🔐🦙🤖 Private & Local Ollama Self-Hosted LLM Router

For the Private & Local Ollama Self-Hosted LLM Router, this automated workflow intelligently analyzes user prompts and dynamically selects the most suitable local large language model (LLM) for optimal performance. It simplifies the process of routing requests between specialized models, ensuring efficient handling of tasks like complex reasoning, multilingual conversations, and image analysis—all while maintaining complete privacy by processing everything locally. Ideal for AI enthusiasts and developers, this solution eliminates the need for technical expertise, allowing users to leverage powerful AI capabilities effortlessly.

7/8/2025
16 nodes
Complex
manualcomplexlangchainsticky noteadvanced
Categories:
Complex WorkflowManual Triggered
Integrations:
LangChainSticky Note

Target Audience

This workflow is designed for:
- AI Enthusiasts: Individuals interested in exploring and utilizing advanced AI models locally.
- Developers: Technical users looking to integrate AI capabilities into their applications without relying on external services.
- Privacy-Conscious Users: Those who prioritize data security and wish to keep their interactions private by processing everything locally.

It is particularly beneficial for users running Ollama locally and needing intelligent routing between different specialized models.

Problem Solved

This workflow addresses the challenge of selecting the right local large language model (LLM) for specific tasks. It automates the process of analyzing user prompts and routing them to the most appropriate Ollama model, ensuring optimal performance without requiring technical knowledge from the end user. By intelligently classifying user requests, it eliminates the need for manual selection and enhances the efficiency of using multiple LLMs.

Workflow Steps

  • Chat Message Trigger: The workflow starts when a chat message is received, activating the process.
    2. LLM Router: The input is analyzed to determine the most suitable LLM model based on predefined criteria and rules.
    3. Dynamic Model Selection: The router dynamically selects the appropriate model from the local collection based on the user's request.
    4. Memory Handling: Both router and agent maintain conversation memory to ensure consistent interactions.
    5. Response Generation: The chosen LLM processes the user input and generates a response, which is then delivered back to the user.
    6. Local Processing: All operations are conducted locally, ensuring complete privacy and data security.

    • Customization Guide

    Users can customize this workflow by:
    - Adding or Removing Models: Modify the router's decision framework to include or exclude models based on their specific Ollama collection.
    - Adjusting System Prompts: Change the prompts in the LLM Router to prioritize different selection criteria for models.
    - Modifying Decision Logic: Alter the decision tree logic to better fit specific use cases or user needs.
    - Implementing Additional Preprocessing: Introduce extra steps for specialized inputs to enhance the routing process.

    This flexibility allows users to tailor the workflow to their unique requirements and optimize it for their specific tasks.