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jcode

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The most RAM efficient harness

Subagents19.5k estrellas2.2k forks● RustMITActualizado today
Nota editorial

jcode is a terminal-based coding agent harness written in Rust that wraps LLM providers, including Claude via the Anthropic API, inside a TUI (terminal user interface) designed for running multiple concurrent agent sessions. It supports multi-session workflows where several coding agents operate in parallel, and includes a local embedding system for memory that lets agents retain context across sessions. The tool connects to Claude through direct API access rather than Claude Code or MCP, and also supports OpenAI-compatible endpoints, making it provider-agnostic. A concrete standout is its resource efficiency: with local embeddings disabled, a single jcode session uses roughly 27.8 MB of RAM, compared to 386.6 MB for Claude Code and 3,237 MB for OpenCode at ten concurrent sessions, making it practical to scale parallelized agentic workloads on modest hardware. The primary audience is developers who want to orchestrate multiple AI coding agents simultaneously from the terminal on Linux, macOS, or Windows.

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  • ✓Actively maintained (<30d)
  • ✓Healthy fork ratio
  • ✓Clear description
  • ✓Topics declared
  • ✓Documented (README)
Flags
  • !Install pipes a remote script into a shell (curl | sh)
Last scanned: 9/11/2026
Install as a Claude Code subagent
Method: Clone
Terminal
git clone https://github.com/1jehuang/jcode && cp jcode/*.md ~/.claude/agents/
1. Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
2. Start a new Claude Code session to load the agents.
3. Delegate work to them with the Task/Agent tool or by name.
Items instalables

1 items en este repositorio

optimizationSkill

Use when improving performance, latency, throughput, memory usage, or general efficiency. Start by defining target metrics, measuring comprehensively, attributing bottlenecks, validating with static analysis, and prioritizing macro-optimizations before micro-optimizations.

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Sobre el repo

Resumen de Subagents

<div align="center">

# jcode

[![Latest Release](https://badgen.net/github/release/1jehuang/jcode?icon=github)](https://github.com/1jehuang/jcode/releases)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](LICENSE)
[![Platforms](https://img.shields.io/badge/platforms-Linux%20%7C%20macOS%20%7C%20Windows-blue?style=flat-square)](https://github.com/1jehuang/jcode/releases)
[![Last Commit](https://badgen.net/github/last-commit/1jehuang/jcode/master?icon=github)](https://github.com/1jehuang/jcode/commits/master)
[![GitHub Stars](https://badgen.net/github/stars/1jehuang/jcode?icon=github)](https://github.com/1jehuang/jcode/stargazers)
[![Discord](https://img.shields.io/badge/Discord-Join%20Community-5865F2?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/nBe9vGyK9a)

The most RAM efficient harness <br>
The most intelligent harness

<a href="https://trendshift.io/repositories/25042?utm_source=repository-badge&amp;utm_medium=badge&amp;utm_campaign=badge-repository-25042" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/repositories/25042" alt="1jehuang/jcode | Trendshift" width="250" height="55"></a>

<a href="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-yc-launch.mp4">
  <img src="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-yc-launch.webp" alt="jcode YC launch video" width="800">
</a>

<br>

[Website](https://jcode.sh) · [Docs](https://jcode.sh/docs) · [SDK](https://jcode.sh/sdk) · [Benchmarks](https://jcode.sh/bench) · [Features](#features) · [Install](#installation) · [Quick Start](#quick-start) · [Further Reading](#further-reading) · [Contributing](CONTRIBUTING.md)

</div>

---

<div align="center">

## Installation

</div>

```bash
# macOS & Linux
curl -fsSL https://jcode.sh/install | bash
```

```powershell
# Windows 11 (PowerShell 5.1+)
irm https://jcode.sh/install.ps1 | iex
```

Need Homebrew, source builds, provider setup, or want an agent to set it up for you?
[Jump to detailed installation](#detailed-installation).

### Updating

Run `/update` in the TUI to download the latest stable release in the background
and reload with your session preserved. From a terminal, use `jcode update`, then
restart the client. Both commands use the same update policy, including for dev builds.

Older or equal release versions are skipped. For a development build, Jcode also
compares the running binary's Git commit with the release tag. Builds ahead of,
identical to, or diverged from the release are preserved. If ancestry cannot be
verified locally or through GitHub, the update stops rather than risking a downgrade.
The displayed dev patch includes a commit-count offset, so it is not used as a
release version comparison.

This is the default `features.update_channel = "stable"` behavior. An explicit
`"main"` channel still opts into source-branch updates. Use `/rebuild` or the
self-dev build workflow to rebuild your own checkout.

---


<div align="center">

## Performance & Resource Efficiency

</div>

jcode is built to be as performant and resource efficient as possible. Every metric is optimized to the bone, which is important for scaling multi-session workflows. Here we sample a few metrics to show the difference: RAM usage and boot up.

### RAM comparison

<div align="center">

<table>
  <tr>
    <td valign="top" align="center" width="50%">
      <strong>1 active session</strong>
      <table>
        <thead>
          <tr>
            <th>Tool</th>
            <th>PSS</th>
            <th>Comparison</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td><strong>jcode (local embedding off)</strong></td>
            <td align="right"><strong>27.8 MB</strong></td>
            <td align="right">baseline</td>
          </tr>
          <tr>
            <td><strong>jcode</strong></td>
            <td align="right"><strong>167.1 MB</strong></td>
            <td align="right"><strong>6.0× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>pi</strong></td>
            <td align="right"><strong>144.4 MB</strong></td>
            <td align="right"><strong>5.2× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Codex CLI</strong></td>
            <td align="right"><strong>140.0 MB</strong></td>
            <td align="right"><strong>5.0× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>OpenCode</strong></td>
            <td align="right"><strong>371.5 MB</strong></td>
            <td align="right"><strong>13.4× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>GitHub Copilot CLI</strong></td>
            <td align="right"><strong>333.3 MB</strong></td>
            <td align="right"><strong>12.0× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Cursor Agent</strong></td>
            <td align="right"><strong>214.9 MB</strong></td>
            <td align="right"><strong>7.7× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Claude Code</strong></td>
            <td align="right"><strong>386.6 MB</strong></td>
            <td align="right"><strong>13.9× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Antigravity CLI</strong></td>
            <td align="right"><strong>243.7 MB</strong></td>
            <td align="right"><strong>8.8× more RAM</strong></td>
          </tr>
        </tbody>
      </table>
    </td>
    <td width="24"></td>
    <td valign="top" align="center" width="50%">
      <strong>10 active sessions</strong>
      <table>
        <thead>
          <tr>
            <th>Tool</th>
            <th>PSS</th>
            <th>Comparison</th>
          </tr>
        </thead>
        <tbody>
          <tr>
            <td><strong>jcode (local embedding off)</strong></td>
            <td align="right"><strong>117.0 MB</strong></td>
            <td align="right">baseline</td>
          </tr>
          <tr>
            <td><strong>jcode</strong></td>
            <td align="right"><strong>260.8 MB</strong></td>
            <td align="right"><strong>2.2× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>pi</strong></td>
            <td align="right"><strong>833.0 MB</strong></td>
            <td align="right"><strong>7.1× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Codex CLI</strong></td>
            <td align="right"><strong>334.8 MB</strong></td>
            <td align="right"><strong>2.9× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>OpenCode</strong></td>
            <td align="right"><strong>3237.2 MB</strong></td>
            <td align="right"><strong>27.7× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>GitHub Copilot CLI</strong></td>
            <td align="right"><strong>1756.5 MB</strong></td>
            <td align="right"><strong>15.0× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Cursor Agent</strong></td>
            <td align="right"><strong>1632.4 MB</strong></td>
            <td align="right"><strong>14.0× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Claude Code</strong></td>
            <td align="right"><strong>2300.6 MB</strong></td>
            <td align="right"><strong>19.7× more RAM</strong></td>
          </tr>
          <tr>
            <td><strong>Antigravity CLI</strong></td>
            <td align="right"><strong>1021.2 MB</strong></td>
            <td align="right"><strong>8.7× more RAM</strong></td>
          </tr>
        </tbody>
      </table>
    </td>
  </tr>
</table>

</div>

### Time to first frame

<div align="center">

| Tool | Time to first frame | Range | Comparison |
|---|---:|---:|---:|
| **jcode** | **14.0 ms** | 10.1–19.3 ms | baseline |
| **Antigravity CLI** | **383.5 ms** | 363.1–415.4 ms | **27.4× slower** |
| **pi** | **590.7 ms** | 369.6–934.8 ms | **42.2× slower** |
| **Codex CLI** | **882.8 ms** | 742.3–1640.9 ms | **63.1× slower** |
| **OpenCode** | **1035.9 ms** | 922.5–1104.4 ms | **74.0× slower** |
| **GitHub Copilot CLI** | **1518.6 ms** | 1357.4–1826.8 ms | **108.5× slower** |
| **Cursor Agent** | **1949.7 ms** | 1711.0–2104.8 ms | **139.3× slower** |
| **Claude Code** | **3436.9 ms** | 2032.7–8927.2 ms | **245.5× slower** |

</div>

Measured on this Linux machine across 10 interactive PTY launches.

### Time to first input
(time until typed probe text appears on the rendered screen; Antigravity uses its internal input-ready log marker because the sign-in screen suppresses probe echo.)
<div align="center">

| Tool | Time to first input | Range | Comparison |
|---|---:|---:|---:|
| **jcode** | **48.7 ms** | 30.3–62.7 ms | baseline |
| **Antigravity CLI** | **383.7 ms** | 363.4–415.7 ms | **7.9× slower** |
| **pi** | **596.4 ms** | 373.9–955.2 ms | **12.2× slower** |
| **Codex CLI** | **905.8 ms** | 760.1–1675.7 ms | **18.6× slower** |
| **OpenCode** | **1047.9 ms** | 931.1–1116.9 ms | **21.5× slower** |
| **GitHub Copilot CLI** | **1583.4 ms** | 1422.8–1880.0 ms | **32.5× slower** |
| **Cursor Agent** | **1978.7 ms** | 1727.3–2130.0 ms | **40.6× slower** |
| **Claude Code** | **3512.8 ms** | 2137.4–9002.0 ms | **72.2× slower** |

</div>

Measured on this Linux machine across 10 interactive PTY launches. Antigravity CLI was unauthenticated for this run; its sign-in screen rendered normally and emitted an internal `CLI ready for user input` marker, but did not echo the typed probe.

### Additional clients / memory scaling

<div align="center">

| Tool | Extra PSS per added session | Comparison |
|---|---:|---:|
| **jcode (local embedding off)** | **~9.9 MB** | baseline |
| **jcode** | **~10.4 MB** | **1.1× more RAM** |
| **pi** | **~76.5 MB** | **7.7× more RAM** |
| **Codex CLI** | **~21.6 MB** | **2.2× more RAM** |
| **OpenCode** | **
Topics
aiai-agentai-coding-agentclaudeclicoding-agentllmmcpopenairustterminaltui
Preguntas frecuentes

Lo que la gente pregunta sobre jcode

¿Qué es 1jehuang/jcode?

+

1jehuang/jcode es subagents para el ecosistema de Claude AI. The most RAM efficient harness Tiene 19.5k estrellas en GitHub y su última actualización registrada es del 2026-09-10.

¿Cómo se instala jcode?

+

Puedes instalar jcode clonando el repositorio (https://github.com/1jehuang/jcode) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.

¿Es seguro usar 1jehuang/jcode?

+

Nuestro agente de seguridad ha analizado 1jehuang/jcode y le ha asignado un Trust Score de 97/100 (tier: Verified). Revisa el desglose completo de comprobaciones superadas y flags en esta página.

¿Quién mantiene 1jehuang/jcode?

+

1jehuang/jcode es mantenido por 1jehuang. La última actividad registrada en GitHub es del 2026-09-10, con 431 issues abiertos.

¿Hay alternativas a jcode?

+

Sí. En ClaudeWave puedes explorar subagents similares en /categories/agents, ordenados por popularidad o actividad reciente.

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