From Local LLMs to Production Agents: A Complete LangChain & LangGraph Journey with Ollama
Over the next few weeks, we're launching a video series that takes you end-to-end from "I just installed Ollama" to "I can design and ship serious, graph-based AI agents using LangChain and LangGraph, entirely on my own machine." Who This Is For This journey is designed for: - Developers and data practitioners who want hands-on experience building agentic AI systems - Startups prototyping AI features before committing to expensive API costs - Enterprise teams with data privacy requirements who need local-first solutions - Tech leaders and researchers who want to deeply understand agent architectures without being locked into hosted models Prerequisites Before starting, you should have: - Basic Python knowledge: functions, classes, pip, virtual environments - API/JSON familiarity: understanding request/response patterns - Terminal comfort: running commands, navigating directories - Hardware: 16GB+ RAM recommended; GPU optional but helpful for larger models What This Journey Covers Across the series, we'll walk through five major phases. Each phase ends with tangible projects you can run locally and adapt to your own use cases. --------------------------------------------------------------- Phase 0 – Local Stack: Ollama + Python Estimated time: 1-2 hours We begin by setting up a local AI sandbox: - Installing and configuring Ollama - Pulling and running popular open models (e.g., Llama, Mistral) - Understanding hardware requirements and model selection for different tasks - Creating a clean Python environment and wiring up a minimal script to talk to a local LLM By the end, you'll have a lightweight local playground where you can experiment without API keys or usage limits. Troubleshooting covered: Model selection guide, memory optimization, when local models shine vs. their limitations --------------------------------------------------------------- Phase 1 – LangChain Fundamentals with Local Models Estimated time: 3-4 hours Next, we introduce LangChain as the "capabilities layer" on top of your local model: