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Show HN: Rui, orchestrating 100 agents in 2.5 MB of RAM

Hi HN, I've built Rui (cotton in hindi), an agent runtime built for memory efficiency. The main design choice is that sessions share a host rather than each being a process. Agents are state in DB and the host handles the active work through a share event loop. LLMs are getting better at coordination and teams (swarms) of agents are coming. Memory is getting expensive and my machine is always using swap. We cannot be wasting hundreds of MBs of RAM for an orchestrator (AKA the harness) of LLMs an

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Source record

Recorded metrics

points · 1 votes
2026-11-07 16:45:31.756Z
cumulative since publication

Research analysis

Project record

Repository: DivyanshGolyan/rui. Source account: Hi HN, I've built Rui (cotton in hindi), an agent runtime built for memory efficiency. The main design choice is that sessions share a host rather than each being a process. Agents are state in DB and the host handles the active work through a share event loop. LLMs are getting better at coordination and teams (swarms) of agents are coming. Memory is getting expensive and my machine is always using swap. We cannot be wasting hundreds of MBs of RAM for an orchestrator (AKA the harness) of LLMs an

Observed adoption & comparisons

Source votes: 1 votes (Nov 7, 2026 UTC; cumulative since publication) Recent records to compare: “meta/astryx” (GitHub Trending, Nov 7, 2026); “google/skills” (GitHub Trending, Nov 7, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.

Adoption decision

Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.

Unverified requirements

Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.

Next test

Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.