jcode vs openhuman
Rust coding-agent harness built for footprint: ~28MB per session vs 140-390MB for Codex/Claude Code, instant boot, optional local embeddings — made for running many sessions in parallel. — versus — Rust agent harness built for fleets: desktop app, terminal or embeddable library, with built-in memory, browser/desktop control, 119 OAuth apps and 14 channels — 500 agents on a $10 VPS.
Both are Rust harnesses that sell footprint — tens of MB per session so many run in parallel; jcode stays a coding agent, OpenHuman adds memory, channels, browser control and an embeddable library.
| jcode | openhuman | |
|---|---|---|
| Stars | 20k | 42k |
| Forks | 2.4k | 4.1k |
| Language | Rust | Rust |
| License | MIT | GPL-3.0 |
| Last activity | 3 days ago | today |
| Topics | coding | agents, coding |
| Curated connections | 4 | 3 |
jcode — the curator's take
The RAM numbers are the whole pitch and they only matter at multiplex scale — at 10 concurrent sessions the gap vs OpenCode is 260MB against 3.2GB, and that's when you care. Single-session users won't feel it. 'The most intelligent harness' is self-published benchmarking (jcode.sh/bench) — treat it as marketing until third-party numbers exist. When NOT: if you live off one ecosystem's plugins and skills (Claude Code, Codex), the megabytes you save don't buy back the integrations you lose.
openhuman — the curator's take
Interesting if you want one agent per customer or per task at fleet scale, or a typed Rust API instead of shelling out to a CLI — its own 10-task SWE-bench run shows far less memory, tokens and time than Claude Code, Codex or Hermes. Caveats: early beta, benchmarks are self-run, GPL-3.0 constrains embedding it in closed products, and tool search and on-screen clicks lean on the hosted Jev model.