The Context Operating System for AI Agents. Less noise. More signal. Cut token consumption by up to 90%.

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.

The Problem: Expensive Tokens & Noisy Outputs
The Solution: Omni
The Philosophy
Real-World Use Cases
Performance & Benchmarks
Features Explained
Under the Hood: How Omni Works
Architecture
Quick Start & Installation
How to Use It
- Multi-Agent Support & Integrations
- Documentation Index
Works Even Better with Heimsense
Contributing & License

The Problem: Context Bloat, Expensive Tokens & Noisy Outputs

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:

It's extremely expensive: You pay real money for every single token of that junk output.
It makes the AI "dumb": Critical errors get buried under megabytes of warning logs and loading bars, confusing the AI and diluting its reasoning.
Model Lock-in: Advanced agent frameworks force you to use their most expensive flagship models just to have a context window big enough to handle all that noise.
Token-Aware Execution: Agents lack awareness of token costs and outputs, leading to unnecessary consumption.
Context Bloat: The volume of terminal output clutters the AI's context, reducing focus and accuracy.

The Solution: Omni

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.

The Philosophy

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:

Automatically, the tokens you use are drastically fewer.
The AI's response is of significantly higher quality because its context window is laser-focused on the real problem.

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.

Real-World Use Cases

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
- Scenario: You ask Claude to run 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.
- OMNI's Fix: OMNI intercepts the build. It completely mutes the hundreds of 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
- Scenario: The AI wants to understand a project and runs 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.
- OMNI's Fix: OMNI blocks the raw 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
- Scenario: You are using Cursor IDE for quick edits and Claude Code CLI for heavy lifting. They both need to know what's happening without running redundant commands and wasting tokens.
- OMNI's Fix: OMNI acts as a shared memory layer. Using 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.

Performance & Benchmarks

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. |

Pipeline Latency: < 100ms (end-to-end, including binary startup)
All-Time Savings: 97.3% token reduction across average development sessions.
ROI: $35+ USD saved per developer/month (measured against flagship models).

To see your own actual token savings, just run omni stats after a few days of usage.

Features Explained

Core Distillation Engine

No More AI Confusion: Omni acts like a smart sieve. If a test fails, it shows the AI only the specific error line and stack trace, blocking noisy dependency logs and loading spinners.
90% Token Reduction: By eliminating useless terminal noise, you drastically cut your agentic API bills instantly.
Adaptive Compression: OMNI tracks when agents retrieve omitted output. If a command family is frequently retrieved, OMNI automatically softens compression next time — self-tuning without configuration.
Smart High-Speed Bypass: To ensure zero latency for small tasks, OMNI automatically bypasses distillation for outputs under a 2000-token threshold.

Context Safety & Factual Guards

Zero Information Loss: Worried Omni filtered something important? Don't be. Omni saves the raw output locally (RewindStore). The AI can automatically request it using omni_retrieve.
Factual Anti-Hallucination Guards: OMNI emits warnings only when it has hard facts. If output is h

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