Chatgpt Apps

Automate and integrate ChatGPT Apps into your existing workflows

Category: productivity Source: openai/skills

Chatgpt Apps is a community skill for building applications powered by the OpenAI ChatGPT API, covering conversation management, function calling, streaming responses, production deployment patterns, and cost optimization strategies for high-volume applications.

What Is This?

Overview

Chatgpt Apps provides integration patterns for building applications on top of the OpenAI Chat Completions API. It covers conversation state management, system prompt design, function calling for tool use, streaming response handling, and error recovery strategies. The skill focuses on practical patterns for production applications rather than basic API usage.

Who Should Use This

This skill serves developers building chatbot interfaces, teams integrating GPT models into existing products, and engineers designing agent architectures that leverage function calling capabilities. It is relevant for anyone moving beyond simple prompt engineering into structured application development.

Why Use It?

Problems It Solves

Managing multi-turn conversation state across stateless API calls requires careful message history tracking. Token limits force decisions about which context to keep and which to summarize. Function calling setup involves schema definition, response parsing, and error handling that must work reliably. Streaming responses need buffering and UI update logic that differs from standard request-response patterns.

Core Highlights

Conversation manager tracks message history with automatic token budget enforcement. Function calling framework defines tool schemas and routes model requests to handler functions. Streaming processor handles chunked responses with proper buffering for display. Retry logic manages rate limits and transient API errors with exponential backoff.

How to Use It?

Basic Usage

from openai import OpenAI
from dataclasses import dataclass, field

@dataclass
class Conversation:
    system_prompt: str
    messages: list[dict] = field(default_factory=list)
    max_history: int = 20

    def add_user(self, content: str):
        self.messages.append({"role": "user", "content": content})
        self._trim()

    def add_assistant(self, content: str):
        self.messages.append({"role": "assistant", "content": content})

    def _trim(self):
        if len(self.messages) > self.max_history:
            self.messages = self.messages[-self.max_history:]

    def to_api_messages(self) -> list[dict]:
        return [
            {"role": "system", "content": self.system_prompt}
        ] + self.messages

client = OpenAI()
conv = Conversation(system_prompt="You are a helpful assistant.")
conv.add_user("Explain REST APIs briefly.")
response = client.chat.completions.create(
    model="gpt-4o", messages=conv.to_api_messages()
)
reply = response.choices[0].message.content
conv.add_assistant(reply)

Real-World Examples

import json

class FunctionCallingApp:
    def __init__(self, client: OpenAI):
        self.client = client
        self.tools = [{
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current weather for a city",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {"type": "string"}
                    },
                    "required": ["city"]
                }
            }
        }]

    def handle_tool_call(self, name: str, args: dict) -> str:
        if name == "get_weather":
            return json.dumps({"temp": 22, "condition": "sunny"})
        return json.dumps({"error": "unknown function"})

    def chat(self, messages: list[dict]) -> str:
        resp = self.client.chat.completions.create(
            model="gpt-4o", messages=messages, tools=self.tools
        )
        msg = resp.choices[0].message
        if msg.tool_calls:
            call = msg.tool_calls[0]
            result = self.handle_tool_call(
                call.function.name,
                json.loads(call.function.arguments)
            )
            messages.append(msg.model_dump())
            messages.append({
                "role": "tool",
                "tool_call_id": call.id,
                "content": result
            })
            return self.chat(messages)
        return msg.content

Advanced Tips

Implement conversation summarization when message history approaches token limits rather than truncating context abruptly. Use structured output mode for function calling responses to ensure reliable JSON parsing. Cache identical prompts with matching parameters to reduce API costs for repeated queries.

When to Use It?

Use Cases

Build customer support chatbots with function calling for order lookups and account management. Create coding assistants that maintain project context across conversation turns. Develop research tools that combine search function calls with conversational synthesis.

Related Topics

OpenAI API reference documentation, prompt engineering techniques, agent framework design, conversation state management, and LLM application deployment.

Important Notes

Requirements

An OpenAI API key with appropriate model access, the openai Python package or equivalent HTTP client, error handling for API rate limits and service interruptions, and token counting utilities for managing context window budgets.

Usage Recommendations

Do: implement token counting to manage context window budgets proactively. Validate function call arguments before execution to prevent injection attacks. Log all API interactions for debugging and cost monitoring.

Don't: store API keys in source code or client-side applications. Send unlimited conversation history without summarization or truncation. Trust function call arguments from the model without validation against expected schemas.

Limitations

Token limits constrain conversation length and require history management strategies. Function calling reliability varies across model versions and complex schema definitions. API latency affects user experience for real-time chat applications, especially when function calls add sequential round trips. Model behavior changes between versions may require prompt adjustments after upgrades.