An OpenAI API compatible server that uses GitHub Copilot SDK for LLMs

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I've been playing around with the new official GitHub Copilot SDK and realized it's a goldmine for building programmatic bridges to their models. I built this server in Go to act as a OpenAI-compatible proxy. It essentially lets you treat your GitHub Copilot subscription as a standard OpenAI backend for any tool that supports it. I have tested it against OpenWebUI and Langchain. Key Highlights: - Official SDK: Built using the new Github Copilot SDK. It’s much more robust than the reverse-engineered solutions floating around and does not use unpublished APIs. - Tool Calling Support: It maps OpenAI function definitions to Copilot's agentic tools. You can use your own tools/functions through the Copilot without copilot needing access to the said tools just the definitions is enough. The goal was to create a reliable "bridge" so I can use my subscription models in my preferred interfaces.

Visit product View on Hacker NewsJan 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: supports, compatible · Missing: reddit linkedin, podcasting, created

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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