ModelRouter Example¶

The ModelRouter is an intelligent routing system that automatically selects the best AI model for a given task. It analyzes task requirements and recommends the optimal model and provider combination, then executes the task using the selected model.

How It Works¶

Task Analysis: Analyzes the task to understand requirements
Model Selection: Recommends the best model based on task characteristics
Automatic Execution: Runs the task using the selected model
Optimization: Considers factors like complexity, speed, cost, and capabilities

This architecture is perfect for: - Optimizing model selection for different tasks - Cost-effective AI operations - Automatic model routing based on task requirements - Multi-model orchestration

Installation¶

Install the swarms package using pip:

pip install -U swarms

Basic Setup¶

First, set up your environment variables:

WORKSPACE_DIR="agent_workspace"
OPENAI_API_KEY="your-api-key"
# Optional: Set other provider API keys
ANTHROPIC_API_KEY="your-anthropic-key"
GOOGLE_API_KEY="your-google-key"

Step-by-Step Example¶

Step 1: Import Required Modules¶

from swarms import ModelRouter

Step 2: Create the ModelRouter¶

router = ModelRouter(
    max_tokens=4000,
    temperature=0.5,
    max_workers=10,
)

Step 3: Run a Task¶

The router will automatically: 1. Analyze the task 2. Select the best model 3. Execute the task 4. Return the result

task = "Write a creative short story about a robot learning to paint"

result = router.run(task=task)

print(result)

Understanding Model Selection¶

The ModelRouter considers various factors:

Task Complexity: Simple tasks → faster/cheaper models
Reasoning Needs: Complex reasoning → specialized models
Creativity: Creative tasks → models with higher temperature
Speed Requirements: Fast responses → optimized models
Cost Efficiency: Balance between quality and cost

Example Tasks¶

Simple Query¶

router = ModelRouter()

result = router.run("What is the capital of France?")
# Likely selects: gpt-4o-mini or gpt-3.5-turbo (fast, cost-effective)

Complex Reasoning¶

router = ModelRouter()

result = router.run("""
Solve this logic puzzle:
- Alice, Bob, and Charlie are sitting in a row
- Alice is not next to Bob
- Charlie is at one end
- Who is sitting where?
""")
# Likely selects: deepseek-reasoner or gpt-4-turbo (reasoning-focused)

Creative Writing¶

router = ModelRouter()

result = router.run("Write a poem about artificial intelligence")
# Likely selects: claude-3-5-sonnet or gpt-4 (creative capabilities)

Technical Analysis¶

router = ModelRouter()

result = router.run("""
Analyze the time complexity of this algorithm:
def binary_search(arr, target):
    left, right = 0, len(arr) - 1
    while left <= right:
        mid = (left + right) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1
    return -1
""")
# Likely selects: claude-3-opus or gpt-4-turbo (technical analysis)

Custom Configuration¶

Adjust Temperature¶

router = ModelRouter(
    temperature=0.7,  # Higher for more creative outputs
    max_tokens=2000,
)

Set Max Tokens¶

router = ModelRouter(
    max_tokens=8000,  # For longer outputs
    temperature=0.5,
)

Custom System Prompt¶

from swarms import ModelRouter

custom_prompt = """
You are an expert model router. Consider these factors:
1. Task complexity
2. Required reasoning depth
3. Creative needs
4. Speed requirements
5. Cost efficiency
"""

router = ModelRouter(
    system_prompt=custom_prompt,
    max_tokens=4000,
)

Batch Processing¶

Process multiple tasks:

router = ModelRouter()

tasks = [
    "Summarize this article: [article text]",
    "Translate to Spanish: Hello, how are you?",
    "Solve: 2x + 5 = 15",
]

results = router.batch_run(tasks)

print(results)

Concurrent Execution¶

Run multiple tasks concurrently:

router = ModelRouter(max_workers=5)

tasks = [
    "Task 1: Analyze market trends",
    "Task 2: Generate report summary",
    "Task 3: Create data visualization description",
]

results = router.concurrent_run(tasks)

Single Step Execution¶

If you want to see the model selection process:

router = ModelRouter()

# This will show the selected model, provider, and rationale
result = router.step("Your task here")

The output will display: - Selected Model - Provider - Task (optimized for the model) - Rationale (why this model was chosen) - Max Tokens - Temperature - System Prompt

Run multiple iterations for improvement:

router = ModelRouter(max_loops=3)

# Will run up to 3 iterations, stopping if output stabilizes
result = router.run("Improve this code: [code snippet]")

Supported Models¶

The ModelRouter can route to various models including: - GPT-4, GPT-3.5 (OpenAI) - Claude 3.5 Sonnet, Claude 3 Opus (Anthropic) - Gemini Pro (Google) - DeepSeek Reasoner (DeepSeek) - Mistral Large (Mistral) - And more...

Support and Community¶

If you're facing issues or want to learn more, check out the following resources:

Platform	Link	Description
📚 Documentation	docs.swarms.world	Official documentation and guides
💬 Discord	Join Discord	Live chat and community support
🐦 Twitter	@swarms_corp	Latest news and announcements