Installation
# macOS & Linux
curl -fsSL https://jcode.sh/install | bash
# 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.
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.
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
1 active session
| Tool |
PSS |
Comparison |
| jcode (local embedding off) |
27.8 MB |
baseline |
| jcode |
167.1 MB |
6.0× more RAM |
| pi |
144.4 MB |
5.2× more RAM |
| Codex CLI |
140.0 MB |
5.0× more RAM |
| OpenCode |
371.5 MB |
13.4× more RAM |
| GitHub Copilot CLI |
333.3 MB |
12.0× more RAM |
| Cursor Agent |
214.9 MB |
7.7× more RAM |
| Claude Code |
386.6 MB |
13.9× more RAM |
| Antigravity CLI |
243.7 MB |
8.8× more RAM |
|
|
10 active sessions
| Tool |
PSS |
Comparison |
| jcode (local embedding off) |
117.0 MB |
baseline |
| jcode |
260.8 MB |
2.2× more RAM |
| pi |
833.0 MB |
7.1× more RAM |
| Codex CLI |
334.8 MB |
2.9× more RAM |
| OpenCode |
3237.2 MB |
27.7× more RAM |
| GitHub Copilot CLI |
1756.5 MB |
15.0× more RAM |
| Cursor Agent |
1632.4 MB |
14.0× more RAM |
| Claude Code |
2300.6 MB |
19.7× more RAM |
| Antigravity CLI |
1021.2 MB |
8.7× more RAM |
|
Time to first frame
| 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 |
Measured on this Linux machine across 10 interactive PTY launches.
(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.)
| 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 |
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
| 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 |
versions tested for this corrected memory rerun:
jcode v0.9.1888-dev (be386f2)pi 0.62.0codex-cli 0.120.0opencode 1.0.203GitHub Copilot CLI 1.0.24 for the 1-session rerun, GitHub Copilot CLI 1.0.27 for the 10-session rerunCursor Agent 2026.04.08-a41fba1Claude Code 2.1.86 (Claude Code)Antigravity CLI 1.0.0
jcode performance demonstration
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, or optionally uses a memory sideagent which verifies the memories are relevant, and potentially does more work for information retreival before injecting into the conversation. This results in a human like memory system which allows the agent to automatically recall relevant information to the conversation without actively calling memory tools or being a token burner.
ot
To have memories which are retrieved, they must also be extracted and stored. Every so often (semantic drift, K turns since last extraction, session end, etc), memories are extracted via a memory sideagent, and put into the memory graph.
The harness also provides explicit memory tools to allow the agent to actively search or store the memory without relying on a passive background process. The harness also provides session search for traditional RAG on previous sessions.
Memories are automatically consolidated every so often via the ambient mode. This reorganizes, checks for staleness and conflicts, etc
jcode memory demonstration
The side panel is a place for auxiliary information. Tell your jcode agent to load a file into the side panel and see it update in real time, or tell your agent to write directly to the side panel, or use it as a diff viewer. The side panel (and chat) is able to render mermaid diagrams inline.
To make this possible, I created a new mermaid rendering library to render diagrams 1800x faster. It has no browser or Typescript dependency. See https://github.com/1jehuang/mermaid-rs-renderer
To show you important information without taking space away from the screen that could be used for responses, I developed info widgets. Info widgets will only ever take up the negative space on the screen to show you information, and will get out of the way if there isn't any.
Jcode can render at over a thousand fps. Your monitor will not have the refresh rate to show you, but this means you will not have silly flicker problems.
The custom scrollback implementation of jcode allows it to do much more than a native scrollback. However, it is a terminal-level limitation that I cannot have smooth, partial line scrolling with a custom scrollback. To fix this, I made my own terminal. Handterm https://github.com/1jehuang/handterm implements a native scroll api, and also happens to be very efficient. This is a work in progress. Scrolling is still well implemented for normal terminals.
Jcode is left-aligned by default. You can switch to centered mode with the Alt+C hotkey, with the /alignment command, or in the config.
To disable emoji globally in TUI and CLI output, set emoji = false under [display] in ~/.jcode/config.toml, or launch with JCODE_NO_EMOJI=1. Jcode replaces emoji with compact ASCII markers while preserving other Unicode text.
Swarm
Spawn two or more agents in the same repo, and they will automatically be managed by the server to allow native collaboration. When agent A edits a file that agent B has read (code shifting under its feet), the server notifies agent B. Agent B can ignore it if it is not relevant, or it can check the diff to make sure that it doesn't conflict. Each agent has messaging abilities, capable of DMing just one agent, broadcasting to all other agents hosted by the server, or just agents working in that repo. This allows you to spawn multiple sessions in the same repo, and have all conflicts automatically resolved.
jcode swarm demonstration
Agents are also able to spawn their own swarms autonomously. They have a swarm tool which allows them to spawn in their own teamates to accomplish tasks in parallel. Doing so turns the main agent into a coordinator and the spawned agents into workers. Groups of agents, their messaging channels, their completion statuses, etc are all automatically managed. This can be done headlessly or headed.
OAuth and Providers
jcode works with subscription-backed OAuth flows and many provider integrations, so you can use the models you already pay for and still fall back to direct API providers when needed.
Supported built-in login flows
- Claude (
jcode login --provider claude)
- OpenAI / ChatGPT / Codex (
jcode login --provider openai)
- Google Gemini (
jcode login --provider gemini)
- GitHub Copilot (
jcode login --provider copilot)
- Azure OpenAI (
jcode login --provider azure)
- Alibaba Cloud Coding Plan (
jcode login --provider alibaba-coding-plan)
- Fireworks (
jcode login --provider fireworks)
- Novita AI (
jcode login --provider novita, API key)
- MiniMax (
jcode login --provider minimax)
- Meta Model API / Muse (
jcode login --provider meta-muse)
- LM Studio (
jcode login --provider lmstudio)
- Ollama (
jcode login --provider ollama)
- Custom OpenAI-compatible endpoint (
jcode login --provider openai-compatible)
For custom OpenAI-compatible endpoints, jcode now prompts for the API base and supports local localhost servers without requiring an API key.
The native OpenAI providers use Responses WebSocket v2 with opportunistic
background prewarming and HTTPS fallback. See OpenAI WebSocket transport
for behavior, controls, and verification.
Config-file setup for self-hosted endpoints and MCP
If you prefer to configure things by editing files instead of using the login UI, jcode supports both a custom OpenAI-compatible endpoint config and MCP config files.
OpenAI-compatible providers
Many hosted services speak the standard OpenAI /v1/chat/completions API. jcode talks to them through one shared OpenAI-compatible provider, so you can use almost any such endpoint without waiting for a dedicated integration.
There are two ways to set one up:
Built-in named profiles — jcode ships ready-made profiles for several popular OpenAI-compatible services. Log in by id and jcode fills in the base URL and key environment variable for you:
jcode login --provider <profile-id>
# for example:
jcode login --provider openrouter
jcode login --provider orcarouter
jcode login --provider deepseek
jcode login --provider opencode # OpenCode Zen
jcode login --provider moonshotai
jcode login --provider meta-muse # Meta Model API / Muse Spark
Built-in OpenAI-compatible profile ids include: openrouter, orcarouter, deepseek, zai, kimi, moonshotai, meta-muse (Meta Model API / Muse Spark), opencode (OpenCode Zen), opencode-go, 302ai, baseten, cortecs, huggingface, nebius, scaleway, stackit, and firmware. Each profile only sets the endpoint and key variable; you still pick the model with /model (or --model). Run jcode login with no provider to see the interactive list.
Any other endpoint — point jcode at an arbitrary OpenAI-compatible API (hosted or local) with jcode login --provider openai-compatible or the scriptable jcode provider add command described below.
Useful environment overrides for these endpoints:
JCODE_STREAM_IDLE_TIMEOUT_SECS — raise the base streaming idle timeout (default 180s) for slow reasoning models that think silently before emitting tokens. High reasoning efforts scale this automatically (high 2x, xhigh 3x, max 4x). Also settable as [provider] stream_idle_timeout_secs in config.toml.- Per-model
context_window (alias context_limit) in a [[providers.<name>.models]] entry — set the context window when the endpoint has no usable /v1/models response, so jcode does not fall back to the generic 200k default.
extra_body — inject non-standard top-level fields into every chat/completions request body for backends that require them. See Extra request-body fields below.
For details on self-hosting,
jcode is a high-performance coding agent harness written in Rust — a terminal-based AI coding agent that connects to LLM providers like Claude, OpenAI and Gemini and drives coding work from your TUI.