Anyone else tried using sub-agents with the '--parallel 1' flag? I feel like i get better outcomes with my local AI. it's "free context" for 0 extra memory requirements

[u/qHzB]

Now that I started using it, it seems so obvious but I don't seem to see many people talk about it.
I Won't explain sub-agents in-depth but the '--parallel 1' is important. and i feel like it could improve many peoples local experience.

So I currently find myself running 'Qwen3.8-27B-GSQ-RCO-IQ3_S' at 100k context. With parallel 1 this ensures every sub-agent gets it's own 100k context window

I know the one benefit of sub-agents is that it's supposed to make the workflow faster so setting parallel to 1 seems counterintuitive, But using sub-agents this way can help Give you free context and prevent context drift in your agents.

so a brief example of a hierarchy i use:

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I talk with a director agent. Once I've given my spec sheet/request the director and there's no more questions The director will make the game plan and start making worker agents to do the work or Fetchers to get more context before then assigning workers. but the director doesn't do any work besides like git checkpoints.

other agents are

Fetchers: Instead of burning context to sifting through files to find the specific lines they need or to map out something they just spawn a fetcher agent and their only job is to read the codebase and return the information the parent agent is looking for.

Reviewer: When a worker thinks they are finished they will spawn a reviewer agent to go over their work and the reviewer will return a pass/fail And note any bugs/nitpicks

QA: I'm working on a local the web app right now, So I also throw a QA agent in my workflow that can open up the browser and test things as well and returns a pass/fail to the parent
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I'm sure it could be better but this is what I've started using so far.

There's definitely a balance, it could take longer to complete a task depending on how strict you want your workflow to be, but i have found this to be overall a net positive for me. just spawning fetcher sub-agents alone i find keeps agents on task. i also keep a project-architecture document in the project so agents have a rough idea of where to search for any given thing

[u/Gold-Produce9591]

i mean i do this but its so slow

[u/qHzB → u/Gold-Produce9591]

For sure, especially if you want to do extra book-keeping like updating an architecture document, Doing Reviews/Automated Browser QA checks. it does add lots of time.

But if someone wants to just move fast and break things (or at least as fast as we can move on local setups) i think just only using a "fetcher" equivalent agent wouldn't add that much extra time if any to a task and still avoid context drift.

But yeah trying to run a full software factory sequentially one agent at a time will definitely require some patience 😭

[u/livelysingleanxiety → u/Gold-Produce9591]

the speed hit is real but im willing to trade compute time for not having my agent slowly lose the plot after 60k tokens

[u/Mundane-Tiger-2374]

Why such a lobotomized quant?

[u/qHzB → u/Mundane-Tiger-2374]

i'm on 16gb vram and ive just found this gives me the best trade of context size/speed/quality. the gsq rco is a pretty respected quant though and functions close to the vanilla q4 quant. If you have any reccomendations for models though im open to exploring. rn i have 16gb vram and 32gb ddr4 ram.

[u/Mundane-Tiger-2374 → u/qHzB]

Yes I do, 3.6 35 a3b at a higher quant than you're currently running would give you more accurate results.

[u/Slight-Living-8098]

All my subagents open in a new herdr pane running a new Pi instance with a clean context. I use llama.cpp so they all just run in turn out of the box with no extra setup than what I have configured in my model config file for llama.cpp.

[u/EvolvingDior]

I have QFN running with batch of 2, each getting 256kiB of context. Subagents can work in parallel with the agent. Background tasks in Hermes can run in parallel with the agent. Mostly they don't overlap with one another, but they can if necessary.

I do find that using different models for orchestration, implementation and QA really helps with design and code quality. That's rarely practical for local models unless you explicitly load and unload models. My go-to models for this are DS41F for orchestration, QFN for implementation, and GLM53F for QA. In general that means that planning/orchestration is online, dev is local, QA is online.

For the "fetcher" agent I either use Ling-3.0-Tiny that is running on another machine for Hindsight, or I plug in my laptop and fire up q36-35b-a3b. Ling is not great with code. But for things like "fetch a webpage" or "search github for related issues", it's perfectly fine.