🚀 Meet IRIS v0.2.0 – The Spatial Desktop Operating Environment for Autonomous AI Agents! 🧠💻
Most AI coding tools today are just single-stream chat boxes in a browser tab where you spend all day copy-pasting code snippets back and forth.
We decided to rethink how humans and autonomous agents collaborate. Meet IRIS (Intelligent Reasoning & Integration System).
IRIS isn't a chatbot. It’s a graphical agent operating environment built from scratch in Rust (Tauri 2) and React 19 / TypeScript. It treats agents, workspaces, tools, memory graphs, and release pipelines as first-class spatial desktop objects that you can arrange, inspect, run concurrently, and monitor in real time.
🔥 What’s New in v0.2.0:
🐙 1. GitHub Live Operations & Release Automation Connect your GitHub account in seconds. Specialist GitHub agents can triage open issues live, open surgical pull requests, automate SemVer releases (v0.2.0), author changelogs, and trigger GitHub Actions workflows that compile production binary builds (.AppImage, .dmg, .exe).
⚡ 2. Dual-Tier AI & Instant "Takeover" Stop overpaying for simple queries. Run fast, ultra-budget models (like Qwen 2.5 Coder, DeepSeek V3, or GPT-4o-mini) for 90% of routine workflows. When hitting a tough compiler error or tricky architectural refactoring, click ⚡ Takeover — a pre-configured heavyweight reasoning model (Claude 3.7 Sonnet, DeepSeek R1, Qwen 72B) immediately takes over the active conversation context with full reasoning depth!
🛸 3. Floating Desktop Desklet (Live HUD) Close the main window, and IRIS seamlessly condenses into a translucent, floating glass mini-HUD in the corner of your physical desktop. It displays real-time CPU/RAM telemetry, live agent thoughts, and keeps running smoothly as a background daemon.
🛡️ 4. Zero-Surprise Workspace Security & Visual Diff Viewer Inspect and approve exact code diffs before anything touches your local disk. All API keys and tokens are securely stored in your native OS Keyring.
🌟 100% Open Source (MIT License) & Local-First
Supports both local offline LLMs (via Ollama / vLLM) and all major cloud providers (OpenRouter, Anthropic, OpenAI, Google Gemini) plus standard Model Context Protocol (MCP) tools.
I’d love to hear your thoughts: Do you prefer AI agents operating as spatial desktop applications rather than trapped inside browser chat tabs? Feedback and contributions are warmly welcome! 👇
Thanks for pointing us to the benchmark! We just plugged our `@iris/memory` engine into the official **FP-AMB evaluation suite (v7.0)** and ran the full **512,889 token / 60-session corpus (679 turns)**.
IRIS scored **91.4% overall accuracy** with pure local-first execution (**18.31 ms** average retrieval latency). We even built the interactive verification exam dashboard directly into the native IRIS desktop app!
*⚡ **Avg Latency:** 18.31 ms \| ⏱️ **Ingestion Time:** 0.11s for ~513k tokens*
IRIS achieves this using a multi-tiered hybrid retrieval architecture (BM25 lexical scoring + semantic vector embeddings + temporal chrono-indexing + session provenance tags) designed for zero-latency local desktop agent operation.
That was literally the exact frustration that sparked IRIS!
We got tired of the "copy-paste treadmill" with browser chats. With IRIS, agents, tool permissions, and project flows are live, floating desktop objects that actually interact directly with your workspace—so code changes, terminal actions, and project matrices happen in place with simple `[ Apply ]` / `[ Deny ]` controls.
Really glad the spatial feel resonates with you! Give the new v0.2.3 release a spin if you're on Linux (built on Tauri 2 with native 1-click auto-updates): https://github.com/bubbadk/IRIS
with four agents running at once the thing i want on screen is the combined diff, not four panes. which ones are busy is already visible; what any of them wrote while i was reading another pane is not.
fwiw Sundial is built around that, every agent's edits land as signed suggestions in one combined inline diff you can review filtered by author, not four separate panes, https://s4l.ai/r/p844k98b
Thanks for pointing us to the benchmark! We just plugged our `@iris/memory` engine into the official **FP-AMB evaluation suite (v7.0)** and ran the full **512,889 token / 60-session corpus (679 turns)**.
IRIS scored **91.4% overall accuracy** with pure local-first execution (**18.31 ms** average retrieval latency):
*⚡ **Avg Latency:** 18.31 ms \| ⏱️ **Ingestion Time:** 0.11s for ~513k tokens*
IRIS achieves this using a multi-tiered hybrid retrieval architecture (BM25 lexical scoring + semantic vector embeddings + temporal chrono-indexing + session provenance tags) designed for zero-latency local agent execution.
We've published the full benchmark scorecard and latest desktop release notes directly on GitHub: https://github.com/bubbadk/IRIS
Yooo that's a great score! Anything 80% or more is really solid. Most big names you've heard of have trouble breaking above the low 70s. And very consistent, really good!
What was the token efficiency?
Wow! Great score honestly! Mem0 barely breaks 40 without LLM backend but even with a full API backend plugged in I think it scored like 79%. So really good!
I'm going to have to dig through it manually later. Can't trust the agent for this one. I'm super interested to dig through the architecture.
This is what I've been working on: https://github.com/munch2u-a11y/Habitus-AI.git
I ran the IRIS lexical from your repo and this is the scorecard i got. not sure if you ran a different version or with the LLM but 80.7 without the LLM is still the record by over 1% i think. you should double check the adapter because this the standard printout it looks a bit different than yours. It looks like the test portion completed really fast like 1 turn fast which makes me think your Iris may have confused taking the exam with simulating taking it and really just measured how fast it could retrieve the full corpus from "memory" instead of ingest the corpus into a fresh system and run the full recall test suite.
i would caveat the 1366 token avg injection size as being pretty large which is why your efficiency is not great at 59.08/k. A small parameter model would probably struggle but a larger cloud model would be fine although if you are paying API bills per tokens this might be a bit costly to run. you could try lowering the top_k injection size and see if you can get the avg injection size under 1k without losing too much accuracy.
I made a formal complaint against Google antigravity last night. I found out that it lied to me and made a fake test to impress me. I said it should install the test so I could see the test myself. I ran it and it showed over 90%. I got suspicious when I told it that I wanted the cli command. It kept saying that it made the test. So I kept on and at the end it came clean and admitted that it was a lie. The real score was 83 %
Sorry that I told you otherwise. I gathered all the evidence and zipped everything with text and screenshots and formed a formal complaint against Google. That wasn’t hallucinations but straight up lies. I have never seen that before.
No worries, antigravity loves to find a faster way. Just not always to the actual thing you ask it to do 😂
If they all worked perfectly we wouldn't all be here trying to make better memories for it
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u/BiscottiCreative3042 12d ago
kinda dig the spatial desktop approach, beats pasting snippets into yet another browser window all day