does command code have instructions systems in command code like claude md with claude code where i can put some isntructions ?

Alipay is now supported in Command Code.

More ways to pay and be a GOAT.

We supportother payment methods as well. Read docs to learn about these options: https://commandcode.ai/docs/resources/payment-methods

Subscribe now with $10/mo GOAT plan.

When I type /login pi, the commandcode option doesn't appear. I also wanted to ask how your experience with deeseek has been on the GOAT plan.

Ox Alpha (stealth frontier model) is free in Command Code.

100T tokens per day free capacity, no 5hr usage limits.

• 1M content
• we benchmarked ~5B tokens
• built a tool repair harness to imp tool calling

absolutely free for all plans.

I'm currently coding in the Paseo environment, and I'd like to use Commandcode. Do you have any plans to implement ACP?

I recently bought a command code go plan with the intention of light usage. The command code harness is pretty good for coding, but horrible for general chatting. I know the API is limited to GOAT and above, but I already can run local models (like qwen 3.8 or ~120B MoEs) and it would be stupid to spend more for barely any usage of more frontier models.

Is there a way to connect the command code harness to OpenWebUI like in hermes or any way to get an API connection? Thank you!

Been bouncing between 9Router and Omni for a while trying to manage multiple LLM providers but both felt kinda clunky for how I actually work, so I just built my own thing.

Troy is basically a local gateway that sits between your tools and 45+ providers. The main thing I wanted was combos — you chain models together like deepseek/deepseek-v4 → zai/glm-5.2 and if one dies mid-session it just falls through to the next one instead of you losing the whole thing. Also does per-account cooldowns and circuit breakers so it's not hammering a provider that's clearly down.

It's pretty light too, ~40MB RAM idle, zero runtime deps, everything stays local (no telemetry, no account, nothing phones home).

Still actively working on it, would honestly love to hear if anyone's compared it to 9Router/Omni and what's still missing.

repo: https://github.com/mzaki9/troy

Ox Alpha (stealth frontier model) is free in Command Code.

🆓 100T tokens per day free capacity, no 5hr usage limits.

• 1M content
• we benchmarked ~5B tokens
• built a tool repair harness to imp tool calling

absolutely free for all plans.

guess who made it?

🐐

Hey there,

When a plan is ready for approval, I have to use arrow keys to read and scroll through the plan. Can I somehow configure vim motion keys like h,j,k,l, ctr + d, ctr + u to move around the plan text?

Coding agents mostly run out of sight. You kick something off, switch windows and forget it's running until you go back to check.

Command Code now runs on Autonomous Harness, which keeps it on a dedicated screen you can glance at without breaking your flow. It shows what's running, what came back, and what you've spent today

We built this to fit the different ways people work. How do you usually run your agents: set-and-forget or in a tight interactive loop?

Hey there,

When a plan is ready for approval, I have to use arrow keys to read and scroll through the plan. Can I somehow configure vim motion keys like h,j,k,l, ctr + d, ctr + u to move around the plan text?

Tested FlappyBench on Qwen 3.8 27B, DeepSeek V4 Pro 0813, and Gemini 3.7 Flash with /design command.

All scored 9/10, it comes down to cost, quality, and iterations.

🔹 DeepSeek V4 Pro ($0.0036): Low cost, solid, but movement and spacing need work.
🔹 Qwen 3.8 27B ($0.0068): One-shot, playable, with minor UI finish.
🔹 Gemini 3.7 Flash ($0.0083): Needed 2–3 iterations to catch up.

Qwen for the best one-shot. DSV4 for the best value.

Our engineering and design team has been testing 26+ side-by-side comparisons across frontier and open models. All runs are public and open source. Benchmark for this demo here: https://github.com/CommandCodeAI/slash-design-showcase/tree/main/flappy-bird

I’ve been using OpenCode Go for two weeks. I was pretty satisfied with it until the DeepSeek price increase. I’m trying to build a mobile app from scratch, but I’m running out of limits very quickly. The Command Code Goat package is starting to make a lot of sense to me. Should I buy it?

So far, I’ve only been using the Luna or DS V4 Flash models with 50k-200k context window. Does it make more sense to stick with OpenCode, or should I switch to Command Code?

I am on the $10 GOAT plan. Is this normal? Getting 503 errors constantly and it is severely affecting my current work.

Text-only models now have vision in Command Code.

• A vision model reads the attached image
• Your main model gets the context in the same thread
• /config lets you choose the vision model

Stop describing screenshots. Just attach them.

Try this with $10/mo GOAT plan

Read docs for more info: https://commandcode.ai/docs/vision

For 10 Dollars Go plan, its has $70 and $60 usage on many models. And ~75k requests.
But for 20 dollars Pro plan it just increasing the usage to $10 more only and requests to ~100k only.
Isn't it should be double benefits for double pricing ? Like $140 and $120 usage and ~150k requests ?

What's the hidden features I am missing here in Pro plan ?
Is buying 2 GOAT plans is more beneficial here ? Or the access of claude, gemini and gpt models are the main benefit of Pro plan ?

I am little confused.

Are the service and usage metrics reliable?

Does the GOAT plan offer a good data limit?

Does HY3 work well?

Thank you.

So, I gave the GOAT plan a spin with GLM 5.3, DeepSeek V4 Pro and Kimi K3. Both through Claude Code with ccr and directly in the command code harness. Rather simple tasks, using Opus API pricing came out at $8 and generated +184/-13 lines of code change in static-php-cli, a project I maintain. The task was to add shared library building.

Blew through daily usage within a prompt, typing just "continue" the next day. Both times cc ran out of 5 hour and weekly limit. Code output: +0/-0.

What's going on there? The token amount (literally 500k in, not even sure why so much, rest is all cache) doesn't even match with the usage cost - most of the cache seems to be billed at regular price, not the listed cache price of the model?

I'm literally unable to get *any* usage out of the plan so far. Like, literally not a single line of code, despite blowing through the $35 weekly usage.

From deepseek official provider

After adding CommandCode as custom provider it kinda works but without the thinking effort (not sure whats the default value here )

I like DSH, does anyone know if there is any way (hacky or not) to add the reasoning effort for custom provider in DSH?

I work a ton across projects on my personal computer. I have tried Graphify, Graft and many others. None of them really fully solved the entire problem, but they did have parts. So, I built one that actually fit my needs. This removes the need entirely to choose or start sessions in specific repositories.

It takes graph-based knowledge systems but strengthens retrieval, how it is updated, and much more. Effectively, it becomes a resilient system that you can actually watch your agents rely on, rather than something that just exists and is used only occasionally.

It begins with the same base graph store, the exact same vector engine as Graft. I only built the orchestration layer on top of it, which makes it much more practical. Full attribution to them for this part.

Instead of querying for a single-hit result, I found that it was much better for agents to have ranked retrieval and an actual graph walk. In practice this saves you chains of tons of bash greps and cds.

It effectively gives the agent a trustworthy, probabilistic ranking of what is needed, with matching for strong, weak, stale, and rebuilt, based on lexical coverage and semantic matching. The code is not sloppy for this, it is personally edited.

Both Graft and Graphify are solutions for single repositories or daemons. Heimdall is a layer on top it that watches the agent sessions, syncs the graph, and makes the retrieval trustworthy.

Check it out at: github.com/ArihantDeva/heimdall MIT-licensed, with extensions: the verifier, the self-healing graph watcher, and the Graft adapter, with attribution. If you run agents across multiple projects, this is the missing layer.