The most frustrating thing about AI coding agents isn't that they make mistakes. It's how confidently they build on assumptions.

Even when I tell them to verify things, they still forget context, skip due diligence, or move forward on stale information.

So I built LLM Orchestrator, an open-source Claude Code plugin that makes research, memory, planning, review, parallel execution, and verification part of the system, instead of something I have to keep reminding the agent to do.

I built it after using and loving Superpowers, which got me thinking about Claude Code skills as reusable operating patterns.

But for my workflow, I wanted two things it didn't fully give me: due diligence before code gets written, and an easier way to capture and reuse a project's decisions and conventions. (The memory model is inspired by ECC.)

So I added them:

• A research gate so agents verify docs, APIs, installed versions, local files, and assumptions before implementation starts.
• A memory workflow on top of Claude Code's native memory: it studies the codebase and drafts its key decisions and conventions, then the agents consistently apply them, so you curate project context once instead of re-explaining it.

The result is a more disciplined flow inside Claude Code:

• Explore the codebase and respect its patterns.
• Research assumptions before implementation.
• Write and review a plan.
• Split the work by dependencies.
• Run agents in parallel where safe, sequentially where needed.
• Review the result from fresh context.
• Fix what failed. Prove what changed.

That structure has a tradeoff: it trades tokens for correctness.

It's built for substantial engineering work where getting it right matters more than minimizing spend: multi-step features, refactors, and debugging that needs real investigation.

That's what I care about: not just making agents faster, but making their output more reliable.

I open-sourced it because it's been working extremely well in my own workflow, and I wanted to give back to the community I've learned so much from.

I'll keep refining it, and if you find it useful and want to contribute, you're more than welcome.

Agents create speed. Structure makes it shippable.

That's what I tried to capture with LLM Orchestrator.

Would love feedback from anyone building with higher-level AI-assisted engineering workflows — the repo is on GitHub.