~ / agents
##Agents
Need it to pick the next click or tap.
The model picks an action. Code runs it.
### Screen & device control 7
Drives native macOS apps through the Accessibility API. Jev chooses the next action and target token from a live snapshot, and stops if confidence drops too low.
Headless browser agent. Jev picks click / type / select / done from the page's elements; code owns budgets and stop gates. MCP server, CLI, or library.
Split-brain browser agent: Jev clicks, Codex thinks and verifies.
Browser agent with a dynamic, indexed action space. Jev picks an operation and an element in one request; a small LLM writes text only when the operation is
TYPE_TEXT. Zürich to London on Google Flights in 7.1 seconds.Android agent on a real phone via Mobilerun. Jev picks
OPEN_APP/TAP/TYPE_TEXTfrom an indexed snapshot. Text is copied from the goal, not generated.Local computer-use app for macOS where Jev is the default engine: it picks from locally detected controls and the app executes and validates each action.
Computer use for about $0.0002 a step: OCR the screen, Jev classifies the next action, code clicks. macOS.
### Domain agents 10
MuJoCo robot decision workbench: MiniCPM-2B or Jev-class APIs pick the next action, with physics previews.
Phone chat copilot for WeChat / QQ / X / Feishu: reads the screen, suggests candidate replies, one tap to fill. Read-only — no app hooks.
Camera-only autonomous drone in MuJoCo with Jev in the loop at 2.5 Hz.
Local-first social-media research agent for Instagram, TikTok, and LinkedIn: Jev picks each next operation (search, open a post or profile, read comments, finish), a local CLI executes it in your signed-in Chrome, and every observed result goes back to Jev. Source-cited reports, no account credentials leave the machine.
Experimental: one Jev buy/sell decision every Monad block (about 300 ms) on the Kuru MON-USDC book. Dry-run by default.
Jev navigates a Neo4j graph by classifying over each node's neighboring relationships: graph traversal as a chain of typed choices.
Open-source AI trading OS with a Jev decision gate in front of live entry orders: typed Choice plus an auditable decision timeline, fail-open, and exits that always bypass AI.
Prebuilt agents that run a System 1 model (Jev, Laya, or Cua-S1) as the brain across browser use, computer use, robotics, and games, with Claude Code or Codex scaffolding new ones. Publishes per-scenario timings: up to 6× faster and 25× cheaper than a chat model at the same score.
Home Assistant conversation agent. Jev maps a spoken command plus exposed entities onto built-in intents. Free-text intents fall back to another agent.
Jev plays Super Mario Bros. from structured emulator state: the classic RL demo with a decision model in the loop.
nothing matches. try fewer words, or clear a filter.