🚀 How I Built FederatedRouter in MultiMindSDK to Seamlessly Switch Between GPT-4, Mistral, Qwen & More LLMs

As an AI engineer, I kept running into one frustrating pattern: every LLM pipeline I built was locked to a single model.
What if your agent could choose the best model for each task — GPT-4 for reasoning, Mistral for speed, and Qwen for multilingual support — in real time?

That’s exactly why I built FederatedRouter inside MultiMindSDK — an open-source framework for modular AI agents.

Why Multi-Model Routing Matters

Multi-LLM pipelines give developers:

• ✅ Lower latency (Mistral or DeepSeek for faster responses)

• ✅ Better cost control (fallback to local models)

• ✅ Smarter fallback logic (route based on context or error)

📦 Real-World Use Case

from multimind.client.federated_router import FederatedRouter

# Initialize model clients (placeholders)
gpt4_client = ...
mistral_client = ...
qwen_client = ...

# Define the router
router = FederatedRouter(
    clients={
        "gpt4": gpt4_client,
        "mistral": mistral_client,
        "qwen": qwen_client
    },
    routing_fn=lambda prompt:
        "qwen" if "translate" in prompt.lower() else
        "mistral" if len(prompt) < 50 else
        "gpt4"
)

response = router.generate("Translate this to French and explain the grammar.")
print(response)

✨ Results

• 🔁 Real-time model switching

• 💸 Token efficiency

• 📈 Flexibility in deploying agents across use cases

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📦 Try MultiMindSDK: pip install multimind-sdk | npm i multimind-sdk
🧪 Website: https://multimind.dev
🔗 GitHub: https://github.com/multimindlab/multimind-sdk