jcode vs pi: Which one is better for you?

jcode and pi are both open source coding agents that live in your terminal, read your files, edit code, and run commands on your behalf. That’s close to where the similarity ends.

They start from opposite design decisions about what belongs in the core tool versus what you should bolt on yourself, and that one choice explains most of the differences below.

This page walks through both, including jcode’s own published performance numbers against pi.

What is jcode?

jcode is written in Rust and installs as a single binary. It ships with a memory system, multi agent coordination, MCP support, browser automation, and a range of other features turned on from the first run, and it is tuned specifically for running many sessions at once without eating through RAM.

What is pi?

pi is written in TypeScript and installs as an npm package. Its core deliberately does very little: four tools (read, write, edit, bash) and a small system prompt. Everything else, from MCP to subagents to a permission system, is left for you to add through extensions, skills, or third party packages.

jcode vs pi quick comparison

	jcode	pi
Language	Rust	TypeScript
License	MIT	MIT
Install	Install script, Homebrew, or build from source	npm install -g (or another Node package manager)
Runtime dependency	None, ships as a standalone binary	Requires Node.js
Design approach	Full feature set on by default, tuned for many parallel sessions	Minimal four tool core, everything else added through extensions
Multi agent work	Built in swarm support; agents in the same repo coordinate automatically	Not built in; run separate instances yourself, for example through tmux
MCP support	Built in, including reading Claude Code’s own MCP config files	Not built in; add it through an extension
Browser automation	Built in, through Firefox Agent Bridge	Available through a community package, not the core
Memory	Built in semantic memory graph with automatic recall	Not built in; sessions are saved as JSONL files you can branch and replay, but nothing is recalled automatically
Background tasks	Built in; long running commands become trackable background tasks	Not part of the core by design
Plan mode	Built in /plan command	Not built in; write plans to a file or add an extension
Built in to dos	Built in /todos command	Not built in; the project points you toward a plain TODO.md file instead
Session resume from other tools	Reads sessions from Codex, Claude Code, OpenCode, and pi itself	Not designed for this
Self modification	Has a dedicated self dev mode for editing its own source	Not a built in feature

Two different starting points

pi’s own documentation is unusually direct about what it leaves out: no MCP, no subagents, no permission prompts, no plan mode, no built in to do list, no background bash. The reasoning is consistent throughout: those are all things a team might want built differently depending on their setup, so pi would rather give you the building blocks (extensions, skills, prompt templates, themes, and a package system) than pick one version of each and bake it in.

If you want subagents, you spawn more pi instances yourself, commonly through tmux, or you install a community package that does it for you.

jcode takes the opposite bet. Multi agent coordination, MCP, memory, browser control, background tasks, a plan mode, and a to do list are all there from the first launch, with no extension needed to turn them on.

The trade off is the one you’d expect: less of a blank slate to shape to your exact liking, but a lot less setup before you’re doing real work.

Neither approach is simply better. It depends on whether you’d rather start from a small core and add exactly what you need, or start with most things already switched on.

Memory and multi agent work

jcode’s memory system embeds each turn as a vector and automatically pulls related memories into later conversations, with an ambient process that periodically consolidates and cleans up what’s stored. pi has no equivalent built in.

What it does instead is store every session as a JSONL file with a tree structure, so you can branch a conversation at any earlier point and resume from there, or share full sessions with others. That’s a strong session model, but it’s replay and branching rather than automatic recall across sessions.

For running more than one agent on the same project, jcode’s swarm support lets several agents work in the same repo with the server keeping them informed when one edits a file another has read.

pi has no equivalent in its core; you’d run separate instances through tmux, write your own coordination layer, or reach for a community package such as oh-my-pi, which adds subagent support on top of pi.

Providers and models

Both tools connect to a wide set of model providers rather than locking you into one. jcode ships built in login flows for providers including Claude, OpenAI, Gemini, GitHub Copilot, Azure OpenAI, and several OpenAI compatible services, plus a provider add command for pointing it at almost any other endpoint, local models included.

pi covers similar ground through its own unified API: subscription login for Claude Pro/Max, ChatGPT Plus/Pro, GitHub Copilot, and Gemini, API keys for providers like Anthropic, OpenAI, Google, Azure, Amazon Bedrock, xAI, DeepSeek, Mistral, and Groq, and a models.json file for adding Ollama, LM Studio, vLLM, or any other API compatible endpoint. Custom OAuth flows or nonstandard APIs go through an extension, the same way any other added capability does in pi.

In practice, neither tool limits you to one lab’s models, and switching between a paid subscription and a local model is possible in both.

Installing and getting started

jcode installs with a single script and needs nothing else on your machine first:

# macOS & Linux
curl -fsSL https://jcode.sh/install | bash
# Windows 11 (PowerShell 5.1+)
irm https://jcode.sh/install.ps1 | iex

pi installs as a global npm package, so Node.js needs to already be on your machine:

npm install -g @mariozechner/pi-coding-agent

If you’re new to the terminal and don’t already have Node.js set up, that’s an extra step jcode doesn’t require.

Performance: jcode’s own benchmark numbers

These figures come from jcode’s own measurements, run on one Linux machine, comparing jcode v0.9.1888-dev against pi 0.62.0 among other tools. Treat them as the project’s own published numbers rather than an independent audit, and check current figures before treating any of this as fixed, since both tools update often.

Memory use at one active session

Tool	Memory (PSS)	Compared to jcode with memory off
jcode (local embedding off)	27.8 MB	baseline
jcode	167.1 MB	6.0× more
pi	144.4 MB	5.2× more

At a single session, pi actually uses somewhat less memory than jcode running with its memory system turned on. jcode’s leanest mode, with local embeddings switched off, is the lightest of the three.

Memory use at ten active sessions

Tool	Memory (PSS)	Compared to jcode with memory off
jcode (local embedding off)	117.0 MB	baseline
jcode	260.8 MB	2.2× more
pi	833.0 MB	7.1× more

Extra memory per additional session

Tool	Extra memory per session	Compared to jcode with memory off
jcode (local embedding off)	~9.9 MB	baseline
jcode	~10.4 MB	1.1× more
pi	~76.5 MB	7.7× more

This is where the gap widens. A single pi session is fairly light, but each additional session costs roughly seven times what an additional jcode session costs, so the difference grows quickly once you’re running several sessions side by side, which is the situation jcode is specifically tuned for.

Startup speed

Measurement	jcode	pi	Difference
Time to first frame	14.0 ms	590.7 ms	42.2× slower
Time to first input	48.7 ms	596.4 ms	12.2× slower

Both figures are averaged across ten interactive launches on the same machine. Some of this gap likely comes down to runtime: jcode starts as an already compiled Rust binary, while pi starts a Node process and its TypeScript layers on top, which typically takes longer to get to a usable screen.

Which one fits your workflow

If you want a tool that already does most of what you need, across many sessions, without assembling it yourself first, jcode is the more direct route. If you’d rather start from a small core and build your own setup piece by piece in TypeScript, or you want to embed the underlying agent runtime inside something you’re building yourself, pi’s package split (coding agent, agent core, and a unified model API as separate pieces) is built for exactly that.

Conclusion

jcode and pi solve the same basic problem with different amounts of scaffolding included by default. jcode gives you memory, multi agent coordination, MCP, browser control, and background tasks from the first run, and its own numbers show it staying lighter and starting faster as you add more sessions. pi gives you a small, well understood core and asks you to add capability through extensions, which suits anyone who wants to shape the tool exactly around their own workflow instead of adopting someone else’s defaults.