5 Free Tools That Make Local AI Actually Useful

5 free tools that make local AI useful: llama.cpp, Open WebUI, Docling, Open Terminal, OpenCode. Run models locally, extract documents, execute code, edit repositories. Already running a local model? You don't need a fresh set of weights to connect more apps. In documented setups, one llama.cpp server exposes an OpenAI-compatible endpoint that both Open WebUI and OpenCode can use. This video maps five free tools to five capabilities: llama.cpp serves your model locally, Open WebUI handles document-grounded answers in a persistent workspace, Docling keeps table structure and reading order and reads scanned pages, Open Terminal runs code and writes files you can inspect, and OpenCode applies changes to your repository. The running example is a hypothetical supplier quote, an illustration of documented features rather than a benchmark or a test run. The Stack did not run these tools for this video: the quote, code and files on screen are illustrative, the documentation and license text are the projects' own, and the app screens are rebuilt from their real screenshots. Along the way: OpenAI-compatible means the message format, not OpenAI's cloud. Retrieval only finds text that extraction kept, so a flattened table can lead a model to multiply a one-off setup fee by twelve. A CSV holds raw values, not formulas, so check it against a total you already know. And a passing test suite protects you only if a test checks the math. The limits: free means free to use under each tool's license. llama.cpp, Open Terminal, OpenCode and Docling's code are MIT; Open WebUI has its own license with branding conditions; Docling's model weights carry their own licenses. You still supply the machine, the power and the model weights. A local chat model does not make your document pipeline private or offline by itself: extraction and the embedding model can route to separate services, and offline flags alone do not block outbound connections. Running Open Terminal in Docker is not automatic isolation, because mounting the host's Docker socket gives the container control of your host's Docker. OpenCode's permission rules are policy, not a sandbox. And small models often fail at multi-step tool loops even when they can emit a valid tool call. The answer: keep the setup that already works and add only the one missing capability. Serve an endpoint with llama.cpp, organize document chats in Open WebUI, recover messy layouts with Docling, run calculations and file tasks in Open Terminal, or direct local coding with OpenCode. No tool makes an underpowered model reliable, so start with one recurring task whose result you already know, and inspect that output before you expand your setup. Chapters: 0:00 Intro 1:10 llama.cpp 2:31 Open WebUI 4:36 Docling 6:38 Open Terminal 9:28 OpenCode 12:28 Conclusion Tools & resources mentioned: - llama.cpp: https://github.com/ggml-org/llama.cpp - Open WebUI: https://github.com/open-webui/open-webui - Docling: https://github.com/docling-project/docling - Open Terminal: https://github.com/open-webui/open-terminal - OpenCode: https://github.com/anomalyco/opencode About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #local ai #open webui #free ai tools #local llm #opencode