
OpenProvider
Free-first AI gateway for every model
OpenProvider launched on May 17, 2026.
What it does
OpenProvider is a free-first, OpenAI-compatible gateway for discovering and using AI models across providers. It combines live model discovery, provider key management, health-aware auto routing, fallback, chat, image, vision, and speech APIs in one clean developer dashboard.
Does a similar job
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We built open OpenRouter that turns usage into a better modelAug 2026 · github.com · ▲222Hi HN, we built an open source model gateway. It's a single place to manage our own self hosted, frontier, and open source models in one place. It’s is rust native, built for concurrency, and implements all the config quirks across models and providers (streaming formats, tool calls, model parameters, rate limits, and different error behavior). The gateway adds under 1 ms for BYOK requests and under 2 ms when Experiential supplies the provider key. It has every major inference provider, and 1000+ models refreshed daily via a codex agent that opens a PR. Compared to other similar projects…
FastRouter.ai24d ago · fastrouter.ai · ▲364Route requests to the right LLM for cost, latency & quality


GoModel – an open-source AI gateway in GoApr 2026 · github.com · ▲217Hi, I’m Jakub, a solo founder based in Warsaw. I’ve been building GoModel since December with a couple of contributors. It's an open-source AI gateway that sits between your app and model providers like OpenAI, Anthropic or others. I built it for my startup to solve a few problems: - track AI usage and cost per client or team - switch models without changing app code - debug request flows more easily - reduce AI spendings with exact and semantic caching How is it different? - ~17MB docker image - LiteLLM's image is more than 44x bigger ("docker.litellm.ai/berriai/litellm:latest" ~…
Experiential LabsSep 2026 · experientiallabs.ai · ▲136Open source AI gateway turning traffic into a better model
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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