AI Lab app store

Explore local app candidates.

These curriculum tools publish container information and can be considered for AI Lab. Listing is not approval: installable apps are pinned, reviewed, tested, and bundled with an AI Lab release.

Catalogue JSON
11catalogue candidates

runtime · free

Ollama

Ollama runs open large language models locally from the command line and exposes them through an OpenAI-compatible API on localhost:11434. Free and open source, it handles model download, quantization, and GPU acceleration for you, and runs as a long-lived service — a private endpoint your scripts, apps, or whole lab can call without sending data to the cloud or paying per token. It carries a curated model registry plus any GGUF model. For a biology student it is the way to give analysis scripts a local LLM: point your Python at localhost and have it summarize results or draft figure captions, fully offline.

Self-hostedOpen sourceDocker

app · free

n8n

n8n is an open-source, node-based workflow automation platform that lets you chain APIs, AI models, databases, and files into working pipelines — without writing a backend. Each "node" is a building block: an HTTP request, a code block, an AI call, an IF-branch, a file writer. Chains of nodes become workflows. n8n is the backbone of this entire course: you will connect PubMed APIs, call LLMs, parse XML, route messages via Mattermost, and build multi-stage research pipelines — all inside its visual canvas. Unlike tools that wrap AI features for non-technical users, n8n exposes the full plumbing: you see every API call, every error, every output. That transparency is deliberate — it trains real systems thinking. First released in 2019 by Jan Oberhauser; v1.0 in 2023; fair-code licensed (free self-host, cloud from $20/mo).

cloudSelf-hostedOpen sourceDocker

app · free

Langflow

Langflow is a free, open-source visual tool for building AI applications without writing much code. You drag pre-made blocks — an LLM, a document store, a chat input, an agent, a web-fetch tool — onto a canvas and wire them to design how information flows. It's a friendly front end over Python and LangChain: every block is real Python you can open and edit, but beginners never have to. A built-in Playground lets you chat with your flow and watch the agent reason and pick tools in real time. Typical builds are RAG chatbots that answer questions from your own PDFs or a website, and multi-step agents. Finished flows export as an API or MCP server, so a scientist could turn a literature-Q&A bot or a data-lookup assistant into something a whole lab can use.

Self-hostedOpen sourceDocker

app · free

Hermes

Hermes Agent is a self-hosted, open-source autonomous AI agent from Nous Research — "the agent that grows with you." Unlike a chat window you start fresh each time, Hermes keeps a persistent memory of your preferences, projects, and environment across every session, so you stop re-explaining context. When it solves a hard problem it writes a reusable "skill" document (compatible with the agentskills.io standard) so it never forgets how to do it again. You reach it through the messaging apps you already use — Telegram, Discord, Slack, WhatsApp, Signal — or the CLI, all from one gateway process, and a built-in cron scheduler lets it run unattended tasks (daily digests, nightly backups, weekly audits) described in plain language. It runs on local models for full privacy or cloud models for more power, and deploys six ways (local, Docker, SSH, Singularity, and the serverless Modal/Daytona backends that hibernate when idle). For a researcher it is the tool when you want a private, always-on assistant that accumulates context about your work instead of forgetting it. (Note: distinct from the Hermes large language models, also by Nous Research — this is the agent that runs on top of a model.)

Self-hostedOpen sourceDocker

app · free

Gitea

Gitea is a self-hosted alternative to GitHub: you install it on your own server (or a cheap VPS) and get the same core workflow — git repos, pull requests, issue tracking, code review — without a single commit leaving your own infrastructure. It ships as one lightweight Go binary or Docker image, so it runs comfortably on hardware that GitHub-scale tools would never fit on. Gitea Actions gives you GitHub-Actions-compatible CI/CD, so workflow files mostly copy over unchanged, meaning you can run AI coding agents, test suites, and deploy pipelines entirely on infrastructure you control. It also bundles a package registry (npm, PyPI, Docker images, and more) so a lab or small team can host code, packages, and CI in one place. For biology students, the practical case is control: unpublished thesis code, sensitive datasets, or a pre-print analysis pipeline stay on a server you or your institution owns, not a third-party cloud.

Self-hostedCommercialDocker

app · free-tier

Flowise

Flowise is an open-source, visual tool for building AI agents and chatbots without writing code. Instead of programming, you drag boxes called "nodes" onto a canvas and connect them — one node for a language model, one for your documents, one for a search tool — so you literally see how information flows. A non-programmer can assemble a working assistant: e.g. a chatbot that answers questions from your own PDFs of lab protocols or papers (a technique called RAG, retrieval-augmented generation). You build and test in the browser, then publish the agent as a chat widget you embed on a site or call from other software. It sits between hand-written code and rigid pre-made apps — more flexible than a fixed chatbot builder, far gentler than coding an agent from scratch. Supports 100+ models, vector databases, and tools.

Self-hostedOpen sourceDocker

app · free

AnythingLLM

AnythingLLM (by Mintplex Labs) is an all-in-one, private AI workspace: drop in PDFs, documents, or a whole folder and chat with them — with citations — in a familiar ChatGPT-style UI. It is provider-agnostic, so you can run a fully local model (built-in, or via Ollama / LM Studio) or bring your own cloud key (OpenAI, Anthropic, Gemini). When you point it at a local model, nothing leaves your machine. It ships as a one-click desktop app for Mac/Windows/Linux and as a Docker server you can self-host as a shared, always-on team endpoint that exposes an OpenAI-compatible API. Its angle, versus Ollama / LM Studio / Jan, is document-RAG and AI agents bundled in — those tools run models; AnythingLLM turns a model into a private assistant over your own knowledge. Open source under MIT.

cloudSelf-hostedOpen sourceDocker