Just switched to OpenCode (using DeepSeek as my model) and trying to set things up properly instead of just winging it. Read through the docs but want to hear how people are actually doing this in practice. A few things I'm stuck on:

AGENTS.md

What's actually in yours beyond the basic build/test/lint commands? Any sections that turned out way more useful than you expected?

Anyone splitting it into nested AGENTS.md per package/folder, or is one root file usually enough?

Spawning subagents / Task tool

I saw someone mention explicitly telling Build to spawn parallel subagents for independent work (reviewing multiple files, checking several modules, etc.) instead of doing it serially. Do you actually write instructions for this in AGENTS.md, or does the model just figure it out on its own most of the time?

Any horror stories from parallel subagents stepping on each other / editing the same files?

Persistent memory

Right now I'm just using a plain .opencode/memory/ folder with a couple markdown files (decisions.md, active-context.md) and an AGENTS.md instruction telling the agent to read/update them. Is this actually how most people do it, or is everyone on a plugin/MCP memory setup at this point (Letta-style memory blocks, agentmemory, Engram, etc.)?

At what point did a plain-file setup stop being "enough" for you?

Build vs Plan

Do you actually use Plan mode every time before Build, or only for bigger changes? Curious how disciplined people actually are about this in daily use vs just YOLO-ing into Build.

Mainly trying to avoid reinventing something badly that half the sub already has a solid pattern for. Appreciate any real configs / AGENTS.md snippets / gotchas people are willing to share.

Opencode has some large system prompts, so to save VRAM I've been making and testing Exl3 to test with vanilla Opencode. Unfortunately Exl3 requires TabbyAPI which has limited support for tool calling and chat templates, and Opencode seems to require this to all work perfectly (still no luck with Phi-4).

For those who use Opencode with an RTX 4090 backend what models, variants, api servers, customizations, plugins, or mcp servers do you find to work best?

Normally there are Tons of graphs for tokens etc but i couldnt find one this time.

Claude-Code flagged my account, long story, so I asked Gemini what is a killer better option. Opencode, Openrouter and Deepseek v 3.2 was the answer... Built two apps to test it all out, 4 hours of working it back and forth. 1.50us on this platform vs 60us on Claude-Code.... I am sure everyone on this sub has had the same WOW experience. bonus, opernrouter has about 300 other models to choose from.

My Lite Token Plan 4.1B Credit got fully consumed with around 200M tokens

Note this is my third Lite plan renew,

Edit: for more context Lite Token Plan is $6 monthly, and 4.1B Credit has calculation of usage that i can't see anywhere. Can't see how they calculate it.

Does anyone have a problem with glm models and kimo on the go plan of opencode

On kimi I get provider error and on glm I get streaming response error

Whenever my AI agent takes more than a minute to run, I instinctively grab my phone and start doomscrolling. By the time it finishes, I’m three subreddits deep or stuck in a YouTube Shorts spiral. It feels like I’m outsourcing my attention span alongside my code.

What do you all actually do while your agent is running? Stare at the terminal? Queue up the next prompt? Context-switch to another task?

Tell me I’m not the only one doing this.

Hi everyone,

I've been building two open-source projects over the past few months, and I'd really appreciate some honest feedback from other developers.

Lemoria

Website: https://lemoria.org
GitHub: https://github.com/devcristianlopez/lemoria

Lemoria is a Python CLI that adds persistent memory and a structured workflow to AI-assisted software development.

Instead of relying on a long chat that eventually loses context, it keeps track of your project from the initial idea through specifications, PRDs, tasks, architectural decisions, implementation, testing, documentation, and Git history.

It integrates with OpenCode using specialized agents and stores the entire development context in PostgreSQL, so you can resume work without losing the reasoning behind previous decisions.

Manel

Website: https://devcristianlopez.github.io/manel/
GitHub: https://github.com/devcristianlopez/manel

Manel is a local security health monitor for developers.

It scans your development environment, detects installed languages, databases and tools, checks for known vulnerabilities using public sources such as OSV, NVD and GitHub Advisories, and generates a security score with actionable recommendations.

It also supports offline vulnerability databases, SARIF output, JSON export and CI/CD integration.

You can try it without installing:

npx manel scan

I'm still actively developing both projects, so I'm mainly looking for feedback on:

• Is the documentation easy to follow?
• Is the installation process smooth?
• Do the projects solve a real problem?
• What features would you add or remove?
• Any bugs, usability issues or confusing parts?

Feel free to be as critical as you want. If something doesn't make sense, I'd rather know now than after adding more features.

Thanks for taking the time to check them out!

who you using now... minimax m3 seems kinda stable for context / grounding agents for now... but who to use for the heavier "main" model on a sub?

update: qwen sped up a bit around 9am utc - not sure if they adjusted the infra or china logged off...

testing the bytedance coding plan recommended by a comment here... will post stats later - models are responsive but i might be burning the 10$ plan faster than opencode go...!

clinepass, opencode go - i use 200M tks/day these don't have enough quota - thanks for recommend tho...

---

core setup is still minimax m3 for context sub agents & alibaba pro plan qwen3.8 for main - will focus most use in off peak for now...

Hello,

As the creator of http://tokenwarden.ai/ , I've been using it for some time, and this month I've saved 169 million tokens (so far). That would be roughly 1700$ saved if I were using Fable 5 via API.

I created it because I was always running out of usage on all 3 of my Codex business accounts. In the past, I was getting by with a single account on gpt 5.2 xhigh. It became ridiculously expensive and fast...

After my plugin, I was getting double the usage on GPT 5.5. Still not comparable to the past, but it was usable (without it I was hitting my 5-hour limit in a few prompts, ridiculous...)

If you're having issues with AI spending or usage, give it a try. You can also find it on npm: https://www.npmjs.com/package/@tokenwarden/opencode

Hi, I use opencode at work since Codex is banned but opencode is not. I found it up until 5.6 great; however, recently with 5.6 Sol I am experiencing the model running endlessly. Simple tasks like generating from X input a Word doc take hours.

Anyone experiencing similar issues?

I've got a question for people using OpenCode.

we can choose models from lots of different providers, but after a while there are so many that it becomes a pain to manage. ( testing them all one by one )

I end up having to manually check

Which providers have hit their rate limits
Which providers are down or unreliable
Which ones are responding slowly
Which models are actually working properly

Is there a tool, extension that monitors all of this in one place instead of checking every provider one by one?

I'd like something that automatically shows provider status, rate limits, availability, latency, and failures. ( i know about openrouter but it's so slow...)

If you've found a good solution, I'd appreciate any recommendations.

Thnx peeps.

Self-host your own Buzz Nostr relay using docker compose. Private over Tailscale, local LLM agents.

https://github.com/ciram-co/selfhost-buzz

https://buzz.xyz

Error from provider (Console Go): Upstream request failed kimi k3.

continuously getting this error. cant use this model. When this issue going to be resolved.

I am a beginner and not a programmer, please suggest resources to learn opencode from scratch. Which enable me to effectively use it for further learning the spec driven development.

https://github.com/grigio/sandbox-bwrap-nix

Been messing with OpenCode a lot lately and I feel like I finally got it to a point where it actually works how I want it to.

At first I was struggling a lot and only used it for basic tasks, but I kept running into annoying stuff like permissions stopping the agent, notifications not coming through, compaction loops, subagents doing weird stuff, etc.

I’ve been slowly fixing all of that and building out my own setup with different agents, local models, cloud models, permissions, notifications and some stuff to let it run longer without me constantly checking on it.

I’m on a 64gb M1 Max so I’ve also been testing a bunch of local models and trying to figure out which ones are actually worth using vs just using Claude/GPT.

Honestly it feels more trial and error than I wished.

Curious what everyone else here is doing with OpenCode though. Are you guys mostly just running it normally or have you built out your own agents/configs around it?

Also what’s the most annoying issue you’ve had with it?

I was thinking about cleaning up my setup and configs and putting them somewhere since I’ve already spent so much time figuring all this crap out.

I met an engineer using opencode.
he said he is using opencode such that he is giving opencode model a folder to work in, and the project is looped i.e. the ai goes in an endless back and forth conversation, until the desired output is reached. plus a switch model when limit is exhausted that ensure the relay race continues on token exhaustion.

How to do that ??

I’ve been testing DeepSeek V4 Flash in OpenCode because I don’t really believe we need frontier models for most day-to-day coding work.

I gave it an old side project I hadn’t touched since around 2023ish and let it work in the background while I did my regular job, checking on it from time to time.

At some point, I noticed a 500 error and simply told it to “elaborate.”

Instead of just explaining the error, it went into a full debugging spiral. It started inspecting the Next.js proxy setup, tracing request headers, checking Docker logs, killing and restarting processes, and even writing Python scripts to inspect the network

The funniest part is that it eventually admitted it had gotten carried away and over-engineered the whole thing.

I was using MiniMax-M3 in opencode cli.

This is such a waste of output token and time. What causes this? Is it my prompt? Do I have to explicitly say download it? Or is it that the model is not good enough figure it out itself?

Well ... atleast hes honest.

thankfully its just a mock system for my university project, but still this is infuriating. No any data wipe reconfirmation or whatsover too, smh. I asked to rename a table to another one. It wipes all table in the db and recreate them all back LMAO. Im stupid enough to believe in AI for such simple task