Alternatives
Products that do what VoidLLM does
The LLM proxy that never sees your prompts
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Hey HN, I am proud to show you guys that I have built an open source alternative to Azure OpenAI services. Azure OpenAI services was born out of companies needing enhanced security and access control for using different GPT models. I want to build an OSS version of Azure OpenAI services that people could self host in their own infrastructure. "How can I track LLM spend per API key?" "Can I create a development OpenAI API key with limited access for Bob?" "Can I see my LLM spend breakdown by models and endpoints?" "Can I create 100 OpenAI API keys that my students could use in a classroom…
2023 · github.com
- 3AM
I built this out of curiosity about what Claude Code was actually sending to the API. Turns out, watching your tokens tick up in real-time is oddly satisfying. Sherlock sits between your LLM tools and the API, showing you every request with a live dashboard, and auto-saved copies of every prompt as markdown and json.
Jan 2026 · github.com
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- 5IB
I spent the last few days building out a nicer ChatGPT-like interface to use Mistral 7B and Llama 3 fully within a browser (no deps and installs). I’ve used the WebLLM project by MLC AI for a while to interact with LLMs in the browser when handling sensitive data but I found their UI quite lacking for serious use so I built a much better interface around WebLLM. I’ve been using it as a therapist and coach. And it’s wonderful knowing that my personal information never leaves my local computer. Should work on Desktop with Chrome or Edge. Other browsers are adding WebGPU support as well - see…
2024 · github.com
- 6CO
I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com
- 7LP
Hello hacker news, I’m the maintainer of liteLLM() - package to simplify input/output to OpenAI, Azure, Cohere, Anthropic, Hugging face API Endpoints: https://github.com/BerriAI/litellm/ We’re open sourcing our implementation of liteLLM proxy: https://github.com/BerriAI/litellm/blob/main/cookbook/proxy-... TLDR: It has one API endpoint /chat/completions and standardizes input/output for 50+ LLM models + handles logging, error tracking, caching, streaming What can liteLLM proxy do? - It’s a central place to…
2023 · github.com
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We built any-llm because we needed a lightweight router for LLM providers with minimal overhead. Switching between models is just a string change : update "openai/gpt-4" to "anthropic/claude-3" and you're done. It uses official provider SDKs when available, which helps since providers handle their own compatibility updates. No proxy or gateway service needed either, so getting started is pretty straightforward - just pip install and import. Currently supports 20+ providers including OpenAI, Anthropic, Google, Mistral, and AWS Bedrock. Would love to hear what you think!
2025 · github.com
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Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
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Hey HN! This is Adil, Salman and Jose and and we’re behind archgw [1]. An intelligent proxy server designed as an edge and AI gateway for agents - one that natively know how to handle prompts, not just network traffic. We’ve made several sweeping changes so sharing the project again. A bit of background on why we’ve built this project. Building AI agent demos is easy, but to create something production-ready there is a lot of repeat low-level plumbing work that everyone is doing. You’re applying guardrails to make sure unsafe or off-topic requests don’t get through. You’re clarifying vague…
2025 · github.com
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2023 · tinyllms.vercel.app
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- 18BA
Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs. - It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url - The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables You can see it in action in this demo where it memorizes tic-tac-toe games: https://www.youtube.com/watch?v=PWbyeZwPjuY Why we built this: before Butter, we were Pig.dev (YC W25), where we…
Oct 2025 · butter.dev
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Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…
Oct 2025 · docs.butter.dev
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LLMStack is a low-code platform that can be used to build LLM apps, chatbots and integrate AI experiences into existing products/workflows. It comes with everything out of the box that one needs to build LLM apps locally. It can also be used in a multi-tenant setting, making it available for everyone to use in an enterprise. Some highlights of the platform: - Chain multiple LLM models allowing for complex pipelines - Includes a vector database and necessary connectors to help enrich LLM responses with private data - App templates tailored to specific use cases to quickly build LLM apps…
2023 · github.com
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