jcode

Name: 1jehuang/jcode
Author: 1jehuang

The most RAM effiecent harness

Subagents12.2k estrellas1.3k 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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✓Open-source license (MIT)
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✓Documented (README)

Last scanned: 6/11/2026

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.

Instalar

Casos de uso

AI / ML Dev Tools Creative

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 most intelligent harness

<a href="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-memory-demo.mp4">
  <img src="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-memory-demo.webp" alt="jcode memory demonstration" width="800">
</a>

<br>

[Website](https://jcode.sh) · [Docs](https://jcode.sh/docs) · [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).

---


<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** | **~318.4 MB** | **32.2× more RAM** |
| **GitHub Copilot CLI** | **~158.1 MB** | **16.0× more RAM** |
| **Cursor Agent** | **~157.5 MB** | **15.9× more RAM** |
| **Claude Code** | **~212.7 MB** | **21.5× more RAM** |
| **Antigravity CLI** | **~86.4 MB** | **8.7× more RAM** |

</div>
versions tested for this corrected memory rerun:

- `jcode v0.9.1888-dev (be386f2)`
- `pi 0.62.0`
- `codex-cli 0.120.0`
- `opencode 1.0.203`
- `GitHub Copilot CLI 1.0.24` for the 1-session rerun, `GitHub Copilot CLI 1.0.27` for the 10-session rerun
- `Cursor Agent 2026.04.08-a41fba1`
- `Claude Code 2.1.86 (Claude Code)`
- `Antigravity CLI 1.0.0`

<div align="center">

  <a href="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-performance-demo.mp4">
    <img src="https://github.com/1jehuang/jcode/releases/download/readme-assets/jcode-performance-demo.webp" alt="jcode performance demonstration" width="900">
  </a>

  <p><em>jcode performance demonstration</em></p>

</div>


---

## Memory (Agent memory)

Jcode embeds each turn/response as a semantic vector. Every turn does queries a graph of memories to efficiently find related memory entries via a cosine similarity check. The embedding hits are fed into the conversation

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 effiecent harness Tiene 12.2k estrellas en GitHub y se actualizó por última vez today.

¿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 100/100 (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 de today, con 137 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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