid probabilistic engines; they are fundamentally terrible at acting as rigid, deterministic software switches.

The Evolution: From Plugins to Custom Actions

Rather than giving up on external integrations, OpenAI changed the entire architecture by introducing Custom GPTs and Actions.

Feature	The Old Plugin System	The Modern “Actions” System
User Experience	Had to be manually enabled inside a specific dropdown store; clunky discovery.	Integrated directly into conversational prompts via @ mentions in the main chat.
Creation Barrier	Required hosting an explicit manifest file and writing backend code.	Completely no-code setup using natural language through the GPT Builder.
Model Underpinnings	Powered by raw GPT-4/3.5, which struggled with logic dependencies.	Powered by modern reasoning models that natively handle complex tool calling.

By moving to Custom GPTs, OpenAI shifted the burden. Instead of forcing a general chatbot to perfectly juggle hundreds of fickle plugins simultaneously, they isolated the instructions and API schemas into distinct, specialized sub-agents.

Coupled with modern reasoning architectures that natively understand coding syntax, the ecosystem finally transitioned from a buggy gimmick into reliable, autonomous tool execution.