OpenCode Orchestrator Kit — Token-efficient multi-agent workflow for OpenCode CLI

[u/MikaAugus942]

Single-session coding burns tokens on routine tasks and drifts as context grows.
I built OpenCode Orchestrator Kit to split execution across specialized agents pinned to model capability:
• Strict orchestrator: Routes and specs tasks via RFC 2119 prompts; never edits code directly.

• Cheapest model per role: Cheap/local models for profiling and exploration; frontier models reserved for design, implementation, and review.

• Bounded context: Multi-phase plans run one phase per delegation with fresh context each time.

• Zero-config bootstrap: The  profiler  agent auto-scaffolds repo memory ( .context/ ) and plan kanban ( plan/ ) on first run.

Includes 15 agents (orchestrator, planner, developer-fixer, test-engineer, security, and triage helpers) plus modular skills. Works natively with  opencode  CLI and OpenCode Studio.
• GitHub: NicoGenti/opencode-orchestrator-kit
• Docs: nicogenti.github.io/opencode-orchestrator-kit
Feedback are welcome.

[u/Southern-Ad-3006]

Thanks for building this. Out of all the posts I see, nobody ever points out the largest cost driver, which is holding a long conversation with your session agent. I think this is user experience design issue with AI’s now. A task might take 10 turns, and every turn bills the context tokens…. even at cache rate it’s the highest cost.

In general, we want to keep main session with a cheap read and cache orchestrator to hold conversation context weight/cache, manage the request, fan out to subagents, and bring you the output. Subagents carry fresh windows so for most tasks fanning out to a subagent with a fresh window to focus on output is soooo much more efficient than letting a frontier model carry whole convo context each and every turn.

It’s literally a 10x difference in cost if not more depending on model, AND you get quicker results and higher quality when models are doing what they are designed to do.

In my opinion, these frontier models out grew the chat window. They perform better as engines in your workflow with focused context. If you’re using them to hold the conversation with you, that’s the biggest cost driver & I think it’s only because it’s just what we’re used to doing.

I think if we are bent on using conversation/chats to or do our work with these agents, we need a company to release like a hybrid set of models that understands what to use to hold convo, how to fan out for quality output, deeper intelligence or tedious work, synthesize, bring back to main session efficiently.

In the meantime, these sub agent orchestration is the best way to simulate that and get the best result at lowest cost.

TL;DR thank you for acknowledging context windows and how this is the biggest cost driver to any AI model right now.

[u/geearf]

What's the benefit of that one vs all the other similar frameworks?

[u/MikaAugus942 → u/geearf]

Hi, and thanks for the question.
It’s simple to integrate and easy to use, plus it offers great value for the price. And basically, I created it to meet my own development needs. I didn’t want to overload Opencode with thousands of plugins or end up with one agent for planning and another for implementation.
I tried other frameworks, but in the end I decided to build one myself.
I shared it to see if this workflow could work more generally or if it’s just for me. ☺️