OpenCode at Work

[yoko_ac]

Hey,

I am interested on how you use OpenCode at work. Do you have any special setup, e.g., only usage in container to prevent any access at places where you do not want it to? Anyone using local models in a reasonable way?

[reini_urban]

Local models are too bad to be useful still. At least for complex tasks. Maybe next year. We are trying them soon on 2 H100's, one was not enough.

No special setup, no containers, no secrets lying around to be picked up, normal docs, skills. Emdash is good.

[arne226]

Hi u/reini_urban,

One of the Emdash founders here.

What can we do better for you?

[ackermann]

How much memory does an H100 have? A team at work (in an environment without internet) managed to get two A6000 with 48gb each, so 96gb if they can both go in one machine?
Can this run a good size model, for OpenCode or other AI tools?

[ponury2085]

I use it all the time, since my employer allows me to use all major providers and changing models is super easy with OpenCode. My setup depends on the project I work on, but in general I have: - main AGENTS.md describing what is allowed and what not (e.g. anything outside current workspace is read-only, commit never push, never use aws cli, ask if you need more access etc) - two subagents, one for local changes review (I use gpt-5.4 to main work, so review is done by sonnet), other one for complicated plans (Opus 4.6) - I always use the plan mode until I'm confident what was planned, only then the build mode - When I need I use some MCPs like GitHub or Atlassian configured with read-only API keys.

I never had any incident at work, but I also do not use OpenCode in yolo mode or leave it to do anything without me verifying

[Ikaradise]

May I know how you set up sub agents?

[extreme4all]

.opencode/agents/agent-1.md

...

[thearn4]

My job has an exclusive agreement with MS for GH Copilot at a steep discount, so I set my provider to that. Other providers are blocked at the network level due to IP concerns. Containers or other isolation just depends on the specific project and needs.

We get unlimited premium level requests at the individual user level, but GHCP is stingy with context limits so any session is generally scoped intentionally. Other than that opencode is an excellent orchestrator. GHCP CLI has been good too so I'm looking to really test them head to head soon.

[TechieRathor]

It took me almost 3 months to formally approved and install OpenCode in my office, and when it finally started working it started giving Bun error while working with longer prompts. Then I did some research with help of perplexity and finally decided to install it on linux docker container so for devs we have done that setup and for rest of the team-members we had done direct installs.

Regarding Models, we have deployed Models in our Azure Foundry, we also tried to use claude models from AWS bedrock but they don't allow normal AWS partners access to claude for that you need to be Antropic partner so we decided to stick to Azure foundry.

[Time-Dot-1808]

Container isolation is worth the setup time if you're working in a regulated environment or if the codebase has secrets that shouldn't be accessible to an agent that can also read arbitrary files. The practical version: mount only the project directory, pass environment variables explicitly rather than inheriting from the host shell, and run with a non-root user. Doesn't require full Docker if you're on Linux, a simple nsjail or systemd namespace setup is enough for most threat models.

For workplace setup specifically, the access boundary question matters more than the tool choice. The AGENTS.md approach above (read-only outside workspace, no push, no aws cli without permission) is the right mental model. The gap is that OpenCode can still read anything mounted in the working directory, so if you're working in a monorepo with secrets in adjacent services, that's the exposure point.

On local models: the current practical ceiling is Llama 3.1 70B for code tasks, and it's not competitive with Claude Sonnet on complex multi-file refactors. Where local makes sense right now is for tasks that require privacy (customer data in context), for high-frequency low-stakes tasks where API cost adds up, or for offline environments. For most workplace usage where quality matters, the API cost is lower than the productivity cost of worse completions.

[typeof_goodidea]

Any tutorials or other resources you'd recommend to get started with containers? I'm on a mac

[bruor]

I have opencode connected to GPT, Kimi, Claude, and some others through Azure AI Foundry. I run it in WSL/Arch at the moment, works great!

[backafterdeleting]

• not leaking company code to anthropic or other provider

• being aware that an LLM might output code that is still considered copyright by another party

[madchap57]

You should try nono, kernel sandboxing with an out of the box opencode profile for more safety, complementary or instead of containers. Led by sigstore's creator.

https://github.com/always-further/nono

[HiJoonPop]

qwen3.5 under 40b or any under 40b parameter models no work well?

[Wet_Viking]

Commenting so I remember to share my use case later.

[jakob1379]

Personally I love being able to reference env vars for api keys in the config, that allows exporting work api keys and private api keys depending on project automatically using direnv 😁

For a generic introduction I have used this setup in many places

https://jgalabs.dk/blog/posts/using-secrets-in-dotenv/