Best Local LLMs for n8n, Koog, and LangChain Workflows (via Ollama):
As a developer building local AI workflows, choosing the right LLM makes all the difference—especially when balancing performance, memory, and use case fit.
Here are some top models that run smoothly on local hardware and integrate well with tools like n8n, LangChain, or Koog.
🧠1. Mistral (7B)
Why: Solid balance of speed and reasoning capabilities.
Use Cases: General-purpose workflows, chatbots, moderate complexity logic.
RAM: 12–16 GB
Integration: Works great with LangChain prompts or n8n HTTP nodes.
Local Storage: 4.4 GB
🧠1. Mistral (7B)
Why: Solid balance of speed and reasoning capabilities.
Use Cases: General-purpose workflows, chatbots, moderate complexity logic.
RAM: 12–16 GB
Integration: Works great with LangChain prompts or n8n HTTP nodes.
Local Storage: 4.4 GB
⚡2. Phi-3 Medium (7B)
Why: Extremely fast and lightweight; great for tight memory environments.
Use Cases: Intent classification, fast user input handling, micro-services.
RAM: 8 GB
Integration: Ideal for high-frequency calls in low-latency automation.
Local Storage: 3.0 GB (mini), 7.0 GB (medium)
🚀3. Command-R+ (7B)
Why: Purpose-built for RAG (Retrieval-Augmented Generation).
Use Cases: Document Q&A, vector DB queries, contextual chat.
RAM: 16–20 GB
Integration: Perfect if your LangChain/Koog stack pulls context before generation.
Local Storage: 17 GB
🧠4. GPT-OSS:20B
Why: High-quality open-weight model with chain-of-thought and tool usage support.
Use Cases: Advanced automation, agent-style pipelines, deep logic chains.
RAM: 16–18 GB (slower but more capable)
Integration: Ideal for mission-critical or complex decision-making workflows.
Local Storage: 13 GB
💡These models are all available via Ollama, making local LLM workflows fast, private, and scalable. Whether you're automating business logic or prototyping AI agents, there's a model here that fits your stack and hardware.
Why: Extremely fast and lightweight; great for tight memory environments.
Use Cases: Intent classification, fast user input handling, micro-services.
RAM: 8 GB
Integration: Ideal for high-frequency calls in low-latency automation.
Local Storage: 3.0 GB (mini), 7.0 GB (medium)
🚀3. Command-R+ (7B)
Why: Purpose-built for RAG (Retrieval-Augmented Generation).
Use Cases: Document Q&A, vector DB queries, contextual chat.
RAM: 16–20 GB
Integration: Perfect if your LangChain/Koog stack pulls context before generation.
Local Storage: 17 GB
🧠4. GPT-OSS:20B
Why: High-quality open-weight model with chain-of-thought and tool usage support.
Use Cases: Advanced automation, agent-style pipelines, deep logic chains.
RAM: 16–18 GB (slower but more capable)
Integration: Ideal for mission-critical or complex decision-making workflows.
Local Storage: 13 GB
💡These models are all available via Ollama, making local LLM workflows fast, private, and scalable. Whether you're automating business logic or prototyping AI agents, there's a model here that fits your stack and hardware.
