Run Multi-Agent Teams from Claude Code with Qualixar OS (25 MCP Tools)

Qualixar OS is an open-source agent orchestration runtime. You give it a task in plain English, and it designs a team of AI agents, picks a topology, runs them, and evaluates the output through an adversarial judge pipeline. It ships with 25 MCP tools, so you can drive the entire system from Claude Code without touching a browser.

This post walks through connecting Qualixar OS as an MCP server in Claude Code and using it to design, run, and evaluate a multi-agent code review team -- all from your terminal.

Installation

npx qualixar-os

That starts the server and opens the dashboard at localhost:3000/dashboard/. You can also install globally:

npm install -g qualixar-os
qos serve --dashboard --port 3000

Qualixar OS auto-detects Ollama for local inference. No API keys required to start. Add cloud providers (Anthropic, OpenAI, Azure, etc.) later through the Settings tab if you want more power.

MCP Server Configuration

Add this to your ~/.claude.json:

{
  "mcpServers": {
    "qualixar-os": {
      "command": "npx",
      "args": ["qualixar-os", "--mcp"]
    }
  }
}

Restart Claude Code. You now have 25 tools available. Run list tools in Claude Code to verify they appear.

The same config works in Cursor, Windsurf, VS Code (with MCP extension), or any MCP-compatible client.

The 25 MCP Tools

Tools are organized by domain. Here is the full catalog.

Task Execution

Agents and Forge AI

Quality and Memory

Chat and Data

Blueprints and Prompts

System

If you are on a tight context budget, Qualixar OS also offers 7 domain-grouped tools (qos_task, qos_system, qos_agents, qos_context, qos_quality, qos_workspace, qos_workflow_create) that pack the same 25 operations into fewer tool definitions using an action discriminator. Set QOS_TIER=core to expose only 2 tools (task + system), or QOS_TIER=extended for 4. Default is full.

Tutorial: Code Review Team via Forge AI

Here is a concrete walkthrough. You are in Claude Code, Qualixar OS is connected as an MCP server, and you want to run a multi-agent code review on a pull request.

Step 1: Submit the task

Call run_task with your prompt:

run_task({
  prompt: "Review the authentication module in src/auth/ for security vulnerabilities, code quality issues, and test coverage gaps. Produce a structured report.",
  type: "code",
  mode: "power"
})

Forge AI reads the prompt, decides this is a code quality task, and auto-designs a team.

Step 2: Inspect the Forge design

Call get_forge_designs to see what Forge created:

get_forge_designs({ taskType: "code" })

Forge might return something like:

• 3 agents: Security Analyst, Code Quality Reviewer, Test Coverage Auditor
• Topology: Debate (two reviewers produce independent reports, a judge synthesizes)
• Tools assigned: file_read, code_search, file_write
• Estimated cost: $0.04

If you disagree with the topology, you can cancel and re-submit with an explicit override:

run_task({
  prompt: "...",
  topology: "hierarchical"
})

Step 3: Monitor execution

Poll status:

get_status({ taskId: "task_abc123" })

Status transitions: pending -> forge_designing -> executing -> judging -> completed (or rejected -> retry loop, up to 5 rounds).

Step 4: Check quality scores

Once execution completes, the judge pipeline runs automatically. Retrieve results:

get_judge_results({ taskId: "task_abc123" })

The judge returns structured feedback: per-criterion scores (correctness, completeness, clarity), an overall verdict (approved/rejected), severity ratings on any issues found, and specific improvement suggestions. If rejected, Forge automatically redesigns the team using the judge's feedback and re-executes -- up to 5 rounds, with a 3x budget cap as a safeguard.

Step 5: View in the dashboard

Open localhost:3000/dashboard/ to see the full execution visually. The 24-tab dashboard shows real-time agent activity (Swarms tab), judge verdicts (Judges tab), cost breakdown (Cost tab), and the final output (Chat tab). Everything you did from Claude Code is reflected there.

Advanced: Topology Selection and Cost Constraints

Choosing a topology

Qualixar OS supports 13 execution topologies. A few worth knowing:

Pass topology to run_task to override Forge's automatic selection.

Budget constraints

run_task({
  prompt: "...",
  budget_usd: 0.10
})

Forge respects the budget when selecting models and team size. Cost tracking is available during and after execution via get_cost.

Dry run

run_task({
  prompt: "...",
  simulate: true
})

Returns the Forge design and cost estimate without actually running agents.

A2A: Agent-to-Agent Protocol

Qualixar OS also implements the A2A protocol (v0.3). When the server is running, it exposes an agent card at:

GET http://localhost:3000/.well-known/agent-card

This means external A2A-compatible agents can discover and submit tasks to your Qualixar OS instance. Internal agents also communicate via A2A. Both MCP (tool calling from IDE) and A2A (agent-to-agent federation) work simultaneously on the same server.

Links

• GitHub: github.com/qualixar/qualixar-os
• Documentation: qualixar.com
• Paper: arXiv:2604.06392 -- formal topology semantics, empirical evaluation (1 of 7 papers in the Qualixar ecosystem)
• Research Ecosystem: Judge pipeline backed by AgentAssert (arXiv:2602.22302). Memory powered by SuperLocalMemory (3 papers). Evaluation via AgentAssay. Skill testing via SkillFortify.
• License: FSL-1.1-ALv2 (converts to Apache 2.0 after two years)
• Tests: 2,936 passing

If you run into issues or have questions, open an issue on GitHub or comment below.

The Qualixar AI Agent Reliability Platform

Seven open-source primitives. Seven peer-reviewed papers. One reliability platform.

• SuperLocalMemory — persistent memory + learning for AI agents (16K+ monthly installs)
• Qualixar OS — universal agent runtime with 13 topologies
• SLM Mesh — P2P coordination across AI sessions
• SLM MCP Hub — federate 430+ MCP tools through one gateway
• AgentAssay — token-efficient agent testing
• AgentAssert — behavioral contracts + drift detection
• SkillFortify — formal verification for agent skills

Start here → qualixar.com