Run AI agents from any framework through one OS. Forge AI designs teams automatically — describe your goal, it picks models, topology, and budget. 13 execution topologies with formal semantics. Judge pipeline evaluates every output. 24-tab dashboard for monitoring. Cost-quality-latency routing across 15 providers. SLM-Lite local memory. 25 MCP tools. 2,936 tests. Backed by a 20-page peer-reviewed paper : https://arxiv.org/abs/2604.06392 Install: npx qualixar-os
Tomorrow I'm launching Qualixar OS a runtime that handles the 80% of multi-agent AI work that isn't building agents: routing, quality control, cost tracking, memory, team design.
Hey Product Hunt -- Varun here.
I've been building multi-agent AI systems and kept running into the same problem: every project required rebuilding orchestration from scratch. Different frameworks, incompatible state management, no unified way to control cost or quality.
Agents need what programs got in the 1970s -- an operating system that handles scheduling, memory, routing, and failure recovery.
That's Qualixar OS. I also published a 20-page paper formalizing the topology semantics because I believe agent infrastructure deserves the same rigor we apply to databases and compilers.
The thing I'm most proud of is the quality process: before shipping v2.2.0, I ran 7 independent AI auditors against the codebase with zero shared context. They found 76 real issues. I fixed every single one. That's the bar I want to set.
The honest truth: I lost all the code in March to a catastrophic data loss. Rebuilt it from architecture docs. The second version came out cleaner.
What I'd love from you:
- Try `npx qualixar-os` and tell me what breaks
- Which topology would you reach for first?
- What's the one feature that would make you use this daily?
Paper: https://arxiv.org/abs/2604.06392
GitHub: https://github.com/qualixar/qual...
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