jcode - open-source AI coding agent for the terminal
jcode<br>An open source terminal coding agent, written in Rust.<br>curl -fsSL https://jcode.sh/install | bashcopy<br>macOSLinuxWindows<br>All builds · Docs · Pricing · GitHub
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Mission<br>Jcode is built on three bets.<br>Parallelism is the biggest lever on coding productivity. You should never wait while coding. Every moment an agent is working is a moment you could be handing out the next task: spin up another session instead of watching this one finish, and your throughput becomes as many tasks as you can give out. Jcode makes it possible to run dozens of agents in parallel, and a dozen agents is a dozen times the output of one.<br>The harness matters as much as the model. The same model produces very different results depending on what surrounds it: the tools it can reach, the context it sees, the memory it keeps, and the feedback loops that catch its mistakes. Model progress arrives on its own schedule. Harness progress is ours to make, and it compounds with every model release.<br>Dev tools must be open source. A coding agent reads your code, edits your files, and runs commands on your machine. A tool with that much reach has to be inspectable, and it has to be modifiable: the best dev tools have always been the ones their users could open up and change, and your agent's source should be yours to customize. Jcode is MIT licensed, and everything we measure and learn is published.
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Resource efficiency<br>The bottleneck to massive parallelism is resource efficiency. Spawn dozens of agents and RAM consumption balloons, unfeasible on most consumer machines: when each agent takes hundreds of megabytes, you run a handful and queue your work behind them. Jcode fixed that. We optimize to the bone so spawning another agent is a non-decision and running dozens of sessions is actually possible, with none of it traded for speed. Every number below is sampled from real launches of real agents on the same machine.
Memory per additional session<br>Extra proportional memory (PSS) each additional client adds once one is already running. Ten jcode sessions cost about 100 MB, less than half of one Claude Code.<br>ToolExtra PSSGraphComparisonjcode~10.4 MB~10.4 MBbaselineClaude Code~212.7 MB~212.7 MB20.5× more RAMShow the full comparisonHide the full comparisonMemory per additional session, all tools<br>ToolExtra PSSGraphComparisonjcode (local embedding off)~9.9 MB~9.9 MBbaselinejcode~10.4 MB~10.4 MB1.1× more RAMCodex CLI~21.6 MB~21.6 MB2.2× more RAMpi~76.5 MB~76.5 MB7.7× more RAMAntigravity CLI~86.4 MB~86.4 MB8.7× more RAMCursor Agent~157.5 MB~157.5 MB15.9× more RAMGitHub Copilot CLI~158.1 MB~158.1 MB16.0× more RAMClaude Code~212.7 MB~212.7 MB21.5× more RAMOpenCode~318.4 MB~318.4 MB32.2× more RAMTime to first input<br>How long until you can actually type: time until typed probe text appears on the rendered screen, 10 interactive PTY launches each. Antigravity uses its internal input-ready log marker because its sign-in screen suppresses probe echo.<br>ToolTimeGraphComparisonjcode48.7 ms48.7 msbaselineAntigravity CLI383.7 ms383.7 ms7.9× slowerpi596.4 ms596.4 ms12.2× slowerCodex CLI905.8 ms905.8 ms18.6× slowerOpenCode1047.9 ms1047.9 ms21.5× slowerGitHub Copilot CLI1583.4 ms1583.4 ms32.5× slowerCursor Agent1978.7 ms1978.7 ms40.6× slowerClaude Code3512.8 ms3512.8 ms72.2× slowerTime to first frame<br>How long until anything renders, same 10 interactive PTY launches.<br>ToolTimeGraphComparisonjcode14.0 ms14.0 msbaselineAntigravity CLI383.5 ms383.5 ms27.4× slowerpi590.7 ms590.7 ms42.2× slowerCodex CLI882.8 ms882.8 ms63.1× slowerOpenCode1035.9 ms1035.9 ms74.0× slowerGitHub Copilot CLI1518.6 ms1518.6 ms108.5× slowerCursor Agent1949.7 ms1949.7 ms139.3× slowerClaude Code3436.9 ms3436.9 ms245.5× slowerWatch the performance demojcode performance demonstrationWatch the swarm session20 agents in parallel
Intelligence<br>optimization task<br>float-print score over time, jcode vs Claude Code, Opus 4.8 high thinkingRunning the standard evals against these harnesses showed us how poorly they represent what agents can actually do. Existing benchmarks face a forced choice. Public benchmarks leak their solutions into training corpora, so scores measure memorization and misrepresent capability. Private benchmarks resist that, but they demand trust in a hidden test set, offer less transparency, and are hard to get access to. Beyond that choice, they share two structural flaws. They impose timeouts, so an agent is penalized for allocating more time to a problem, exactly the long-horizon behavior we want agents to develop. And they score on a coarse, discrete boundary: the gap between two very different models can show up as a single task completion. A model that solves 70% of a bench may sit next to a remaining 30% that demands a far higher capability level, and that entire delta is invisible between the discrete tasks....