Answers to the Questions part: I am not sure I have a good answer for number 1 and 3 but 2 it's extremely important to me that local model is truly local that is likely the point of why I am using a local model to start with for that task. I haven't been using warp for a long time because it wasn't open source so I do not have many thoughts on what parts of the UI I want at this time for local models, the little I have done with like Claude cli and the information that the UI provides for that is really nice and to have something like that for local models would be cool but it might depend on what harness people are using idk.

No server transit for any requests

Warp looks really cool, but the fact it only worked with cloud models always was a deal breaker for me. I would love to have full local AI support for not only for coding, but for the terminal agent when reacting to commands.

Thanks for opening up some discussion!

I think for your options, #1 is most appealing, but yes, more work. I'd like to think it could also help your architecture long term by relying less on scaling server side components along with client components. #2 is a bit hacky but could be quick. I connect to my Hermes agent using OpenWebUI because it is nicer than the raw terminal client - I'd connect to it through Warp if it were an option, but that doesn't really support Warp being a standalone tool. #3 could be most practical because it would be fully local, not require a second tool, and for most local models, not being feature-complete would be OK. The smaller context windows mean fewer turns, fewer tools available, etc. But my use case in Warp would mostly be "uh help me remember how this command is used" not "build out an entire ansible deployment for a lab". #4 is probably not worth doing - if someone wants to use a local model, its because they want it local.

I'd rank them - 1, 3, 2, 4

For the questions:

1. I'm split because I use another agent tool already. However for new users, only having it work if you already have another harness running feels duplicative.
2. Quite important - I either have no/patchy internet at the time I'm working, or I'm doing something that I need to keep local for confidentiality reasons, and in either case, server routing breaks the whole point.
3. I don't use the most advanced tools in Warp now, so for me, the "advanced command completion" and inline planning directly in the terminal UI is what I use, and would love to use with a local model that would be fully competent at that.

My vote is strongly for option 1.

If Warp supports local or arbitrary models, I think it should mean truly local execution with no server transit. I understand that porting the harness to the client and open sourcing it is the most work, but it seems like the right long term architecture for privacy, offline use, trust, and extensibility.

Thanks!

hey got a warp fork of my own ,trying to get the ollama support work repo:https://github.com/crazygamerZ783/warp-ollama
i would appreciate a bit of help

Honestly, I want to use DeepSeek V4 Flash inside Warp — it’s cheap, and it allows me to interact with the terminal using natural language.

Local models in Ollama or similar should be configurable as sources within Warp. Once available, you should be able to select a model during a session, either manually or by directing Warp to use it automatically based on the task or preference.

6666

There are already a handful of "local warp server" implementations on your PR list , and on the wild forking from this repo.

People just want to be able to use a software that they really like (warp) without going through something that they don't need (your servers). we might end up with some opensource spin-off leading this if you don't just release a minimalist opensource server that simply allows people to use warp with a openai-compatible upstream.

In the future you can add something feature-rich and supporting a bunch of stuff... but for now people just want to use warp and remote models without touching someone else servers.

Model selection should be allowed to be

1. local (lm studio, ollama, etc) 2. also allowed to be configurable and tiered, small model for cmd suggestions (with bash history injection) as well as large for agent interactions

All this feedback makes sense. We will have a proposed solution here shortly.

I am mostly interested as I want to use one source of models (openrouter/GLM Coding Plan) for it.

Warp looks really cool, but the fact it only worked with cloud models always was a deal breaker for me. I would love to have full local AI support for not only for coding, but for the terminal agent when reacting to commands.

There are already a handful of "local warp server" implementations on your PR list , and on the wild forking from this repo.

People just want to be able to use a software that they really like (warp) without going through something that they don't need (your servers). we might end up with some opensource spin-off leading this if you don't just release a minimalist opensource server that simply allows people to use warp with a openai-compatible upstream.

In the future you can add something feature-rich and supporting a bunch of stuff... but for now people just want to use warp and remote models without touching someone else servers.

Answers to the Questions part: I am not sure I have a good answer for number 1 and 3 but 2 it's extremely important to me that local model is truly local that is likely the point of why I am using a local model to start with for that task. I haven't been using warp for a long time because it wasn't open source so I do not have many thoughts on what parts of the UI I want at this time for local models, the little I have done with like Claude cli and the information that the UI provides for that is really nice and to have something like that for local models would be cool but it might depend on what harness people are using idk.

I agree with the consensus that #1 is of course the best option, and I would like to point out that I am most interested in warp in its capabilities as a harness.

Like most programmers, I still do use cloud models for the majority of my tasks, and the ability to use my OpenAI Codex sub while still taking advantage of the harness (like Opencode for example) would push me to use warp more.

If this can be achieved in a lightweight manner, option #3 is the clear choice. If not then #1

Updates on our plan here...

With respect to BYOK and arbitrary endpoint support:

1. In the next 1-2 weeks we plan on making BYOK available on our free plan and extending it to support arbitrary OAI compatible endpoints. For now this will still go through our server harness, as it’s a much bigger lift to fully move that harness to the client in a short timeframe.
2. BYOK and arbitrary endpoint support will be available for individuals and companies of up to 10 employees. It will be login-gated, but free to use within that scope. For larger businesses and enterprise customers, they need to go through Business / Enterprise because they are using Warp’s managed harness as part of a broader platform offering, and that usage is metered through platform credits.

With respect to local model support, our plan is to

1. Work on a client side harness that connects directly to local models. We are currently slotting this into our priority list, but I expect work to start within the next few weeks.
2. Implement Warp as an ACP client and allow folks to use other harnesses within our rich terminal UI. This addresses the need to BYO inference without connecting to Warp’s servers. See Support for Agent Client Protocol (ACP) #7326
3. These will not require login and will be completely client side.

Thanks to everyone who has weighed in on this thread.

Main obstacle for using Warp at our company is the privacy: we have the strong governance model and we can't use 3rd party LLM providers.

We would use BYOK and point it to the private instance hosted in Azure, AWS etc. Buying a license is not an issue: we just need to make sure that data never goes to any of Warp servers, which probably makes billing a bit challenging for Warp.

We tried other solutions with BYOK, but none of them work with agents as good as Warp.

From an enterprise/privacy perspective, the important distinction is not only "local model support" but "local execution boundary." If prompts, repository context, tool traces, or intermediate reasoning still route through a remote harness, many regulated users will not be able to use it even if the final model endpoint is self-hosted.

I would rank the options this way:

• Short term: arbitrary OpenAI-compatible endpoint support is useful, but should be labeled clearly if the harness remains server-side.
• Medium term: ACP client support can unlock local harnesses without Warp needing to own every provider integration.
• Long term: client-side/open harness is the strongest trust model because users can verify where code, prompts, and tool outputs flow.

A good product surface would show an "effective data path" for each provider: what stays local, what goes to Warp, what goes to the model endpoint, and what is logged. That is the question security teams will ask before approving local/private model workflows.

Came here as heard Warp now supporting Windows. Installed and then immediately uninstalled after realising the product is effectively useless without sign up and sending data to yet another provider. With most workstation level laptops now coming with a dedicated NPU or GPU with a few gig of VRAM (even shared RAM is OK for lower end qwen models) people may as well make use of to make terminal life easier. Personally just want an AI shell for system management and basic automation:

• Handling of remote sessions including auto switching from Windows powershell to whatever shell the remote system has. Can't install anything on the remote end so the ability for the harness to recognise the change in shell and keep operating.
• Ability for AI to log into remote system and carry out tasks at direction with strong granular guardrails (regex).
• Password hand-off back to user on logon for password input (either via SSH key password or remote user password)
• Automatic detection of password prompts in remote session for hand-off e.g. sudo, keystores, secondary SSH connections.
• The diff tool would be good for other tasks such as comparing configurations before and after changes.
• Basically any use case where remote desktop is not available but complex operations need to be carried out e.g. modifying registry keys
• generation of quick single shell one liners to manipulate and write data. what’s that sed or awk flag i needed.
• generate complex powershell without consulting reams of documentation.

Not interested in coding capabilities as use other tools for that.

1 and 3 are the better options. Given there are already forks in the wild why not encourage them to PR (if they haven't already)

It's frustrating how big companies constantly try to seize control of your computer and data. LLMs and their harnesses should act as assistants to the terminal, not as supervisors. If Warp continues with its closed mindset, open alternatives like OpenWarp or other terminal+LLM apps will thrive and take its place.

If I can contribute in any way whatsoever whether it's bug bounty for your project or contributing code. Please let me know I'd be highly interested. I have a personal vendetta against warp. I was just thinking along the lines of instead of starting. My own repository in starting this whole task, out from scratch, I would join the community.

This is something that I did not do with deep seek

now that's music to the ears,Also add ability to fetch model, for /models endpoint, as well as context windows, and whether model has vision or not.

Quick update here: we’ve shipped two related pieces of this work.

BYOK is now available on the Free plan for individual users, and Warp now supports custom inference endpoints compatible with the OpenAI Chat Completions API.

That means you can use your own OpenAI, Anthropic, or Google API key, or connect Warp to an OpenAI-compatible endpoint such as OpenRouter, LiteLLM, z.ai, a gateway, or a similar setup.

Docs:

• BYOK: https://docs.warp.dev/agent-platform/inference/bring-your-own-api-key/
• Custom inference endpoints: https://docs.warp.dev/agent-platform/inference/custom-inference-endpoint/

Fully client-side local model support is still a separate direction. We’re planning a lightweight local client harness so Warp can connect directly to local models without routing through Warp’s servers, and we’re also planning support for Agent Client Protocol so developers can bring other harnesses into Warp’s terminal UI.

If you try the new flow and hit a specific issue with a provider, endpoint, or model, please open a focused GitHub issue with the details so we can track it directly.

I was about to test this, with the understanding that our internal LiteLLM proxy works, but as it turns out it needs to be internet accessible - which is understandable if the traffic bounces through your services. I will wait for the version that does not require publicly accessible endpoint.

though,I got to say, this page https://docs.warp.dev/agent-platform/inference/custom-inference-endpoint/ states
Custom inference endpoint | Connect Warp to an OpenAI-compatible endpoint such as OpenRouter, LiteLLM, z.ai, or an internal gateway. | Free and all eligible paid plans
"internal gateway" is not yet true then.

If you want to test your local LLM before Warp adds support, I used the free tier of Cloudflare Zero Trust Connectors (it's a tunnel that you can run in a Docker container) to make my local LM Studio available publicly (with authentication of course). You'll need a domain though.

Cloudflare turns on "Block AI training bots" by default for your domain, I had to disable that, otherwise Warp was getting 403s.

This discussion in linked in #8759, but I don't see any mention of support for using ChatGPT Pro/Plus subscriptions. It would be really great to have this on the roadmap. Not everyone can afford paying per-token with API keys.

Echoing @Patrik88:

I want to use Warp as my full-time coding agent, but relying on standard per-token API keys (BYOK) is too expensive for heavy daily use. Implementing a flexible provider layer—similar to the open-source @mariozechner/pi-ai library—would solve this perfectly.

For folks who need fully self-hosted local/remote model routing today (not just client-side harness migration), one option outside Warp ecosystem: WinkTerm — open-source AI terminal where AI and user share the same PTY. Bring your own API key, type # at the prompt for in-terminal chat, agent pre-fills commands and you press Enter.

Docker deploy, SSH/SFTP, HTTP Agent API, MIT: https://github.com/Cznorth/winkterm

Different product (web self-hosted vs native terminal), but relevant if local-model support timeline matters for your workflow.

Looking forward to use, any new status on fully client-side local model support or lightweight local client harness so Warp can connect directly to local models without routing trough external (as we say Kirche ums Dorf bringen - Bringing the church around the village) ?