Security
Local runs with a full audit trail
In 4RGE IDE, your repo stays on your machine. Every file read, terminal command, and edit streams beside your editor — with a trace you can review before anything ships.
Desktop agents
4RGED IDE
One native editor for code, agents, terminals, and review. Describe what you want built — the agent reads your repo, does the work, and hands you a diff you can actually trust.
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# autoresearch
Autonomous experiment loop for architecture and training search.
## How it works
1. **program.md** defines the experiment scope and success criteria.
2. The agent reads in-scope files, branches off `master`, and initializes **results.tsv**.
3. Each iteration edits `train.py` (or related config), runs training under a fixed time budget, and logs `val_bpb` + memory to **results.tsv**.
4. Good runs get committed; bad runs are reverted or marked `crash` in the log.
## Quick start
```bash
uv run prepare.py
uv run train.py
```
See **program.md** for the full agent playbook and **results.tsv** for the experiment history.A world-class IDE
The power of an IDE,
exactly when you need it.
Read, trace, and debug every change your agents ship.
4RGE IDE includes a full editor with syntax highlighting, autocomplete, and debugging tools built in for you to stay in flow.
Trust and control
No trust without control
In 4RGE IDE, autonomy only scales when control comes first. The same window that runs agents also surfaces every tool call, diff, and approval boundary.
Governance
Policies and approvals you control
Set model access, tool scopes, and approval paths for your team. 4RGE agents operate inside boundaries you define — across IDE, CLI, cloud, and mobile.
Enablement
From pilot IDE to team-wide rollout
Start with local agents in the IDE, expand squad by squad, and hand off to cloud when ready. Run history keeps adoption visible without slowing delivery.
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Agent orchestration
Feature Integrations
Coordinate specialized agents to connect features across services while preserving context, quality, and delivery speed.
Research, architecture, and implementation agents run in parallel — with repo context and review gates intact from start to merge.