Frame the target
Capture requirements, acceptance checks, boundaries, and the files that matter before an agent starts editing.
Local AI development instrument
Vector Studio
A sharper desktop cockpit for turning specifications into code: plan the work, let agents operate in your repo, then verify every change before it ships.
- Execution
- Files, terminal, git
- Control
- Your keys, your machine
- Record
- Specs become history
The working loop
Not a chat box. A production lane.
Vector Studio treats AI work like engineering work: scoped input, observable execution, concrete verification, and a record you can come back to.
Let it touch the repo
Agents read the codebase, update files, run commands, and adjust based on real terminal output.
Watch the evidence
Review diffs, command results, build output, and remaining risks with the work still fresh.
Close the loop
Move from plan to patch to release with the original intent still attached to the change.
The surface
Four panels that feel like a studio bench.
Every surface keeps intent, context, execution, and judgment near each other, so agent work stays inspectable instead of drifting into a transcript.
Specifications stay visible while the implementation moves.
Use the spec as the source of truth instead of scattering intent across prompts and follow-up corrections.
acceptance:
- no hidden data leaves the machine
- tests pass before review
- diff explains itself
Project shape first.
Package metadata, git state, files, and run targets are gathered before the agent reaches for changes.
Terminal output is part of the conversation.
$ npm test
18 files checked
1 failing path repaired
Diffs, risks, and next actions land in one place.
Vector Studio keeps the work grounded in code changes and observable results, not vibes.
Model choice
Bring the provider that fits the task.
Vector Studio is designed around your configured API keys and your local project. The AI provider can change without changing the operating loop.
One studio, multiple engines.
Use the model family you trust for planning, implementation, or review, while keeping repository control in the desktop app.
Claude
Long-form reasoning and code execution loops
OpenAI
General coding, review, and tool-driven workflows
Gemini
Alternative model route for project analysis
MCP
Connect databases, APIs, and local tools
Git
Read history and reason over diffs
Terminal
Run the commands your project already uses
Desktop build
Install it where your repos already live.
No hosted workspace required. Open a folder, configure an AI provider, and work from the same machine that runs your tests.