I’ve been noticing something that bugs me about the open source AI community. Maybe I’m wrong here but I wanted to get other people’s thoughts on this.
It feels like many open source AI projects are built specifically for ChatGPT or Claude but then claim they work with local models too. When you actually try to use them with your own hosted model, they either break or work really poorly.
For example, I’ll find a really nice agent library that looks perfect for my needs. Then I read the docs and see “optimized for GPT-4, other models may have reduced performance.” Or I’ll try to use some OpenWebUI plugin that’s supposed to work locally but it secretly makes API calls to OpenAI anyway.
I get that GPT-4 and Claude are powerful and easier to develop with initially. But if you’re going to say your open source tool supports local models, shouldn’t you actually test it with local models? It seems like these projects are open source in name only when they’re really just wrappers around proprietary APIs.
Am I being too harsh here? I usually just write my own scripts to avoid this problem, but it makes me worried about the sustainability of truly open AI tools.
The issue runs deeper than just development convenience. Having worked with both approaches extensively, I’ve noticed that many open source projects fundamentally misunderstand what local model compatibility actually requires. They assume local models are just drop-in replacements for GPT-4, but the reality is completely different. Local models have varying context windows, different instruction following capabilities, and inconsistent output formatting. What really frustrates me is when projects claim local support but their prompting strategies are clearly designed for ChatGPT’s specific training. They use complex multi-step reasoning prompts that work great with GPT-4 but completely confuse smaller local models. The successful open source tools I’ve used take a completely different approach - they design simpler, more direct prompting strategies that work across different model capabilities. It requires accepting that you might not get the same sophisticated responses from local models, but at least you get consistent, usable results instead of unpredictable failures.
You’re not being too harsh at all. I’ve run into this exact issue when trying to build production systems around supposedly “open” tools. The fundamental problem is that many developers treat local model support as an afterthought rather than a core design principle. They build the entire system architecture around the predictable behavior and specific response formats of proprietary models, then try to bolt on local model compatibility later. This approach is backwards and inevitably leads to the problems you’re describing. I’ve found that truly robust open source AI tools need to be designed from the ground up with model-agnostic architectures. This means implementing proper abstraction layers, standardized prompt templating systems, and extensive error handling for the wide variety of behaviors you get from different local models. The projects that do this well are rare but they exist. Unfortunately, the current incentive structure in open source AI development favors quick demos that work with ChatGPT over the more complex engineering required for genuine model independence.
honestly this comes down to money and convenience. developers want to ship fast and get users, so they build for whats easiest - which is proprietary apis with consistent responses. local models are a pain to test with different hardware configs and you cant guarantee how theyll behave. most projects just slap “local model support” in their readme without proper testing cause it sounds good for marketing.
This is actually a resource allocation problem more than anything else. Most open source AI projects are built by small teams or individual developers who have limited time and computational resources. Testing against GPT-4 or Claude is straightforward because you just make API calls and get consistent results. Testing against local models means downloading gigabytes of weights, dealing with different quantization formats, managing VRAM limitations, and handling the inconsistencies between different model architectures. I’ve seen this firsthand when contributing to a few projects. The maintainer had one GPU setup and tested with Llama 2, but users were running everything from CodeLlama to Mistral variants with different prompt formats. Each model behaves differently with the same prompts, so what works well with one local model might produce garbage with another. The reality is that supporting local models properly requires significantly more development and testing effort, which many open source projects simply cannot sustain without dedicated funding or contributors.