jcode

A blazing-fast, fully autonomous AI coding agent with a gorgeous TUI, multi-model support, swarm coordination, persistent memory, and 30+ built-in tools - all running natively in your terminal.

Features · Install · Quick Start · Further Reading

# macOS & Linux
curl -fsSL https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.sh | bash

Need Windows, Homebrew, source builds, provider setup, or tell your agent to set it up for you? Jump to detailed installation.

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 two metrics to show the difference: RAM usage and startup time.

• jcode uses 7.2× less PSS than pi
• jcode uses 22× less PSS than OpenCode
• jcode uses 25× less PSS than Claude Code
• jcode spawns 44.4× faster than pi
• jcode spawns 66.8× faster than OpenCode
• jcode spawns 24.3× faster than Claude Code

Measured on this Linux machine using real interactive PTY sessions and Linux /proc memory stats. For jcode, the number includes both client memory and the incremental memory growth of the shared server, which is the fair comparison for many active sessions.

Versions tested:

• jcode v0.8.16-dev (161f9fa)
• pi-coding-agent 0.62.0 (pi)
• opencode 1.0.203
• Claude Code 2.1.86

Measured as median time to first terminal output across 10 PTY launches on this Linux machine.

Jcode embedds 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 a memory sideagent which verifies the memories are relevant 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 relying on heavy use of subagents.

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 stalenss, etc

The side panel can render linked markdown, diagrams, and generated visual output directly inside the terminal workflow.

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

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.

The above is a video of me manually managing a swarm of 20 agents. Some working on the jcode codebase, and some not. This is only possible with the performance/resource efficiency noted in the above section on client/session memory usage.

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.

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.

• Claude (jcode login --provider claude)
• OpenAI / ChatGPT / Codex (jcode login --provider openai)
• Google Gemini (jcode login --provider gemini)
• Google / Gmail tools (jcode login --provider google)
• 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)
• MiniMax (jcode login --provider minimax)

• Native / first-party style providers: jcode, claude, openai, copilot, gemini, azure, alibaba-coding-plan
• Aggregator / compatibility providers: openrouter, openai-compatible
• Additional provider integrations: opencode, opencode-go, zai / kimi, 302ai, baseten, cortecs, deepseek, firmware, huggingface, moonshotai, nebius, scaleway, stackit, groq, mistral, perplexity, togetherai, deepinfra, fireworks, minimax, xai, lmstudio, ollama, chutes, cerebras, cursor, antigravity, google

• OAuth flows use PKCE + state validation.
• Secret files are written with owner-only permissions when supported.
• For auth managed by other tools (for example ~/.codex/auth.json or ~/.claude/.credentials.json), jcode asks before reading them, binds that approval to the external file path, rejects symlinked auth files, and does not rewrite or chmod the original external auth file.
• OpenAI OAuth uses http://localhost:1455/auth/callback by default, with a manual paste fallback if the callback port is unavailable.
• You can verify currently configured providers with jcode auth-test --all-configured.

For more auth details, see OAUTH.md.

jcode is the most customizable coding agent. Why? Because you can just modify the source code directly. Tell your jcode agent to enter self dev mode, and it will start modifying its own source code. Jcode is optimized to iterate on itself. There is significant infrastrucutre around self developement, including being able to make changes, test, build, and hot reload, fully automously without breaking your flow.

A native iOS implementation of OpenClaw is coming soon.

# Launch the TUI
jcode

# Run a single command non-interactively
jcode run "say hello"

# Resume a previous session by memorable name
jcode --resume fox

# Run as a persistent background server, then attach more clients
jcode serve
jcode connect

# Send voice input from your configured STT command
jcode dictate

jcode supports interactive TUI use, non-interactive runs, persistent server/client workflows, and hotkey-friendly dictation without requiring a bundled speech-to-text stack.

• Ambient Mode / OpenClaw
• Memory Architecture
• Swarm Architecture
• Server Architecture
• iOS Client Notes
• Safety System
• Windows Notes
• Wrappers and Shell Integration
• Refactoring Notes

If you want another agent to set up jcode for you, give it this prompt:

Set up jcode on this machine for me.

1. Detect the operating system, available package managers, and shell environment, then install jcode using the best matching command below instead of referring me somewhere else:

   - macOS with Homebrew available:
     brew tap 1jehuang/jcode
     brew install jcode

   - macOS or Linux via install script:
     curl -fsSL https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.sh | bash

   - Windows PowerShell:
     irm https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.ps1 | iex

   - From source if the above paths are not appropriate:
     git clone https://github.com/1jehuang/jcode.git
     cd jcode
     cargo build --release
     scripts/install_release.sh

   - For local self-dev / refactor work on Linux x86_64, prefer:
     scripts/dev_cargo.sh build --release -p jcode --bin jcode
     scripts/dev_cargo.sh --print-setup
     scripts/install_release.sh

2. Verify that `jcode` is on my `PATH`.
3. Launch `jcode` once in a new terminal window/session to confirm it starts successfully.
4. Before attempting any interactive login flow, assess which providers are already available non-interactively and prefer those first. Check existing local credentials, config files, CLI sessions, and environment variables such as:
   - Claude: `~/.jcode/auth.json`, `~/.claude/.credentials.json`, `~/.local/share/opencode/auth.json`, `ANTHROPIC_API_KEY`
   - OpenAI: `~/.jcode/openai-auth.json`, `~/.codex/auth.json`, `OPENAI_API_KEY`
   - Gemini: `~/.jcode/gemini_oauth.json`, `~/.gemini/oauth_creds.json`
   - GitHub Copilot: existing auth under `~/.config/github-copilot/`
   - Azure OpenAI: `~/.config/jcode/azure-openai.env`, `AZURE_OPENAI_*`, or an existing `az login`
   - OpenRouter: `OPENROUTER_API_KEY`
   - Fireworks: `~/.config/jcode/fireworks.env`, `FIREWORKS_API_KEY`
   - MiniMax: `~/.config/jcode/minimax.env`, `MINIMAX_API_KEY`
   - Alibaba Cloud Coding Plan: existing jcode config/env if present
5. Prefer whichever provider is already configured and verify it with `jcode auth-test --all-configured` or a provider-specific auth test when appropriate.
6. Only if no usable provider is already configured, guide me through the minimal manual step needed:
   - Claude: `jcode login --provider claude`
   - GitHub Copilot: `jcode login --provider copilot`
   - OpenAI: `jcode login --provider openai`
   - Gemini: `jcode login --provider gemini`
   - Azure OpenAI: `jcode login --provider azure`
   - Fireworks: `jcode login --provider fireworks`
   - MiniMax: `jcode login --provider minimax`
   - Alibaba Cloud Coding Plan: `jcode login --provider alibaba-coding-plan`
   - OpenRouter: help me set `OPENROUTER_API_KEY`
   - Anthropic direct API: help me set `ANTHROPIC_API_KEY`
7. After setup, run a simple smoke test with `jcode run "say hello"` and confirm it works.
8. Explain any manual step that still needs me, especially browser OAuth, device login, or API key entry.

This is intended to be a copy-paste bootstrap prompt for jcode itself or any other coding agent.

# macOS & Linux
curl -fsSL https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.sh | bash

# Windows (PowerShell)
irm https://raw.githubusercontent.com/1jehuang/jcode/master/scripts/install.ps1 | iex

brew tap 1jehuang/jcode
brew install jcode

git clone https://github.com/1jehuang/jcode.git
cd jcode
cargo build --release

For local self-dev / refactor work on Linux x86_64, prefer:

scripts/dev_cargo.sh build --release -p jcode --bin jcode
scripts/dev_cargo.sh --print-setup

That wrapper automatically uses sccache when available, prefers a fast working local linker setup (clang + lld) instead of assuming every machine's mold configuration is valid, and can print the active linker/cache setup via --print-setup so slow-path builds are easier to diagnose.

Then symlink to your PATH:

scripts/install_release.sh