Local AI Stack Setup

help me do this: https://www.xda-developers.com/added-these-mcp-servers-local-llm-stack-one-replaces-paid-tool/

That article is basically describing a modern local AI stack built around: - a local LLM runner like urlOllamahttps://ollama.com or urlLM Studiohttps://lmstudio.ai - MCP (Model Context Protocol) servers - a coding/chat client that can use MCP tools - optional memory + search layers MCP is becoming the “USB-C for AI tools,” where the same tools work across different models and apps. citeturn0search1turn0search7 Given your background with Ollama, agent orchestration, and local-first workflows, you can absolutely build this stack. # Recommended Stack For You This is the setup I’d recommend specifically for your workflow: | Layer | Recommendation | Why | |---|---|---| | Local model runtime | urlOllamahttps://ollama.com | Easiest local serving | | Main coding client | urlOpen WebUIhttps://openwebui.com or urlContinue.devhttps://continue.dev | Good local workflows | | MCP support | urlClaude Desktop MCP docshttps://modelcontextprotocol.io style config | Standard ecosystem | | Code editing | urlVS Codehttps://code.visualstudio.com + Continue | Best MCP/dev experience | | Memory system | urlMnemo Cortexhttps://github.com/guymanndude/mnemo | Persistent semantic memory | | Search | Brave Search MCP or Tavily MCP | Replaces Perplexity-style workflows | | Filesystem tools | Filesystem MCP server | Lets the model read/write projects | | Git tools | Git MCP server | Lets agents inspect repos | | Browser automation | Playwright MCP | Browser actions locally | The big win is that this replaces multiple paid subscriptions with one local stack. citeturn0search4turn0search1 # What You Should Install First ## 1. Install Ollama ### Mac/Linux: ```bash curl -fsSL https://ollama.com/install.sh | sh ``` ### Pull models Start with: ```bash ollama pull qwen3:14b ollama pull deepseek-coder-v2 ollama pull nomic-embed-text ``` For your coding workflows: - Qwen3 is excellent - DeepSeek Coder is strong for repos - embedding model helps memory/search --- ## 2. Install Open WebUI This gives you a ChatGPT-like interface locally. ```bash docker run -d \ -p 3000:8080 \ --add-host=host.docker.internal:host-gateway \ -v open-webui:/app/backend/data \ --name open-webui \ ghcr.io/open-webui/open-webui:main ``` Then open: ```text http://localhost:3000 ``` --- ## 3. Install Continue.dev in VS Code Install: urlContinue.devhttps://continue.dev Then connect it to Ollama. Example `config.yaml`: ```yaml models: - title: Qwen3 provider: ollama model: qwen3:14b ``` This gives: - local autocomplete - local chat - repo understanding - MCP integrations --- # Add MCP Servers This is the important part. MCP servers expose tools to your model. Examples: - filesystem access - git - browser automation - memory - web search - databases ## Install the Filesystem MCP ```bash npm install -g @modelcontextprotocol/server-filesystem ``` ## Example MCP Config Create: ```json { "mcpServers": { "filesystem": { "command": "npx", "args": [ "@modelcontextprotocol/server-filesystem", "/Users/YOURNAME/projects" ] } } } ``` Now your model can: - read files - write files - inspect repos - refactor code --- # Add Browser Automation This is one of the most powerful additions. Install Playwright MCP: ```bash npm install -g @playwright/mcp ``` This lets your local model: - browse sites - scrape pages - test apps - automate workflows XDA specifically mentions replacing paid AI browser tooling with local MCP/browser workflows. citeturn0search4turn0search1 --- # Add Persistent AI Memory You would probably love this part. Install: urlMnemo Cortexhttps://github.com/guymanndude/mnemo It gives: - cross-session memory - semantic search - persistent agent knowledge - shared memory between agents This is especially useful for your persona systems and journaling systems. citeturn0search6turn0search5 --- # Best Models For MCP + Tool Calling Small models often struggle with tool calling. citeturn0reddit15turn0search5 Good local choices: | Model | Use | |---|---| | Qwen3 14B | Best overall local agent | | DeepSeek Coder V2 | Coding | | Gemma 3 27B | Strong reasoning | | Mistral Small | Fast tool use | | Phi-4 | Lightweight | For your GPU goals: - 4070 = excellent for 14B models - current setup can still run 7B models well --- # Your Ideal Architecture You’re already thinking in agent graphs and orchestration systems, so this architecture fits your existing projects very naturally: ```text VS Code + Continue ↓ Ollama ↓ MCP Layer ↙ ↓ ↘ Files Browser Memory Git Search SQLite ``` --- # What This Lets You Build This stack is extremely close to what you’ve been experimenting with already: - autonomous coding agents - persona orchestration - local RAG systems - AI journaling - repo-aware assistants - browser agents - memory-driven workflows - multi-agent systems It also aligns heavily with your: - local-first philosophy - anti-dependency approach - Ollama usage - SmolAgents/network orchestration ideas --- # What I’d Personally Prioritize First Order matters here. ## Phase 1 Get: - Ollama - Open WebUI - Continue.dev working together. --- ## Phase 2 Add: - filesystem MCP - git MCP --- ## Phase 3 Add: - Playwright MCP - Mnemo Cortex --- ## Phase 4 Build your own MCP servers. You already have enough background to make: - persona MCP servers - economic analysis MCPs - Reddit scraping MCPs - journaling MCPs - agent graph MCPs That’s where things become genuinely powerful.