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Show HN: Open Codex – OpenAI Codex CLI with open-source LLMs
Hey HN,
I’ve built Open Codex, a fully local, open-source alternative to OpenAI’s Codex CLI.
My initial plan was to fork their project and extend it. I even started doing that. But it turned out their code has several leaky abstractions, which made it hard to override core behavior cleanly. Shortly after, OpenAI introduced breaking changes. Maintaining my customizations on top became increasingly difficult.
So I rewrote the whole thing from scratch using Python. My version is designed to support local LLMs.
Right now, it only works with phi-4-mini (GGUF) via lmstudio-community/Phi-4-mini-instruct-GGUF, but I plan to support more models. Everything is structured to be extendable.
At the moment I only support single-shot mode, but I intend to add interactive (chat mode), function calling, and more.
You can install it using Homebrew:
brew tap codingmoh/open-codex
brew install open-codex
It's also published on PyPI:
pip install open-codex
Source: https://github.com/codingmoh/open-codex https://github.com/codingmoh/open-codex
- user_4028b09 1y ago[dead]
- strangescript 1y agocurious why you went with Phi as the default models, that seems a bit unusual compared to current trends
- codingmoh 1y agoI went with Phi as the default model because, after some testing, I was honestly surprised by how high the quality was relative to its size and speed. The responses felt better in some reasoning tasks-but were running on way less hardware. What really convinced me, though, was the focus on the kinds of tasks I actually care about: multi-step reasoning, math, structured data extraction, and code understanding.There’s a great Microsoft paper on this: "Textbooks Are All You Need" and solid follow-ups with Phi‑2 and Phi‑3.
- jasonjmcghee 1y agoagreed - thought the qwen2.5-coder was kind of standard non-reasoning small line of coding models right now
- codingmoh 1y agoI saw pretty good reasoning quality with phi-4-mini. But alright - I’ll still run some tests with qwen2.5-coder and plan to add support for it next. Would be great to compare them side by side in practical shell tasks. Thanks so much for the pointer!
- siva7 1y agoAt least it can't be worse than the original codex using o4-mini.
- codingmoh 1y agofair jab - haha; if we’re gonna go small, might as well go fully local and open. At least with phi-4-mini you don’t need an API key, and you can tweak/replace the model easily
- KTibow 1y agoWithout any changes, you can already use Codex with a remote or local API by setting base URL and key environment variables.
- asadm 1y agoi think this was made before that PR was merged into codex.
- KTibow 1y agoGood correction - while the SDK used has supported changing the API through environment variables for a long time, Codex only recently added Chat Completions support recently.
- xiphias2 1y agoMaybe it was part of the reason that they accepted the PR. The fork would happen anyways if they don't allow any LLM. A bit like how Android came after iPhone with open source implementation.
- kingo55 1y agoDoes it work for local though? It's my understanding this is still missing.
- KTibow 1y agoIf your favorite LLM inference program can run a Chat Completions API.
- codingmoh 1y agoThanks for bringing that up - it's exactly why I approached it this way from the start. Technically you can use the original Codex CLI with a local LLM - if your inference provider implements the OpenAI Chat Completions API, with function calling, etc. included. But based on what I had in mind - the idea that small models can be really useful if optimized for very specific use cases - I figured the current architecture of Codex CLI wasn't the best fit for that. So instead of forking it, I started from scratch. Here's the rough thinking behind it: 1. You still have to manually set up and run your own inference server (e.g., with ollama, lmstudio, vllm, etc.). 2. You need to ensure that the model you choose works well with Codex's pre-defined prompt setup and configuration. 3. Prompting patterns for small open-source models (like phi-4-mini) often need to be very different - they don't generalize as well. 4. The function calling format (or structured output) might not even be supported by your local inference provider. Codex CLI's implementation and prompts seem tailored for a specific class of hosted, large-scale models (e.g. GPT, Gemini, Grok). But if you want to get good results with small, local models, everything - prompting, reasoning chains, output structure - often needs to be different. So I built this with a few assumptions in mind: - Write the tool specifically to run _locally_ out of the box, no inference API server required. - Use model directly (currently for phi-4-mini via llama-cpp-python). - Optimize the prompt and execution logic _per model_ to get the best performance. Instead of forcing small models into a system meant for large, general-purpose APIs, I wanted to explore a local-first, model-specific alternative that's easy to install and extend — and free to run.
- xyproto 1y agoThis is very convenient and nice! But I could not get it to work with the best small models available for Ollama for programming, like https://ollama.com/MFDoom/deepseek-coder-v2-tool-calling https://ollama.com/MFDoom/deepseek-coder-v2-tool-calling for example.
- smcleod 1y agoThat's a really old model now. Even the old Qwen 2.5 coder 32b model is better than DSv2
- shmoogy 1y agoCodex merged in to allow multiple providers today - https://github.com/openai/codex/pull/247 https://github.com/openai/codex/pull/247
- ai-christianson 1y ago> So I rewrote the whole thing from scratch using Python So this isn't really codex then?
- deleted 1y ago[deleted]
- user_4028b09 1y agoGreat work making Codex easily accessible with open-source LLMs – really excited to try it!
- vincent0405 1y agoCool project! It's awesome to see someone taking on the challenge of a fully local Codex alternative.
- underlines 1y agoDon't forget https://ollama.com/library/deepcoder https://ollama.com/library/deepcoder which ranks really well for its size
- submeta 1y agoSounds great! Although I would prefer Claude Code to be open sourced as it’s a tool that works best for Vibe coding. Albeit expensive using Anthropic‘s models via API. There is an inofficial clone though („Anon Kode“), but it’s not legitimate.
- Philpax 1y agoI believe anon-kode is a decompiled Claude Code, so it should work identically when paired with Claude.
- submeta 1y agoUnfortunately it does not. Where I can feed Claude Code with a file larger than 256k, Anon Code (like Roo) will complain that the file is too large, using Gemini 2.5 Pro.
- deleted 1y ago[deleted]
- fcap 1y agoWhy forking and use open codex when the original OpenAI opened it for multiple models? Just trying to understand.
- codingmoh 1y agoHey, that is a very good question, I have answered that before. I hope you don't mind, if I simply copy paste my previous answer: Technically you can use the original Codex CLI with a local LLM - if your inference provider implements the OpenAI Chat Completions API, with function calling, etc. included. But based on what I had in mind - the idea that small models can be really useful if optimized for very specific use cases - I figured the current architecture of Codex CLI wasn't the best fit for that. So instead of forking it, I started from scratch. Here's the rough thinking behind it: 1. You still have to manually set up and run your own inference server (e.g., with ollama, lmstudio, vllm, etc.). 2. You need to ensure that the model you choose works well with Codex's pre-defined prompt setup and configuration. 3. Prompting patterns for small open-source models (like phi-4-mini) often need to be very different - they don't generalize as well. 4. The function calling format (or structured output) might not even be supported by your local inference provider. Codex CLI's implementation and prompts seem tailored for a specific class of hosted, large-scale models (e.g. GPT, Gemini, Grok). But if you want to get good results with small, local models, everything - prompting, reasoning chains, output structure - often needs to be different. So I built this with a few assumptions in mind: - Write the tool specifically to run _locally_ out of the box, no inference API server required. - Use model directly (currently for phi-4-mini via llama-cpp-python). - Optimize the prompt and execution logic _per model_ to get the best performance. Instead of forcing small models into a system meant for large, general-purpose APIs, I wanted to explore a local-first, model-specific alternative that's easy to install and extend — and free to run.
- danielktdoranie 1y agobut what about the Codex Giggas my niggas?
- jpmonette 1y agoYou can also do the same with OpenAI Codex (with Ollama for example): 1. ~ codex --provider ollama 2. Run: /model 3. Pick your model 4. Profit!