by fajarhide
A high-performance Semantic Signal Engine with Context OS for Agentic AI. Run your AI with zero noise, pure context, and 90% lower token costs.
# Add to your Claude Code skills
git clone https://github.com/fajarhide/omniLast scanned: 5/30/2026
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30 days in the Featured rail
The Context Operating System for AI Agents. Less noise. More signal. Cut token consumption by up to 90%.
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OMNI is a high-performance Semantic Signal Engine and Context Operating System that intelligently intercepts, analyzes, and distills terminal outputs before they reach your AI Agent. It acts as a transparent signal optimization layer that sits between the shell and the AI, ensuring every token sent to the model is high-value, relevant, and noise-free. By preventing your AI from getting confused by noisy output, you get accurate answers faster while saving massive amounts of token costs.
Fully transparent. You're always in control.
When you use autonomous AI agents (like Claude Code or Cursor) in your terminal, they read everything. A simple git diff, npm install, or cargo test command can easily dump 10,000 to 25,000 tokens of useless terminal noise into your AI's context.
This causes three huge problems:
I built Omni because I wanted to run AI agents efficiently and cheaply every single day in my own workflow.
Omni acts as the perfect filter between your terminal and your AI.
The result? You can run your AI agent on a super-advanced framework and feed it zero noise. Because the AI is only fed highly focused, straight-to-the-point context, even affordable or ordinary models will perform on-par with expensive flagship models, since they are never distracted by junk data.
My ultimate passion isn't to monetize this—it's to build the ultimate open-source toolbelt for the Agentic AI era. By aggressively saving token costs, I can develop software robustly and cost-effectively today, and you can too.
Context is expensive and noisy, and Omni is here to fix that. By optimizing context, Omni makes AI agents more efficient, cost-effective, and easier to use. This is done by reducing the amount of context that is sent to the AI agent, which in turn reduces the amount of processing time and memory required to generate a response.
OMNI wasn't built just to "cut context" or "save tokens"—those are simply the happy side effects. The true philosophy behind OMNI is Context Quality.
AI agents like Claude are only as smart as the context you feed them. When you flood them with megabytes of dependency logs or loading bars, you force them to sift through garbage to find the actual problem. This dilutes their reasoning and leads to degraded or unhelpful responses.
OMNI's goal is to feed your AI pure, highly-dense signal. This means only grabbing the context that is actually important and meaningful for Claude. We clean up the noise the AI doesn't need, which means:
Try it for a week. Feel the difference in the quality and speed of your AI's reasoning when it's fed on a diet of pure signal instead of raw terminal noise.
OMNI is designed to solve the daily frustrations of Agentic AI developers. Here is how it transforms your workflow:
The "Infinite Loop of Death" in Monorepos
npm install and npm run build in a large monorepo. It outputs 20,000 lines of dependency warnings and a small build error at the end. The AI gets distracted by the warnings and tries to fix unrelated dependency issues, burning through your tokens and trapping you in an infinite loop.peer dependency warnings and only surfaces the exact Build Error: Cannot find module 'X' alongside the stack trace. The AI sees a 50-token output and fixes the code instantly.The "Silent Hallucination" on Large Files
cat src/utils.ts. The file is 3,000 lines long. The AI struggles to keep all of it in working memory and starts hallucinating function signatures.cat and replaces it with a Structured Outline. It shows the AI the imports, the public API (function names and types), and risk markers, reducing the output by 80%. OMNI then warns the AI: "This file has 12 dependents — use omni_context for full impact map." The AI is guided to make safer, factual edits.Multi-Agent Collaboration
omni_agents and its local SQLite Store, Cursor and Claude share the same filtered memory streams, active errors, and execution environments. They collaborate without clashing.OMNI is built in Rust for zero-overhead execution and ruthless efficiency. Here are the actual benchmarks measured on the release binary:
| Command / Context | Input Size | Output Size | Token Savings | Impact on AI |
|-------------------|------------|-------------|---------------|--------------|
| docker build (multi-stage) | 9.2 KB | 49 bytes | 99.5% | Eliminates caching noise; AI instantly sees the real build error. |
| cargo test (large suite) | 16.5 KB | 4.3 KB | 78.0% | Strips hundreds of "ok" tests; AI focuses only on the failures and stack traces. |
| git status (dirty) | 496 bytes | 113 bytes | 77.2% | Removes clean files and hints; keeps only modified/untracked files. |
| kubectl get pods | 840 bytes | 762 bytes | 10.0% | Selectively surfaces CrashLoopBackOff/Error pods, skipping healthy ones. |
| git diff (multi-file) | 397 bytes | 220 bytes | 50.0% | Preserves hunks with changes, dropping excessive context lines. |
To see your own actual token savings, just run omni stats after a few days of usage.
RewindStore). The AI can automatically request it using omni_retrieve.