Context resets every session
Most agents keep asking the same project questions because decisions, fixes, and conventions are not carried forward.
Terminal-native AI coding agent
AI coding work should compound inside your repo.
CyxCode helps you inspect code, plan changes, edit files, run commands, and remember what matters. Solved failures become reusable workflow instead of repeated model spend.
curl -fsSL https://raw.githubusercontent.com/code3hr/cyxcode/HEAD/install | bash Works with
TerminalDesktopIDEGitHubLocal modelsProvider keys
See it in action
CyxCode working through a real session.
The problem
Coding agents are powerful, but real projects expose the gaps.
The work is not just generating code. It is remembering decisions, recognizing repeated failures, using the right tools, and making automation visible enough to trust.
Failures burn model turns
Known errors still get sent through expensive reasoning loops instead of being recognized locally with a reviewed fix.
Automation gets hard to trust
When edits, commands, and permissions are not visible, teams slow the agent down to keep the repo safe.
The CyxCode approach
Make solved work reusable.
136+ built-in recovery patterns
75+ supported model providers
4 ways to work: terminal, desktop, IDE, GitHub
Workflow
Inspect, edit, run, remember, repeat.
CyxCode stays close to your terminal and repository, with memory, permissions, and recovery patterns designed for the loop engineers already use every day.
136
patterns active
tokens saved
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Capabilities
A practical agent for codebases that do not fit in one prompt.
Works inside your repo
Ask questions, edit files, run commands, and keep context close to the work.
Learns the project
Memory and recall preserve decisions, fixes, and conventions across sessions.
Fast on repeated failures
Known errors can be diagnosed before a model call, with fixes ready in under a second.
Choose your model
Use CyxCode Zen, your own provider keys, local models, Copilot, or ChatGPT accounts.
Guarded automation
Permissions, CyxWatch, and audit trails keep agent actions visible and controlled.
Improves over time
Corrections, learned patterns, and behavior versioning make repeated work reusable.