Works in Claude Code and Codex
Built on Claude Code, with a ready-made plugin. OpenAI Codex and any other MCP client connect to the same tools, and there's an OpenAI-compatible endpoint for everything else.
The engineering platform for the AI era
The cockpit for the modern engineer.
HybridYour local GPUs + Claude Code or OpenAI Codex
Creating code got easy. Understanding it didn't.
Claude Code and Codex write code faster than anyone can read it. You prompt, wait for the run to finish, and get back a pile of changes to review, a review that is too often rushed or skipped. A pilot doesn't fly that way. They see every instrument while they fly and correct course the moment something drifts. Kavent gives engineers that cockpit: see the work as it happens, stop it on the spot, and direct it to the solution you meant.
One live view of every agent, every file and every decision across all your repos. Nothing happens off your radar.
See what every run, engineer and model costs, set daily budgets, and let the hybrid router keep the bill to a minimum.
Free checks, then a review of every diff and a confidence score that tells you exactly what to read.
Context is built before you ask, so agents start working instead of reading. Features ship in minutes.
Hybrid by design
Keep the leading coding agent you already trust. Kavent adds the GPUs you already own and gives every piece of work to the side that does it best, so frontier models are paid only where they make a difference.
Frontier models where quality depends on them:
Routes every step, watches the budget, scores the quality
The everyday work, on hardware you already own:
Typical split. Work on local GPUs never leaves your network and costs only electricity.
See it fly
Maya maintains Lunchbox, a small todo app with a FastAPI backend and a React frontend. She asks for due dates and red overdue todos. These are screen recordings of the real Kavent dashboard during that run; long stretches are sped up and marked on screen.
The cockpit
Built on Claude Code, with a ready-made plugin. OpenAI Codex and any other MCP client connect to the same tools, and there's an OpenAI-compatible endpoint for everything else.
Your repositories are read and summarized ahead of time into a context graph. Agents start from a short brief that cites path::symbol and line numbers, not from a blind re-read.
Kavent maps how your repos connect through APIs, packages, infrastructure and shared config. A change that needs the backend and the frontend is planned as one, with a contract between them.
Search by meaning, by exact words or by symbol name, fused into one ranking. It also covers the data models and data flows extracted from each repo.
Each step goes to the cheapest model that will get it right: a local GPU first, Claude Haiku for medium work, Claude Sonnet only for the hard parts. If a step fails, it moves up a tier on its own.
Linux GPU servers and opt-in engineer Macs join a private pool. A Mac only takes work when it's idle, on mains power and not in a call. Setup picks the best model for each machine's GPU.
The local model makes the change, free checks run (lint, parse), and Claude reviews only the diff. You get reviewed diffs in Python, JS/TS, Java, Go and C#. Nothing is applied without you.
Every run is a live tree of the files it touches, with state, line counts, confidence and diff. Stop, resume, exclude a file, or type a correction. The Kaven Agent re-plans only what changed.
Every change gets a confidence score. Changes Kavent is sure of collapse out of the way, and the ones it doubts are flagged for a human, so your attention goes where it's needed.
Failed checks and rejected reviews become short lessons for later runs, and similar past changes are reused as examples. It improves every week without retraining.
Every finished run is summarized and searchable. Before starting, agents check whether the question was already answered, and every answer traces back to the context it used.
See what the work cost against what one model would have charged, how many tokens stayed on your own hardware, and what each engineer shipped.
Under the hood
Why “Kavent”
The agents write the code. You steer it the right way. Steering works only when you can see everything, so Kavent gives you full observability: every model and machine doing the work, every repo and how they connect, every file a run touches, as it happens.
You don't need to read every line. When something drifts from what you meant, you see it at once: a low confidence score, a failed review, a file that shouldn't change. You correct it in plain words, and the run goes the way you see it.
That's the name. Kaven (כַּוֵּן) is Hebrew for aim, direct, fine-tune, from the same root as kivun, direction. Kaven + agent = Kavent: the agent you direct.
Your code stays yours
Kavent deploys with Docker Compose on your own server. Work on local GPUs never leaves your network.
Kavent is in early access for engineering teams that already ship with Claude Code or Codex.