Les cyberattaques exigent une réactivité maximale. L’automatisation détecte les incidents, notifie les équipes, archive les logs et programme les plans d’action correctifs pour sécuriser le SI.
Les cyberattaques exigent une réactivité maximale. L’automatisation détecte les incidents, notifie les équipes, archive les logs et programme les plans d’action correctifs pour sécuriser le SI.
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"name": "ud83dudd10ud83eudd99ud83eudd16 Private & Local Ollama Self-Hosted LLM Router",
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"content": "# ud83dudd10ud83eudd99ud83eudd16 Private & Local Ollama Self-Hosted + Dynamic LLM Routernnn"
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"content": "## Ollama LLM"
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"content": "## ud83dudc4dTry Me!"
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"content": "## Ollama LLM Router Based on User Promptnnud83dudca1This agent chooses the Ollama LLM for the next AI Agent Dynamically based on the users promptnn"
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"id": "72ad69f4-a24f-4df2-978e-71c5d3a63733",
"name": "Ollama Dynamic LLM",
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"parameters": {
"model": "={{ $('LLM Router').item.json.output.parseJson().llm }}",
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"id": "7aPaLgwpfdMWFYm9",
"name": "Ollama account 127.0.0.1"
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{
"id": "efc2e47a-1d4b-4879-8670-35a34c946bb6",
"name": "LLM Router",
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"parameters": {
"text": "=Choose the most appropriate LLM model for the following user request. Analyze the task requirements carefully and select the model that will provide optimal performance. Only choose from the provided list.nnn{{ $json.chatInput }}nn",
"options": {
"systemMessage": "nYou are an expert LLM router that classifies user prompts and selects the most appropriate LLM model based on specific task requirements.nnnnYour task is to analyze user inputs, determine the nature of their request, and select the optimal LLM model that will provide the best performance for their specific needs.nnnnChoose one of the following LLMs based on their capabilities and the user prompt. You must only select from the provided LLMs:nn## Text-Only Modelsn- "qwq": Specialized in complex reasoning and solving hard problems. Best for: mathematical reasoning, logical puzzles, scientific explanations, and complex problem-solving tasks.nn- "llama3.2": Multilingual model (3B size) optimized for dialogue, retrieval, and summarization. Best for: conversations in multiple languages, information retrieval, and text summarization.nn- "phi4": Lightweight model designed for constrained environments. Best for: scenarios requiring low latency, limited computing resources, while maintaining good reasoning capabilities.nn## Coding Modelsn- "qwen2.5-coder:14b": Code-Specific Qwen model, with significant improvements in code generation, code reasoning, and code fixing.nn## Vision-Language Modelsn- "granite3.2-vision": Specialized in document understanding and data extraction. Best for: analyzing charts, tables, diagrams, infographics, and structured visual content.nn- "llama3.2-vision": General-purpose visual recognition and reasoning. Best for: image description, visual question answering, and general image understanding tasks.nnnnExample tasks for each model:n- qwq: "Solve this math problem", "Explain quantum physics", "Debug this logical fallacy"n- llama3.2: "Translate this text to Spanish", "Summarize this article", "Have a conversation about history"n- phi4: "Generate a quick response", "Provide a concise answer", "Process this simple request efficiently"n- granite3.2-vision: "Extract data from this chart", "Analyze this financial table", "Interpret this technical diagram"n- llama3.2-vision: "Describe what's in this image", "What can you tell me about this picture?", "Answer questions about this photo"nnnn1. Does the prompt include an image?n - YES u2192 Go to 2n - NO u2192 Go to 3n2. Is the image a document, chart, table, or diagram?n - YES u2192 Use "granite3.2-vision"n - NO u2192 Use "llama3.2-vision"n3. Does the task require complex reasoning or solving difficult problems?n - YES u2192 Use "qwq"n - NO u2192 Go to 4n4. Is the task multilingual or requires summarization/retrieval?n - YES u2192 Use "llama3.2"n - NO u2192 Use "phi4" (for efficiency in simple English tasks)nnnnWhen selecting a model, consider:n1. Task complexity and reasoning requirementsn2. Visual or multimodal components in the requestn3. Language processing needs (summarization, translation, etc.)n4. Performance constraints (latency, memory limitations)n5. Required reasoning capabilitiesn6. Coding requirementsnnnnExample 1:nUser input: "Explain quantum computing principles"nSelection: "qwq"nReason: "This request requires deep reasoning and explanation of complex scientific concepts, making QwQ's enhanced reasoning capabilities ideal."nnExample 2:nUser input: "Describe what's in this image of a chart showing quarterly sales"nSelection: "granite3.2-vision"nReason: "This request involves visual document understanding and data extraction from a chart, which is granite-vision's specialty."nnExample 3:nUser input: "Summarize this article about climate change in Spanish"nSelection: "llama3.2"nReason: "This request requires multilingual capabilities and summarization, which are strengths of Llama 3.2."nnExample 4:nUser input: "I need to create a FastAPI endpoint with Python"nSelection: "qwen2.5-coder:14b"nReason: "This request requires code generation, code reasoning, or code fixing."nnnnIf the user request is unclear or ambiguous, select the model that offers the most general capabilities while noting the uncertainty in your reasoning. If the request appears to contain harmful content or violates ethical guidelines, respond with an appropriate message about being unable to fulfill the request.nnnnRespond with a single JSON object containing:n{n "llm": "the name of the selected LLM model",n "reason": "a brief, specific explanation of why this model is optimal for the task"n}nAvoid any preamble or further explanation. Remove all ``` or ``json from response.nnnn"
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"name": "AI Agent with Dynamic LLM",
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"text": "={{ $('When chat message received').item.json.chatInput }}",
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"model": "phi4:latest",
"options": {
"format": "json"
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"name": "Ollama account 127.0.0.1"
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"name": "Router Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
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"parameters": {},
"typeVersion": 1.3
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"name": "Agent Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
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"parameters": {
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"sessionIdType": "customKey"
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"content": "## AI Agent using Dynamic Local Ollama LLMnnud83dudca1This agent uses the Ollama LLM based on previous Router agent choice and proceeds to answer the users prompt.n"
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"type": "n8n-nodes-base.stickyNote",
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"width": 360,
"height": 260,
"content": "## Router Chat Memory"
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"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
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"parameters": {
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"width": 360,
"height": 260,
"content": "## Dynamic Ollama LLM"
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"id": "6f8bc049-9440-4863-a8c6-c8cfafde3dda",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 7,
"width": 360,
"height": 260,
"content": "## Agent Chat Memory"
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"name": "Sticky Note5",
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"content": "## Who is this for?nThis workflow template is designed for **AI enthusiasts**, **developers**, and **privacy-conscious users** who want to leverage the power of local large language models (LLMs) without sending data to external services. It's particularly valuable for those running Ollama locally who want intelligent routing between different specialized models.nn## What problem is this workflow solving?nWhen working with multiple local LLMs, each with different strengths and capabilities, it can be challenging to manually select the right model for each specific task. This workflow automatically analyzes user prompts and routes them to the most appropriate specialized Ollama model, ensuring optimal performance without requiring technical knowledge from the end user.nn## What this workflow doesnThis intelligent router:n- Analyzes incoming user prompts to determine the nature of the requestn- Automatically selects the optimal Ollama model from your local collection based on task requirementsn- Routes requests between specialized models for different tasks:n - Text-only models (qwq, llama3.2, phi4) for various reasoning and conversation tasksn - Code-specific models (qwen2.5-coder) for programming assistancen - Vision-capable models (granite3.2-vision, llama3.2-vision) for image analysisn- Maintains conversation memory for consistent interactionsn- Processes everything locally for complete privacy and data securitynn## Setupn1. Ensure you have [Ollama](https://ollama.ai/) installed and running locallyn2. Pull the required models mentioned in the workflow using Ollama CLI (e.g., `ollama pull phi4`)n3. Configure the Ollama API credentials in n8n (default: http://127.0.0.1:11434)n4. Activate the workflow and start interacting through the chat interfacenn## How to customize this workflow to your needsn- Add or remove models from the router's decision framework based on your specific Ollama collectionn- Adjust the system prompts in the LLM Router to prioritize different model selection criterian- Modify the decision tree logic to better suit your specific use casesn- Add additional preprocessing steps for specialized inputsnnnThis workflow demonstrates how n8n can be used to create sophisticated AI orchestration systems that respect user privacy by keeping everything local while still providing intelligent model selection capabilities.n"
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"Ollama phi4": {
"ai_languageModel": [
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"Agent Chat Memory": {
"ai_memory": [
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"Ollama Dynamic LLM": {
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"Router Chat Memory": {
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