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title Getting Started with Qualixar OS
description Install, configure, and run your first AI agent task in under 5 minutes. Comprehensive entry point for all Qualixar OS documentation.
category getting-started
tags
install
quickstart
setup
first-run
documentation
index
last_updated 2026-04-13

Getting Started with Qualixar OS

Qualixar OS is the Universal OS for AI Agents. One control plane to orchestrate LLM agents across 15+ providers, 13 topologies, and every major IDE and framework.

Quick Start

Three commands. Zero cost. Ollama runs locally on your machine.

# 1. Install Qualixar OS
npm install -g qualixar-os

# 2. Pull a free local model
ollama pull llama3.3

# 3. Launch the dashboard
qos dashboard

Open http://localhost:3000. You now have a full agent operating system running locally.

Note: The default server port is 3000. Both qos serve and qos dashboard accept --port to override it.

Submit Your First Task

From the Dashboard: Go to the Chat tab, select llama3.3 from the model dropdown, type a prompt, and press Enter.

From the CLI:

qos run "Summarize the benefits of multi-agent systems in 3 bullet points"
qos status <task-id>    # Check task progress
qos output <task-id>    # View completed output

Prerequisites

  • Node.js 22+ (required for ESM top-level await; 22 LTS or later recommended)
  • Ollama for local models, or an API key for any cloud provider

Alternative Install

Run without a global install:

npx qualixar-os dashboard

Verify your installation:

qos --version
qos doctor

Choose Your Provider

Qualixar OS connects to 15+ LLM providers through a single interface. Start with Ollama (free, local), then add cloud providers as needed.

Provider Models Cost Setup
Ollama Llama 3.3, Mistral, Gemma, Phi, 100+ Free Local install
OpenRouter 100+ models from every vendor Pay-per-token API key
Ollama Cloud 36+ hosted models Free tier available API key
OpenAI GPT-4o, GPT-5, o3 Pay-per-token API key
Anthropic Claude Opus, Sonnet, Haiku Pay-per-token API key
Azure OpenAI models via Azure Enterprise billing Endpoint + key
LM Studio Any GGUF model Free Local install
Custom Any OpenAI-compatible API Varies Base URL + key

Add providers from the dashboard (Settings > Providers) or via qos init.

Full provider documentation: providers/overview.md


Core Concepts

The Pipeline

Every task in Qualixar OS flows through a five-stage pipeline:

Memory --> Forge --> Agents --> Judge --> Output
  1. Memory retrieves relevant context from past tasks and external knowledge
  2. Forge AI automatically designs the optimal agent team for the task
  3. Agents execute the work across one of 13 topologies
  4. Judge Pipeline evaluates output quality against configurable criteria
  5. Output delivers the verified result

Forge AI (Auto Team Design)

Forge analyzes your prompt and assembles the right agents, tools, and topology automatically. No manual configuration required for most tasks. You can override any decision.

Documentation: dashboard/forge.md

Judge Pipeline (Quality Assurance)

Every task output passes through judges that score correctness, completeness, and safety. Configure judge thresholds, add custom judges, or bypass for speed.

Documentation: dashboard/judges.md

13 Topologies

Qualixar OS supports 13 execution topologies, from simple single-agent to complex multi-agent orchestrations:

Topology Code Name Use Case
Sequential sequential Step-by-step chain of agents
Parallel parallel Independent agents, merged results
Hierarchical hierarchical Manager delegates to workers
DAG dag Directed acyclic graph with complex dependencies
Mixture of Agents mixture_of_agents Ensemble answers, synthesized output
Debate debate Agents argue, judge picks winner
Mesh mesh All-to-all collaboration
Star star Hub coordinates spokes
Circular circular Round-robin iterative refinement
Grid grid Matrix neighbor communication
Forest forest Multiple parallel hierarchies
Maker maker Build-test-ship engineering loop
Hybrid hybrid Local/cloud PII-safe split

Documentation: reference/topologies.md

Memory System

Qualixar OS persists task history, agent decisions, and retrieved context across sessions. Integrates with SuperLocalMemory for cross-tool knowledge sharing.

Documentation: memory/overview.md


Documentation Index

Dashboard (24 Tabs)

The web dashboard provides full visual control over every aspect of agent orchestration. Default port: 3000 (configurable via --port).

Tab Description Docs
Overview Dashboard home, system status dashboard/overview.md
Chat Interactive model chat dashboard/chat.md
Agents Agent configuration and management dashboard/agents.md
Swarms Multi-agent swarm orchestration dashboard/swarms.md
Forge AI-powered team design dashboard/forge.md
Judges Quality assurance pipeline dashboard/judges.md
Pipelines Task pipeline builder dashboard/pipelines.md
Flows Visual workflow editor dashboard/flows.md
Memory Memory browser and search dashboard/memory.md
Vectors Vector store management dashboard/vectors.md
Brain Knowledge graph visualization dashboard/brain.md
Tools Tool registry and configuration dashboard/tools.md
Blueprints Reusable task templates dashboard/blueprints.md
Builder Visual agent builder dashboard/builder.md
Lab Experimentation sandbox dashboard/lab.md
Datasets Dataset management for evaluation dashboard/datasets.md
Marketplace Skill and plugin marketplace dashboard/marketplace.md
Connectors External service integrations dashboard/connectors.md
Gate Access control and permissions dashboard/gate.md
Audit Audit log and compliance dashboard/audit.md
Cost Token usage and cost tracking dashboard/cost.md
Logs System and task logs dashboard/logs.md
Traces Execution trace viewer dashboard/traces.md
Settings Provider config, preferences dashboard/settings.md

CLI (25 Commands)

Full command-line interface for headless and scripted workflows.

Command What It Does
qos init Interactive setup wizard
qos serve Start HTTP/WebSocket server
qos dashboard Start standalone dashboard (default port 3000, configurable via --port)
qos run <prompt> Run a task
qos status <id> Check task status
qos output <id> View task output
qos models List available models
qos doctor Health check
qos config <key> <val> Update configuration

Full CLI reference: cli/overview.md | CLI vs MCP comparison: cli/cli-vs-mcp.md

Protocols

Protocol Description Docs
MCP Model Context Protocol server/client protocols/mcp.md
A2A Agent-to-Agent protocol support protocols/a2a.md
Overview Protocol architecture protocols/overview.md

Frameworks

Integrate Qualixar OS as a backend for popular agent frameworks.

Framework Docs
LangChain frameworks/langchain.md
CrewAI frameworks/crewai.md
AutoGen frameworks/autogen.md
OpenAI Agents frameworks/openai-agents.md
Semantic Kernel frameworks/semantic-kernel.md
Custom frameworks/custom-integration.md
Overview frameworks/overview.md

Claude CLI Integration

Use Qualixar OS directly from Claude Code or any Claude CLI session.

Topic Docs
Overview claude-cli/overview.md
MCP Setup claude-cli/mcp-setup.md
Plugin Install claude-cli/plugin-install.md
Skills Guide claude-cli/skills-guide.md
CLI Native Bridge claude-cli/cli-native-bridge.md
Power User claude-cli/power-user.md

IDE Integration

Topic Docs
Overview ide-integration/overview.md
MCP Protocol ide-integration/mcp-protocol.md

Providers

Provider Docs
Overview providers/overview.md
Ollama providers/ollama.md
OpenRouter providers/openrouter.md
Ollama Cloud providers/ollama-cloud.md
OpenAI providers/openai.md
Anthropic providers/anthropic.md
Azure providers/azure.md
LM Studio providers/lmstudio.md
Custom providers/custom.md

Memory

Topic Docs
Overview memory/overview.md
SuperLocalMemory memory/superlocalmemory.md
SLM Integration memory/slm-integration.md

Guides

Guide Description Docs
Quickstart (5 Minutes) Install, launch dashboard, run your first agent team guides/quickstart-5-minutes.md
Multi-Agent Code Review Code review with debate topology and judge verdicts guides/multi-agent-code-review.md
MCP Server Setup Use QOS from Claude Code, VS Code, or Cursor via MCP guides/mcp-server-setup.md
First Multi-Agent Task Run agents in parallel, pipeline, or debate guides/first-multi-agent-task.md
MCP Integration Connect external MCP servers to Qualixar OS guides/mcp-integration.md
Deploy with Docker Containerized deployment guides/deploy-docker.md
Security Setup Auth, TLS, and access control guides/security-setup.md
Cost Optimization Reduce token spend across providers guides/cost-optimization.md
Troubleshooting Common issues and fixes guides/troubleshooting.md

Reference

Reference Description Docs
API Endpoints All 27+ REST endpoints reference/api-endpoints.md
Config Schema Full config.yaml specification reference/config-schema.md
Topologies 13 execution topologies reference/topologies.md
Events Event system and hooks reference/events.md
Skill Manifest Skill package format reference/skill-manifest.md
Tool Categories Tool classification system reference/tool-categories.md

Key Paths

Item Location
Config ~/.qualixar-os/config.yaml
Environment ~/.qualixar-os/.env
Database ~/.qualixar-os/qos.db
Logs ~/.qualixar-os/logs/
Plugins ~/.qualixar-os/plugins/

Next Steps

Once you have the dashboard running and your first task complete, here is where to go next:

  1. Try different topologies. Run the same task as a debate, pipeline, or parallel execution. See how agent collaboration changes the output. Start with the First Multi-Agent Task guide.

  2. Install marketplace skills. Open the Marketplace tab in the dashboard and install pre-built skills for coding, research, writing, and more. See dashboard/marketplace.md.

  3. Connect from Claude Code. Add Qualixar OS as an MCP server in your Claude CLI config and orchestrate agents without leaving your terminal. See claude-cli/mcp-setup.md.

  4. Connect from VS Code. Use the IDE integration to run agent tasks directly from your editor. See ide-integration/overview.md.

  5. Add a cloud provider. Pair Ollama with a cloud model for hybrid local+cloud topologies. Open Settings > Providers or see providers/overview.md.

  6. Deploy with Docker. Run Qualixar OS as a persistent service. See guides/deploy-docker.md.

  7. Read the paper. The research behind Qualixar OS is published on arXiv: 2604.06392.