Local models + big context = slow. How are you orchestrating "map-reduce" style agent workflows?
I tried running local models (qwen3.6*, ds4 flash, gemma4*, etc) on my mbp pro m5 with 128Gb of unified memory and concluded the bottleneck is context size. The moment a conversation gets long (16k is already the bottleneck), inference slows to a crawl. If you work with Hermes agent you know this context size is almost the default with the bloated things. So my working strategy has become: chop every task into tiny pieces, spin up a fresh short session for each piece, and only pass the summary/output forward to the next step.
A concrete example: I want to scrape a bunch of sources overnight, collect the info, then generate a morning dashboard from all of it. The naive approach (one agent, one growing context) is unusable locally. The map-reduce approach feels right: many small parallel workers each doing one tiny extraction, then an aggregator that only sees the short summaries. But I'm building this by hand and it's fiddly.
What I'm wondering:
- Which open-source agent orchestration frameworks actually support this "stateless worker + tiny context per call" pattern well? Most agents I've looked at (CrewAI, AutoGen, default LangChain) drag the full history along, which is the opposite of what I need.
- Anyone built something like this already? A fan-out/fan-in pipeline where each LLM call stays small and the aggregator works only on compressed summaries?
- How are you keeping context per call minimal while still getting useful aggregation at the end?
PS: Not looking for hosted/API solutions specifically local-model constraints. Curious what patterns, frameworks, or projects people have landed on. Thanks!