Show HN: First Claude Code client for Ollama local models(github.com)
github.com
Show HN: First Claude Code client for Ollama local models
https://github.com/21st-dev/1code
28 comments
The Anthropic API was already supported by llama.cpp (The project Ollama ripped off and typically lags in features by 3-6 months), which already works perfectly fine with Claude Code by setting a simple environment variable.
Point of clarification: llama.cpp is MIT-licensed. Using it downstream (commercially or otherwise) is exactly what that license allows, so calling it a rip-off is misleading.
As others said this was possible for months already with llama-cop’s support for Anthropic messages API. You just need to set the ANTHROPIC_BASE_URL. The specific llama-server settings/flags were a pain to figure out and required some hunting, so I collected them in this guide to using CC with local models:
https://github.com/pchalasani/claude-code-tools/blob/main/do...
One tricky thing that took me a whole day to figure out is that using Claude Code in this setup was causing total network failures due to telemetry pings, so I had to set this env var to 1: CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC
https://github.com/pchalasani/claude-code-tools/blob/main/do...
One tricky thing that took me a whole day to figure out is that using Claude Code in this setup was causing total network failures due to telemetry pings, so I had to set this env var to 1: CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC
There are already various proxies to translate between OpenAI-style models (local or otherwise) and an Anthropic endpoint that Claude Code can talk to. Is the advantage here just one less piece of infrastructure to worry about?
siderailing here - but got one that _actually_ works?
in particular i´d like to call claude-models - in openai-schema hosted by a reseller - with some proxy that offers anthropic format to my claude --- but it seems like nothing gets to fully line things up (double-translated tool names for example)
reseller is abacus.ai - tried BerriAI/litellm, musistudio/claude-code-router, ziozzang/claude2openai-proxy, 1rgs/claude-code-proxy, fuergaosi233/claude-code-proxy,
in particular i´d like to call claude-models - in openai-schema hosted by a reseller - with some proxy that offers anthropic format to my claude --- but it seems like nothing gets to fully line things up (double-translated tool names for example)
reseller is abacus.ai - tried BerriAI/litellm, musistudio/claude-code-router, ziozzang/claude2openai-proxy, 1rgs/claude-code-proxy, fuergaosi233/claude-code-proxy,
What probably needs to exist is something like `llsed`.
The invocation would be like this
This sounds like the proper separation of concerns here... probably
The pre/post should probably be json-rpc that get lazy loaded.
Writing that now. Let's do this: https://github.com/day50-dev/llsed
The invocation would be like this
llsed --host 0.0.0.0 --port 8080 --map_file claude_to_openai.json --server https://openrouter.ai/api
{ tag: ... from: ..., to: ..., params: ..., pre: ..., post: ...}
This sounds like the proper separation of concerns here... probably
The pre/post should probably be json-rpc that get lazy loaded.
Writing that now. Let's do this: https://github.com/day50-dev/llsed
Some unsolicited advice: Streaming support is tricky. I'd strip the streaming out when you proxy until everything else is solid.
Cool. Sounds good. Thanks. I'll do it.
This will be a bit challenging I'm sure but I agree, litellm and friends do too many things and take too long to get simple asks from
I've been pitching this suite I'm building as "GNU coreutils for the LLM era"
It's not sticking and nobody is hyped by it.
I don't know if I should keep going or if this is my same old pattern cropping up again of things I really really like but just kinda me
This will be a bit challenging I'm sure but I agree, litellm and friends do too many things and take too long to get simple asks from
I've been pitching this suite I'm building as "GNU coreutils for the LLM era"
It's not sticking and nobody is hyped by it.
I don't know if I should keep going or if this is my same old pattern cropping up again of things I really really like but just kinda me
So I've pitched this a few more times. It's way too complicated for people.
The value comprehension market is small
So I'll need to surface it better or just do something else
The value comprehension market is small
So I'll need to surface it better or just do something else
I've been hacking on this one for a few months now and it works for me https://github.com/elidickinson/claude-code-mux Been optimizing for routing to different models within one session so maybe overkill.
But I'm surprised litellm (and its wrappers) don't work for you and I wonder if there's something wrong with your provider or model. Which model were you using?
But I'm surprised litellm (and its wrappers) don't work for you and I wonder if there's something wrong with your provider or model. Which model were you using?
What hardware are you running the 30b model on? I guess it needs at least 24GB VRAM for decent inference speeds.
Im running qwen3-coder:30b-a3b-q8_0 @ 32k context. Comes out to 36gb and Im splitting it between a 3090 24gb and a 4060ti 16gb (ollama put 20gb on the 3090 and 13.5 on the 4060ti) , runs great tbh. Ollama running in ubuntu server and Im running claude code from my windows desktop pc.
The general rule to follow is that you need as much VRAM as the model size. 30b models are usually around 19GB. So, most likely a GPU with 24GB of VRAM.
But this also means tiny context windows. You can't fit gpt-oss:20b + more than a tiny file + instructions into 24GB
Gpt-oss is natively 4-bit, so you kinda can
You can fit the weights + a tiny context window into 24GB, absolutely. But you can't fit anything of any reasonable size. Or Ollama's implementation is broken, but it needs to be restricted beyond usability for it not to freeze up the entire machine when I last tried to use it.
I'd like to know this, too. I'm just getting started getting my feet wet with ollama and local models using just CPU, and it's obviously terribly slow (even 24 cores, 128GB DRAM. It's hard to gauge how much GPU money I'd need to plonk down to get acceptable performance for coding workflows.
I tried to build a similar local stack recently to save on API costs. In practice I found the hardware savings are a bit of a mirage for coding workflows. The local models hallucinate just enough that you end up spending more in lost time debugging than you would have paid for Sonnet or Opus to get it right the first time.
I was trying to get Claude code to work with llama.cpp but could never figure out anything functional. It always insisted on a phone home login for first time setup. In cline I’m getting better results with glm-4.7-flash than with qwen3-coder:30b
~/.claude.json with {"hasCompletedOnboarding":true} is the key, then ANTHROPIC_BASE_URL and ANTHROPIC_AUTH_TOKEN work as expected
Curious what llama-server flags you used.
On my M1 Max 64GB MacBook I tried it in Claude Code (which has a 25K system message) and I get 3 tps.
But with Qwen3-30B-A3B I get 20 tps in CC.
But with Qwen3-30B-A3B I get 20 tps in CC.
this is cool. not sure it is the first claude code style coding agent that runs against Ollama models though. goose, opencode and others have been able to do that a while no?
hey, thanks for sharing. I had to go to the Twitter feed to find the GitHub link:
https://github.com/21st-dev/1code
https://github.com/21st-dev/1code
Thanks for catching that. I've changed the URL at the top to that from https://twitter.com/serafimcloud/status/2014266928853110862 now.
Here is the release note from Ollama that made this possible: https://ollama.com/blog/claude
Technically, what I do is pretty straightforward:
- Detect which local models are available in Ollama.
- When internet access is unavailable, the client automatically switches to Ollama-backed local models instead of remote ones.
- From the user’s perspective, it is the same Claude Code flow, just backed by local inference.
In practice, the best-performing model so far has been qwen3-coder:30b. I also tested glm-4.7-flash, which was released very recently, but it struggles with reliably following tool-calling instructions, so it is not usable for this workflow yet.