Qualixar OS: A Universal Operating System for AI Agent Orchestration
Abstract
Qualixar OS enables universal AI agent orchestration through a comprehensive runtime environment supporting diverse LLM providers, agent frameworks, and communication protocols, featuring advanced multi-agent topologies, adaptive routing, and robust validation mechanisms.
We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or single-framework tools (AutoGen, CrewAI), Qualixar OS provides a complete runtime for heterogeneous multi-agent systems spanning 10 LLM providers, 8+ agent frameworks, and 7 transports. We contribute: (1) execution semantics for 12 multi-agent topologies including grid, forest, mesh, and maker patterns; (2) Forge, an LLM-driven team design engine with historical strategy memory; (3) three-layer model routing combining Q-learning, five strategies, and Bayesian POMDP with dynamic multi-provider discovery; (4) a consensus-based judge pipeline with Goodhart detection, JSD drift monitoring, and alignment trilemma navigation; (5) four-layer content attribution with HMAC signing and steganographic watermarks; (6) universal compatibility via the Claw Bridge supporting MCP and A2A protocols with a 25-command Universal Command Protocol; (7) a 24-tab production dashboard with visual workflow builder and skill marketplace. Qualixar OS is validated by 2,821 test cases across 217 event types and 8 quality modules. On a custom 20-task evaluation suite, the system achieves 100% accuracy at a mean cost of $0.000039 per task. Source-available under the Elastic License 2.0.
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We introduce Qualixar OS, the first operating system purpose-built for AI agent orchestration. Instead of building routing, quality control, cost tracking, and memory from scratch for every multi-agent project, Qualixar OS provides a unified runtime with:
- 12 formally-specified execution topologies (sequential, parallel, hierarchical, DAG, debate, mesh, star, grid, forest, circular, mixture-of-agents, maker)
- Forge AI: POMDP-based automatic team design from natural language
- Cost-quality-latency routing across 15+ model providers
- Judge pipeline for consensus-based output evaluation
- SLM-Lite cognitive memory (local-first, SQLite-backed)
- 24-tab interactive dashboard
- Claw Bridge for cross-framework agent import (LangGraph, CrewAI, AutoGen, DeerFlow)
2,831 tests. 49 DB tables. 25 MCP tools. 7 communication channels. Elastic License 2.0.
Paper: 20 pages, 7 figures with formal topology semantics.
We're making the GitHub repo and qualixar.com public next week. The system is fully implemented (2,831 tests, 49 DB tables, 25 MCP tools) — we're just finishing the documentation portal before open-sourcing.
In the meantime, the paper covers the full architecture:
- 12 formally-specified execution topologies (Section 5)
- Forge AI: POMDP-based automatic team design (Section 4)
- Three-layer model routing with dynamic discovery (Section 6)
- Consensus-based judge pipeline with Goodhart detection (Section 7)
- SLM-Lite cognitive memory — local-first, SQLite-backed (Section 8)
If you're building multi-agent systems and want early access before the public launch, reach out: varun.pratap.bhardwaj@gmail.com
Star notifications: github.com/qualixar/qualixar-os (repo goes public next week)
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