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DEPRECATED — This repository has been consolidated into mdo-nexus-ooda. No further updates here.

N E X U S

Orchestrate multi-model AI agent teams. Observe every decision in real time.


Release License Last commit Stars



Quick Start  •  Architecture  •  API  •  Releases


FastAPI React TypeScript TailwindCSS LiteLLM Docker

"Humans steer. Agents execute."


Why Nexus

Nexus gives you a single control surface for multi-model AI agent teams. Define a team of specialist models, assign each a role, then launch tasks and watch execution unfold in real time through WebSocket-driven dashboards.

One endpoint provisions a battle-tested five-agent team. One command deploys the entire stack.


Compose
Build teams with role-specific models
Execute
Parallel & sequential pipelines
Observe
Live WebSocket execution feed
Analyze
Tokens, cost, latency per agent

Architecture

graph TB
    subgraph CLIENT ["CLIENT"]
        direction LR
        D["Dashboard"]
        TB["TeamBuilder"]
        EM["ExecutionMonitor"]
        RV["ResultsViewer"]
    end

    subgraph API ["FASTAPI  :8800"]
        direction LR
        REST["/api/*"]
        WS["/ws"]
        ORCH["Orchestrator"]
    end

    subgraph DATA ["PERSISTENCE"]
        direction LR
        DB[("SQLite\nWAL mode")]
        LLM["LiteLLM\n:4000"]
    end

    CLIENT -- "REST + WebSocket" --> API
    ORCH --> DB
    ORCH --> LLM

    style CLIENT fill:#1e1e2e,stroke:#cba6f7,color:#cdd6f4
    style API fill:#1e1e2e,stroke:#89b4fa,color:#cdd6f4
    style DATA fill:#1e1e2e,stroke:#a6e3a1,color:#cdd6f4
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Data Model

erDiagram
    TEAM ||--o{ AGENT : contains
    TEAM ||--o{ TASK : receives
    TASK ||--o{ STEP : "broken into"
    TASK ||--o{ EXECUTION : triggers
    EXECUTION ||--o{ STEP_RESULT : produces

    TEAM {
        string id PK
        string name
        string strategy
    }
    AGENT {
        string id PK
        string role
        string model
    }
    TASK {
        string id PK
        string prompt
        string mode
    }
    EXECUTION {
        string id PK
        string status
        float total_cost
    }
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Harness Engineering Preset

A single POST /api/teams/preset/harness provisions the reference team:

Role Model Responsibility
Orchestrator claude-opus-4-6 Decomposition, delegation, judgment
Backend gpt-5.3-codex Logic, review, refactoring
Actor claude-sonnet-4-6 Primary code generation
Security qwen3-coder Vulnerability analysis
Designer gemini-3.1-pro UI evaluation, visual judgment

Quick Start

Docker — recommended

docker compose up -d

Frontend on :3000 · Backend on :8800

Manual

# backend
cd backend && pip install -e ".[dev]"
uvicorn src.main:app --port 8800 --reload

# frontend
cd frontend && npm install && npm run dev

Open http://localhost:5173 — API proxied automatically.

Optional — connect a LiteLLM proxy at localhost:4000 for live AI execution.


API Reference

Method Endpoint Purpose
GET /api/health Liveness + LiteLLM status
GET /api/dashboard Aggregate statistics
GET /api/models Available models via LiteLLM
POST /api/teams Create team
POST /api/teams/preset/harness Provision preset team
GET /api/teams List teams
POST /api/tasks Create task with steps
POST /api/tasks/{id}/execute Execute task
GET /api/executions List executions
WS /ws Real-time execution stream

Testing

cd backend && pytest tests/ -v   # 17 tests · ~2.6s

License

MIT


Built with precision. Designed for control.

About

AI Agent Mission Control Dashboard — Full-stack web app for visualizing and orchestrating multi-model AI agent teams. FastAPI + React 19 + TailwindCSS + LiteLLM.

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