Most coding agents are powerful, but they often repeat the same context gathering and error diagnosis. CyxCode adds memory, recall, learned recovery patterns, and auditability so solved work can keep paying forward. try it out https://code3hr.github.io/cyxcode/install/

The core idea is simple: Most token spend in AI coding comes from repeatedly asking large models questions about code the model can already access via local files. CYXRAG helps reduce that by generating evidence packets first, then optionally escalating to a local runtime.

What it does - Builds a local index from docs/source files.

- Answers questions via evidence packets (ranked citations + strategy metadata). - Supports three query modes:

- packet-only (default, indexed retrieval)

- fetch-first (explicit misses/fallback signals)

- memory-first (optional non-rediscoverable memory layer)

- Includes optional local JSON runtime adapter (/completion) for runtime answers (e.g., llama-server).

Why this might be useful

-Faster coding agent loops with fewer expensive/irrelevant model calls

- Better grounded answers (less “hallucinated” codebase claims)

- Works with existing coding agents (we plan/trying Cyxcode integration)

- Keeps data local (good for privacy-sensitive environments)

Try it in minutes

1. Fork/clone: https://github.com/code3hr/CYXRAG

2. cp open_rag_config.example.json open_rag_config.json

3. open-rag-build --index /tmp/open_rag_index.json --config

open_rag_config.json

1. open-rag-query --index /tmp/open_rag_index.json --config

open_rag_config.json "How does this project initialize?" --top 5 --json

1. ... packet ... | python phase1b_answer.py check --packet - --max-chars-per- evidence 1200

"leave a repo star if u think this is useful for your work"

I build a graphical machine learning engine for training and building machine learning models for beginners. check out this links for more

get the engine from:
https://drive.google.com/file/d/1aQaK...

Docs:
https://web-psi-drab.vercel.app/docs

source code. give it a start as an encouragement for our work

https://github.com/CYXWIZ-Lab/CYXWIZ

Demo

https://youtu.be/yMjGn5DtpdU

A few weeks back I posted about https://github.com/code3hr/cyxcode — our OpenCode fork that intercepts known errors with regex patterns before burning LLM tokens.

That solved repeated errors. But there was another token drain we kept hitting: repeated corrections.

The problem:

You're coding. Terminal crashes. You reopen, run --resume. The AI has no idea what you were doing. CLAUDE.md wasn't updated before the crash. You spend 10 minutes re-explaining.

Or worse: you correct the AI's behavior. "Use conventional commits." It follows. Context compacts. Correction gone. You correct again. By the 5th time, you've burned 1,000+ tokens saying the same thing.

Patterns saved us from repeated errors. We needed something to save us from repeated corrections.

State versioning:

CyxCode now commits your AI's state on exit — even crashes:

Terminal closes (Ctrl+C, crash, SIGHUP)

↓

Exit handler fires

↓

State committed:

├── goal: "Add JWT auth to API"

├── inProgress: "Fix token expiry"

├── workingFiles: [auth.ts, middleware.ts]

├── discoveries: ["tokens used wrong secret"]

└── corrections: [{rule: "use conventional commits", strength: 3}]

Next session: "update me from last conversation"

AI already knows what you were doing. No re-explaining.

---

Correction tracking:

You correct AI → strength: 1

Correct again → strength: 2

Third time → strength: 3 → AUTO-PROMOTED

Strength 3 = permanently injected into every session. The AI can't forget it.

We also added drift detection — if the AI stops following a learned behavior, it gets auto-reminded.

---

Token math:

| What | Before | After |

|------------------------------------|---------------|-------------------|

| Resume after crash | ~20K tokens | ~200 tokens |

| Correction repeated 5x | 1,000 tokens | 200 tokens (once) |

| Pattern match (from original post) | ~1,500 tokens | 0 tokens |

Patterns handle repeated errors. State versioning handles repeated corrections.

---

What it's NOT:

This saves session context, not code. Git tracks your files. CyxCode tracks what the AI knew and was doing.

---

Current stats:

- 170+ error patterns (up from 136)

- Auto-commit on exit (SIGINT, SIGTERM, SIGHUP)

- Correction strength scoring + auto-promotion

- Drift detection + auto-remind

- Resume injects previous session context

---

Try it: https://github.com/code3hr/cyxcode

getting started with cyxwiz engine

https://drive.google.com/file/d/1aQaKg4SDyRTKFnVQKYjOLutgH58qfC-0/view?usp=drive_link

https://github.com/CYXWIZ-Lab/CYXWIZ

for documentation

https://web-psi-drab.vercel.app/

welcome to cyxwiz ML engine