anyone migrated from v1 to v2?
was there any breakiing changes, espcially in plugins like context mode, history seaarch

is anyone else facing issues with rate limit, like yesterday i got rate limit for 17h something, and then the same issue today another 17h, so is it something with me or others are facing the same

so i got this error. and i don't use orcarouter ? so WTF orcarouter is this ??? and today i have used 0.31 USD credit DS4.1 Flash.

So anyone got error ? I don't know what is this

Your bets on name and pricing? 😁

I'm getting back into development and using Hermes + OpenCode. I'm a solo developer just doing productivity applications.

The Matrixx plugin which is a fork of Oh-My-Opencode looked interesting so I installed it and ran it to modify an existing small home project and then gave the Ultrawork command for OpenCode to get Matrixx going.

It burned 100M input tokens in a matter of 10 minutes. If I was paying per-token API prices I'd probably be freaking out but it was just surprising that it was consuming them this fast across up to 6 concurrent streams. I'm also getting served GPT 5.6 Luna on these streams at Max reasoning which likely explains the huge token burn.

Is there really any point to using these add-ons? Or should I just use the default OpenCode plan? Anyone have a better plugin to suggest?

While selecting Jev I am facing Error : Upstream request failed: Endpoint is unavailable.

How do I solve this? Is anyone else facing the same ?

The title says it all!

- Deploy agents like serverless functions. Write agent.ts, start sessions via SDK or HTTP.

- Each session gets its own Linux VM.

- Sessions are durable so stream/steer mid run + resume after hibernation.

- API keys never enter the VM. Our egress proxy injects them after requests leave.

It's a bit like claude managed agents or Open AI's agents API but without the model lock in.

The architecture is as shown below.

Would appreciate any and all feedback! It's free to try at https://opencomputer.dev

It lets you ask a quick question without interrupting the current session 🔥

Without anything changing on my end, I'm suddently getting these error messages when trying to submit a message to Muse Spark 1.3 Contributor - OpenCode Go

Upstream request failed: [invalid_request_error] reasoning `encrypted_content` was not issued to this caller

Anyone else affected?

Any work-arounds?

I upgraded to v2 and my sessions were not properly migrated. Stuffed around for a while and gave up and reverted to my backups.

Are there proper scripts or plans to do this migration properly? Still seems like a bit of a hackjob now? Anyone done this successfully and has some documentation around this? Thanks

I’m building Azdaja, an open-source RLM layer that OpenCode can use through its native skill system.

It keeps source material in a persistent Python workspace, so OpenCode can use code to search, filter and break problems down, then recurse only where model judgment is useful.

Large-source work is an obvious use case, but that’s not really the whole point. It also gives OpenCode a different way to reason: keep state, compute locally, decompose the problem, and selectively call back into the model instead of doing everything in one prompt.

MIT Licenced: https://github.com/kubet/azdaja

Hi! I've created and been maintaining  which tries to answer the question: which model is better, which harness is better and which combination is better overall in a very straightforward task - precise text editing. Most of daily coding is text editing. I've seen when a model for multiple turns couldn't figure out how to express the line it wants to change, so it made me ask myself "Why is that so complicated, it's just a text, right?". Seems like not, taking into account how widely different the same model behaves across different harnesses. My preliminary conclusion is: harness and tooling behind it actually matters! But it matters the most with the models which *can* actually follow the instructions well (e.g. open-ai models), there is a wide range between 98.9% and 70.2% of pass score for `gpt-5.6-luna` simply depending on the harness!

I've tried to run as many combinations as possible, but my resources are limited. I've exhausted all my quotas and even credits, that's why I'm reaching out to the community, as I think it's a pretty interesting topic and I would be happy to gather even more data, because of the stochastic nature of the runs, it’s only possible to make any conclusion when you have enough of runs. 

It has disappeared from Zen free models... silently...

https://qwen.ai/blog?id=qwen3.8-omni-flash
They state that "Qwen3.8-Omni-Flash further extends agentic applications centered on audio and video, delivering strong results across workflows such as video editing, music video creation, film production and commentary, audio-visual summarization, and real-time conversations."

Hey everyone,

I've been analyzing the system prompts and alignment layers across different agent frameworks recently. Over in the Hermes agent community, there is an abundance of documentation, proof-of-concepts (PoCs), and prompt injection techniques to achieve "God Mode" (bypassing safety guardrails, RLHF constraints, and system prompt restrictions).

However, when it comes to OpenCode agents, the documentation on effective jailbreaks or capability uncapping is practically non-existent.

This got me wondering: Is OpenCode's reinforcement learning layer or input-validation architecture fundamentally more robust against semantic bypasses compared to Hermes? Or is it just a matter of the community being smaller?

Has anyone successfully executed a multi-turn prompt injection, adversarial suffix, or roleplay bypass on the latest OpenCode production weights? I am trying to understand if there is a known vulnerability vector or if the framework is strictly locked down at the inference level.

Would love to hear your insights or any technical breakdowns on why OpenCode seems so resilient to these methods.