import openai
import anthropic
import google.generativeai as genai
from typing import Optional, Dict, Any

class UniversalLLMClient:
    """
    A wrapper client that handles multiple LLM providers with a unified interface.
    This demonstrates the reality of AI integration - you need to handle fragmentation yourself.
    """
    
    def __init__(self, config: Dict[str, Any]):
        self.config = config
        self.clients = {
            'openai': openai.OpenAI(api_key=config.get('openai_key')),
            'anthropic': anthropic.Anthropic(api_key=config.get('anthropic_key')),
            'google': genai.configure(api_key=config.get('google_key'))
        }
    
    def complete(self, 
                 prompt: str, 
                 provider: str = 'openai',
                 **kwargs) -> str:
        """
        Get completion from specified LLM provider.
        Each provider requires different API calls and parameters.
        """
        
        if provider == 'openai':
            response = self.clients['openai'].chat.completions.create(
                model=kwargs.get('model', 'gpt-3.5-turbo'),
                messages=[{"role": "user", "content": prompt}],
                max_tokens=kwargs.get('max_tokens', 1000)
            )
            return response.choices[0].message.content
            
        elif provider == 'anthropic':
            response = self.clients['anthropic'].messages.create(
                model=kwargs.get('model', 'claude-3-haiku-20240307'),
                max_tokens=kwargs.get('max_tokens', 1000),
                messages=[{"role": "user", "content": prompt}]
            )
            return response.content[0].text
            
        elif provider == 'google':
            model = genai.GenerativeModel(kwargs.get('model', 'gemini-pro'))
            response = model.generate_content(prompt)
            return response.text
            
        else:
            raise ValueError(f"Unsupported provider: {provider}")

# Usage example:
# config = {
#     'openai_key': 'your-key-here',
#     'anthropic_key': 'your-key-here',
#     'google_key': 'your-key-here'
# }
# client = UniversalLLMClient(config)
# result = client.complete("Hello, how are you?", provider='openai')

Every developer building a lightweight application with AI features has had the same thought: "Wouldn't it be great if I could just ask the operating system for text completion?" The fantasy is compelling—a simple, universal API that abstracts away the complexity of different LLM providers, handles authentication, and provides consistent results across applications. But this fantasy is exactly that: a fantasy. The technical and commercial realities of today's AI landscape make such standardization not just unlikely, but fundamentally impossible.

The Fragmented Reality of AI Integration

Let's examine why the dream of OS-level LLM completion is fundamentally flawed. First, consider the technical diversity: OpenAI's GPT-4, Anthropic's Claude, Google's Gemini, Meta's Llama, and countless open-source models each have different APIs, pricing structures, capabilities, and limitations. An operating system attempting to standardize these would need to either force providers into a common interface (unlikely) or maintain translation layers for every significant model (unmaintainable).

Second, the business incentives are completely misaligned. Major AI providers are building ecosystems, not plumbing. Microsoft wants you using Copilot Studio and Azure OpenAI Service. Google wants Gemini integrated into Workspace. Apple is developing its own on-device models. None have motivation to create a neutral, standardized completion service that commoditizes their competitive advantage.

Third, privacy and data sovereignty concerns create insurmountable barriers. European GDPR regulations, healthcare HIPAA requirements, and corporate data policies mean that text completion requests can't simply be routed through a generic OS service without explicit user consent and data handling agreements for each request.

The JSONL TUI Example: Why It's Harder Than It Looks

The original question's example—a TUI for browsing JSONL files with natural language querying—perfectly illustrates the problem. "Translate this natural query to jq" seems simple until you consider:

• Which model should handle the request? A general-purpose model might misunderstand JSON-specific syntax.
• Where does the processing happen? On-device for privacy? Cloud for capability?
• Who pays for the API calls? The developer? The user? The OS vendor?
• How are prompts standardized when different models respond differently to identical prompts?

These aren't edge cases—they're fundamental questions that any standardization effort must answer, and there are no one-size-fits-all solutions.

What's Actually Emerging: Three Practical Alternatives

While universal OS-level completion remains a myth, three practical patterns are emerging that developers should understand:

1. The Browser as the New AI Platform

Web browsers are becoming de facto AI platforms through WebGPU access and WebAssembly. Projects like Transformers.js and ONNX Runtime Web allow models to run directly in the browser. The emerging pattern isn't "ask the OS for completion" but "ship the model with your application" or "use a WebAssembly runtime." This gives developers control while maintaining privacy, though at the cost of application size and performance.

2. Configuration Over Standardization

Instead of a universal API, what's emerging are configuration standards. The ~/.config/ai-providers.json pattern lets users specify their preferred models, API keys, and endpoints. Applications can then read this configuration and make appropriate API calls. This approach respects user choice and privacy while avoiding the impossible task of API standardization.

3. Local-First AI Middleware

Projects like Ollama, LM Studio, and LocalAI are creating local inference servers that applications can query via REST API. While not OS-level, they provide a consistent interface to whatever models the user has installed locally. The pattern becomes: "Check for local AI server on port 11434, fall back to cloud if unavailable."

The Technical Debt of Premature Standardization

History teaches us that premature standardization often creates more problems than it solves. Remember CORBA, SOAP, or early web service standards? They attempted to solve similar integration problems through heavy specification, only to be replaced by simpler, more flexible approaches (REST, gRPC).

The AI completion space is evolving too rapidly for meaningful standardization. Consider that just three years ago, GPT-3 was state-of-the-art, and today we have specialized models for coding, mathematics, medicine, and creative writing. Any standard created today would be obsolete within months as new model capabilities and architectures emerge.

A Practical Path Forward for Developers

So what should developers building lightweight applications do today? Follow these practical steps:

1. Design for AI abstraction from the start: Create a clean interface in your code that separates AI completion logic from application logic.
2. Support multiple backends: Implement support for OpenAI-compatible APIs, Anthropic's API, and local inference servers.
3. Make configuration user-friendly: Allow users to easily specify their preferred AI provider through environment variables, config files, or UI settings.
4. Consider on-device options: For privacy-sensitive applications, bundle smaller models (like Phi-3 or Gemma) or use WebAssembly runtimes.
5. Document requirements clearly: Tell users exactly what AI capabilities your application needs and let them choose how to provide them.

The Future Isn't Standardized—It's Specialized

The most likely future isn't one universal AI completion service, but specialized AI capabilities integrated at different levels of the stack. We'll see:

• Hardware-level AI acceleration (NPUs in Apple Silicon, Intel AI Boost)
• Framework-level AI integration (Next.js AI SDK, LangChain)
• Application-level AI features (GitHub Copilot, Cursor)
• User-controlled AI routing (personal AI assistants that manage multiple models)

This specialization actually benefits developers and users. It allows for optimization, privacy controls, and innovation that a one-size-fits-all OS service would stifle.

Conclusion: Embrace the Fragmentation

The dream of a simple os.complete_text(prompt) API is seductive but misguided. The reality is that AI completion is too complex, too rapidly evolving, and too commercially valuable to be standardized at the OS level. Instead of waiting for a standard that will never arrive, developers should embrace the current fragmentation as an opportunity.

Build applications that give users control over their AI choices. Create abstraction layers that can adapt as the ecosystem evolves. And most importantly, focus on solving real user problems rather than chasing integration fantasies. The JSONL TUI with natural language querying is a great idea—build it with a configurable AI backend, document the requirements, and ship it. The users who need it will appreciate the functionality far more than they'll lament the lack of standardization.

In the end, the "standard way" for apps to request text completion is the same as it's always been for any complex service: provide value, make integration straightforward, and let users decide how they want to power it. The AI revolution won't be standardized—and that's actually a good thing.