jcode vs tau
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 — Hugging Face's Python port of Pi's minimalist coding agent: a real terminal agent with TUI, sessions and skills — built to be READ, with a clean brain/environment/frontend separation.
Both are lean open coding agents rejecting harness bloat: tau is a minimalist Python port of Pi with TUI and skills; jcode is a Rust binary optimized to the byte — pick Python hackability or raw footprint.
| jcode | tau | |
|---|---|---|
| Stars | 13k | 2.1k |
| Forks | 1.4k | 226 |
| Language | Rust | Python |
| License | MIT | MIT |
| Last activity | today | yesterday |
| Topics | coding | coding, agents |
| Curated connections | 2 | 2 |
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.
tau — the curator's take
The teaching codebase for how coding agents are shaped: tau_ai (provider-neutral streaming) → tau_agent (the portable brain: loop, tools, events, sessions) → tau_coding (the actual app). That boundary discipline — the core knows nothing about rendering or config paths — is the lesson most production harnesses can't teach because they've already violated it. And unlike a toy, it's a usable daily agent with durable sessions. NOT the benchmark baseline (that's mini-swe-agent's 100 lines) and not the feature-race winner — no MCP sprawl by design. Read tau to understand the architecture; read mini to understand the loop; use whatever you like after.