Model Router

Intelligent, explainable, cost- and latency-aware LLM request routing platform and AI Traffic Control Room.

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Overview

Model Router intercepts incoming AI requests, analyzes their task type and continuous complexity, evaluates available models against a configurable multi-criteria scoring objective, selects the optimal candidate, and dispatches the request with automatic fallback handling and budget guards.

The platform is designed local-first, allowing full local development and testing using Mock models or local Ollama instances without requiring paid external API keys.

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Key Features

• Dual-Mode Request Analyzer: Deterministic heuristics (<3ms latency overhead) for 12 task types, continuous complexity scoring (0.05 to 0.99), and requirement detection, plus an optional LLM classifier mode.
• Explainable Routing Engine: Multi-criteria weighted scoring across Quality, Cost Efficiency, Speed, Capabilities, and Reliability with transparent decision factor reports and candidate rejection logs.
• Provider Abstraction: Decoupled adapters for Mock (simulation), Ollama (local), OpenAI, Anthropic, and Google Gemini.
• Resilience and Tiered Fallback: Automated retry classification for transient errors (timeouts, HTTP 429, 503) and tiered fallback to local/mock alternatives.
• Budget Control Guards: Real-time spend tracking with automated threshold interventions (80% cost optimization, 95% local-only saver, 100% block).
• Traffic Control Room UI: Real-time operational interface with seamless dark/light mode switching, featuring live topology graphs, playground inspector, SSE live request stream, telemetry export as CSV/JSON, visual rules builder, and cost savings simulator.
• Developer CLI: Terminal diagnostics (doctor), routing dry-run (route), execution (run), model catalog (models), and analytics (analytics).

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Architecture & Workflow

[ Client / SDK / Typer CLI ]
           │
           ▼
[ FastAPI Gateway (Port 8000) ]
           │
  ┌────────┴──────────────────────────┐
  │ 1. Request Analyzer (<3ms)        │  --> Task Type, Complexity, Context Size
  │ 2. Priority Rules Evaluation      │  --> Conditional Overrides
  │ 3. Candidate Hard Pruning         │  --> Filter Ineligible Models (Context / Caps)
  │ 4. Multi-Criteria Scoring         │  --> Normalized 0-100 Score across 5 Dimensions
  │ 5. Decision Factor Generator      │  --> Itemized Explainability Breakdown
  └────────┬──────────────────────────┘
           │
           ▼
[ Fallback Supervisor & Provider Layer ]
  ├── Local: Ollama Provider (qwen2.5-coder, llama3.2, deepseek-r1)
  ├── Simulated: In-Memory Mock Provider (Zero Cost)
  └── Cloud: OpenAI, Anthropic, Google Gemini (Optional)
           │
           ▼
[ Storage & Observability Engine ]
  ├── Asynchronous SQLite WAL Database (`model_router.db`)
  └── Server-Sent Events (SSE) Stream -> React Control Room (Port 5173)

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Supported Platforms & Prerequisites

Supported Platforms

• Linux (Ubuntu 20.04+, Debian 11+, Fedora)
• macOS (macOS 12+ / Apple Silicon & Intel)
• Windows (Windows 10, Windows 11 / PowerShell & WSL2)

Prerequisites

• Python 3.10, 3.11, or 3.12
• Node.js 18+ and npm
• (Optional) Ollama for local model inference

📦 Quick Installation (via PyPI)

Install Model Router and its embedded AI Traffic Control Room dashboard directly from PyPI:

pip install model-router-cli

Quick Commands:

# Run system diagnostics & environment checks
modelrouter doctor

# Launch the Traffic Control Room Dashboard & API Gateway in your browser
modelrouter ui --open-browser

# Route a prompt and inspect the explainability decision (Dry Run)
modelrouter route "Write an optimized async task worker in Python"

# Route and execute inference through the optimal provider
modelrouter run "Explain the difference between zero-shot and few-shot prompting"

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Installation & Setup (from Source)

1. Clone the Repository

git clone https://github.com/PicadoLabs/AI-Model-Router.git
cd AI-Model-Router

2. Backend Installation

# Create and activate virtual environment
python -m venv venv
# On Linux/macOS:
source venv/bin/activate
# On Windows PowerShell:
.\venv\Scripts\Activate.ps1

# Install Python dependencies
pip install -r requirements.txt

# Create environment file from template
cp .env.example .env

3. Frontend Installation

cd frontend
npm install
cd ..

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Configuration & Environment Variables

Configuration is loaded via Pydantic Settings from the .env file:

Variable	Default	Description
APP_ENV	development	Application environment (development, production, test)
PORT	8000	FastAPI server port
HOST	0.0.0.0	FastAPI server host
DATABASE_URL	sqlite+aiosqlite:///./model_router.db	SQLAlchemy database connection URI
ROUTER_ANALYZER	rules	Default analyzer mode (rules for heuristics, llm for model classifier)
DEFAULT_ROUTING_POLICY	balanced	Default routing weights (balanced, lowest_cost, lowest_latency, highest_quality)
BASELINE_MODEL_ID	mock-power	Reference model ID for calculating baseline cost savings
DEFAULT_PROVIDER	mock	Default execution provider (mock, ollama)
OLLAMA_BASE_URL	http://localhost:11434	Ollama HTTP endpoint
OPENAI_API_KEY	(empty)	Optional OpenAI API Key
ANTHROPIC_API_KEY	(empty)	Optional Anthropic API Key
GEMINI_API_KEY	(empty)	Optional Google Gemini API Key
DAILY_BUDGET	10.00	Daily spend limit in USD
MONTHLY_BUDGET	100.00	Monthly spend limit in USD
MAX_RETRIES	2	Maximum retries before triggering cascading fallback
PROVIDER_TIMEOUT_SECONDS	30.0	Provider HTTP timeout in seconds

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Quickstart

1. Run System Diagnostics

python backend/app/cli/main.py doctor

2. Start the Backend API Server

python backend/main.py
# API server running at http://127.0.0.1:8000
# Interactive API docs available at http://127.0.0.1:8000/docs

3. Start the Control Room UI

In a separate terminal:

cd frontend
npm run dev
# Access UI at http://localhost:5173

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CLI Usage

The built-in Typer CLI provides terminal commands for inspection, diagnostics, and testing (use modelrouter if installed via pip, or python backend/app/cli/main.py from source):

# Launch the AI Traffic Control Room Web UI and API Gateway
modelrouter ui --open-browser

# Run system diagnostics & provider health checks
modelrouter doctor

# Inspect routing decision for a prompt without executing (Dry Run)
modelrouter route "Write a Python function to parse JSON"

# Route and execute a query through the selected model
modelrouter run "Debug this distributed async deadlock in worker pool"

# List all registered models in the catalog
modelrouter models

# View recent routed traffic logs
modelrouter traffic

# View system-wide routing performance and cost savings analytics
modelrouter analytics

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REST API Usage

1. Dry-Run Routing (POST /api/route)

curl -X POST http://127.0.0.1:8000/api/route \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Write a quicksort algorithm in Python", "policy": "balanced"}'

2. End-to-End Routed Generation (POST /api/generate)

curl -X POST http://127.0.0.1:8000/api/generate \
  -H "Content-Type: application/json" \
  -d '{"prompt": "Explain the difference between TCP and UDP", "policy": "lowest_cost"}'

3. Fetch Registered Models (GET /api/models)

curl http://127.0.0.1:8000/api/models

4. Export Historical Traffic (GET /api/traffic/export)

Export all persisted traffic records for auditing, accounting, or latency analysis:

# Export as JSON
curl -OJ "http://127.0.0.1:8000/api/traffic/export?format=json"

# Export as CSV
curl -OJ "http://127.0.0.1:8000/api/traffic/export?format=csv"

Each export includes the timestamp, request ID, prompt preview, task type, complexity, selected model, input/output/total tokens, cost saved, and total latency. The format query parameter accepts only csv or json.

The same export is available in the frontend under Traffic. Select CSV or JSON beside Export Telemetry, then click the button to download the complete historical traffic dataset.

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Running Tests

The test suite includes 18 automated unit, integration, and end-to-end tests covering prompt heuristics, candidate pruning, scoring weights, provider execution, error fallbacks, REST endpoints, and CSV/JSON traffic exports:

# Run the backend test suite from the repository root
pytest backend/tests

# Or run it from the backend directory
cd backend
python -m pytest tests

# Run frontend production build test
cd frontend
npm run build

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Project Structure

AI-Model-Router/
├── .github/
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug_report.md
│   │   └── feature_request.md
│   ├── pull_request_template.md
│   └── workflows/
│       └── ci.yml
├── backend/
│   ├── app/
│   │   ├── analytics/       # Cost savings and aggregate analytics service
│   │   ├── analyzer/        # Dual-mode request analyzer (heuristics & LLM)
│   │   ├── api/             # FastAPI REST endpoints and request handlers
│   │   ├── budgets/         # Spend tracking and automated threshold manager
│   │   ├── cli/             # Typer CLI application (doctor, route, run, etc.)
│   │   ├── config/          # Pydantic Settings environment configuration
│   │   ├── experiments/     # A/B policy experimentation service
│   │   ├── fallback/        # Error classifier and tiered fallback supervisor
│   │   ├── models/          # Pydantic schemas (RequestAnalysis, RoutingDecision)
│   │   ├── observability/   # Redacted structured JSON event logger
│   │   ├── providers/       # Decoupled adapters (Mock, Ollama, Cloud)
│   │   ├── router/          # Core scoring matrix, pruner, and rules engine
│   │   └── storage/         # SQLAlchemy models and SQLite async database
│   ├── tests/               # Pytest test suite (15 passing tests)
│   ├── main.py              # FastAPI application entrypoint
│   └── requirements.txt     # Python backend dependencies
├── frontend/
│   ├── src/
│   │   ├── components/      # UI components (Navbar, RoutingMap topology graph)
│   │   ├── pages/           # Control Room pages (Dashboard, Playground, Rules, etc.)
│   │   └── types/           # TypeScript data interfaces
│   └── package.json         # Node.js dependencies
├── .env.example             # Configuration template
├── .gitignore               # Git exclusions
├── CODE_OF_CONDUCT.md       # Contributor Covenant Code of Conduct
├── CONTRIBUTING.md          # Contribution guidelines and workflow
├── LICENSE                  # MIT License
├── README.md                # Project documentation
├── requirements.txt         # Root Python dependencies
└── SECURITY.md              # Vulnerability reporting and security policy

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Contributing

We welcome contributions from the community. Please review CONTRIBUTING.md for details on our development setup, coding standards, branch conventions, and pull request process.

Please note that this project is released with a Code of Conduct. By participating in this project you agree to abide by its terms.

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Security

Security and privacy are core to Model Router. For vulnerability reporting procedures and our zero-secret-exposure policy, please refer to SECURITY.md.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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PicadoLabs

Maintained and architected by PicadoLabs.

• Maintainer: @Kaap10
• Organization: PicadoLabs
• Website: https://picadolabs.me
• Contact: picadolabs@gmail.com