> ## Content Index > Fetch the complete content index at: https://blog.meganova.ai/llms.txt > Use this file to discover other available public pages before exploring further. # 7 AI Applications You Can Build with LibraOS: From Personal Knowledge Assistants to Workflow Automation - URL: https://blog.meganova.ai/7-ai-applications-you-can-build-with-libraos-from-personal-knowledge-assistants-to-workflow-automation/ - Published: 2026-10-30T05:14:19.000Z - Updated: 2026-10-30T05:14:19.000Z - Author: Tracy Giang The LibraOS Runtime runs the agents, memory, knowledge and tools inside your app. What you build on top of it is up to you, but it helps to see concrete starting points. The seven applications below come from two public places: the app ideas and interactive concepts on the [LibraOS homepage](https://libraos.com/?ref=blog.meganova.ai), and the runnable examples in the [LibraOS Cookbook](https://github.com/libraos/cookbook?ref=blog.meganova.ai). The homepage concepts, which also include Investment OS and Legal OS, use sample data and fictional records; they show interaction patterns, not a connected AI service. For each application, you'll see what it does, which parts of the Runtime it uses, and where to start. ## **1\. Personal knowledge assistant** An assistant that answers questions from your own documents and carries useful context across sessions, so you don't repeat yourself. It's the first app idea on the LibraOS homepage, and the one with running code: the Personal Assistant starter on the [Developers page](https://libraos.com/developers/?ref=blog.meganova.ai), and a complete signed-in version in the Cookbook where a user uploads documents, chats and resumes past conversations. **Runtime parts:** Knowledge, Memory, Agents. ## **2\. Support copilot** A copilot for a support team that reads the ticket, gathers context and drafts a reply for a person to send. The homepage's Customer Desk concept shows the pattern with sample data: for a delayed replacement shipment, the assistant uses the order snapshot and shipping policy as context, drafts an update, and keeps any credit or refund behind approval. A person reviews the evidence before anything changes. **Runtime parts:** Knowledge, Agents and tools, Control. ## **3\. Research workspace** A workspace where an agent gathers material from your connected sources and helps you work through it: summarizing, comparing and pulling out what matters for a question. Research is one of the three app ideas on the homepage. The key design choice is the source boundary: decide which collections the agent may search, and have it say plainly when the sources don't cover the question. **Runtime parts:** Knowledge, Agents, Memory. ## **4\. Policy Q&A over your documents** An internal copilot that answers questions from a set of policies, such as an employee handbook, and cites the source for each answer. When the policies don't cover a question, it says so instead of inventing one. The Cookbook's [Document Q&A recipe](https://github.com/libraos/cookbook/tree/main/recipes/document-qa?ref=blog.meganova.ai) builds this with an agent bound to a single knowledge collection, and has been tested on Runtime v0.2.2. **Runtime parts:** Knowledge, Agents. ## **5\. Ticket routing** A classifier that reads incoming support tickets and returns a typed verdict, such as a category plus the reasoning behind it, that your code uses to route the ticket. The Runtime validates every reply against a schema before it leaves, so your routing rule never parses free text. The Cookbook's [ticket-routing recipe](https://github.com/libraos/cookbook/tree/main/recipes/ticket-routing?ref=blog.meganova.ai) includes a small labelled set and an accuracy check to run before live traffic. **Runtime parts:** Agents with structured output. ## **6\. Contract review** An assistant that extracts specific clauses from a contract into a structured result, and calls your own service to look up related precedents. The Cookbook's [legaltech example](https://github.com/libraos/cookbook/tree/main/recipes/legaltech?ref=blog.meganova.ai) combines structured output with a custom tool your service answers over a signed webhook, using a synthetic contract. The homepage also includes a Legal OS concept built on sample data. In any legal setting, the output supports a lawyer's review; it isn't legal advice. **Runtime parts:** Agents with structured output, tools, Knowledge. ## **7\. Workflow automation with an agent team** One request becomes organized work across several agents. In the Cookbook's [Digital Marketing Team app](https://github.com/libraos/cookbook/tree/main/apps/digital-marketing-team?ref=blog.meganova.ai), a request to a Head of Marketing employee turns into a plan of tasks with dependencies, specialists that hand structured results to each other, a pause when a real decision needs a person, and one report with its sources and contributors. This is the most advanced example, and the earliest. Today the mission engine runs this marketing team only; loading your own mission templates is proposed, not yet available. It also needs a model that returns structured JSON reliably. **Runtime parts:** Agents, tools, Knowledge, Control. ## **At a glance** | Application | Agents & tools | Memory | Knowledge | Control | Where to start | | ---------------------------- | -------------- | --------------------- | ------------------- | ----------------------------------- | -------------- | | Personal knowledge assistant | ✓ | ✓ | ✓ | Personal Assistant starter | | | Support copilot | ✓ | ✓ | ✓ | Customer Desk concept (sample data) | | | Research workspace | ✓ | ✓ | ✓ | Homepage app idea | | | Policy Q&A | ✓ | ✓ | Document Q&A recipe | | | | Ticket routing | ✓ | Ticket-routing recipe | | | | | Contract review | ✓ | ✓ | Legaltech example | | | | Workflow automation | ✓ | ✓ | ✓ | Digital Marketing Team app | | A pattern shows up: the closer an application gets to changing something in the real world, a refund, a status, a message, the more it needs Control. Applications that only read and answer can start without approval points; applications that act should not. ## **Before you build** - **Concepts aren't products.** The homepage concepts use sample data and fictional records; they aren't connected to a live AI service. Treat them as patterns to adapt, not features to switch on. - **Check the example's status.** Some Cookbook examples are tested on Runtime v0.2.2; others have protocol tests only. Each example's README says which. - **Controls apply where the work runs.** Tasks inside the Runtime get its permissions, approvals and audit. Tools and network calls outside it need their own enforcement. - **Pick your deployment early.** Each of these can run self-hosted, on LibraOS Cloud (private beta), or air-gapped with local models, but where data must sit shapes the rest of the design. ## **Conclusion** From a personal knowledge assistant to a team of agents running a mission, these seven applications share the same building blocks: agents and tools, memory, knowledge and control. Start with the one closest to your use case, use its concept or Cookbook example as the first draft, and add Control before the application is allowed to change anything. > **Explore LibraOS:** [**libraos.com**](https://libraos.com/?ref=blog.meganova.ai) ## Sources - [LibraOS homepage: app ideas and interactive concepts](https://libraos.com/?ref=blog.meganova.ai) - [Developers: Personal Assistant starter](https://libraos.com/developers/?ref=blog.meganova.ai) - [LibraOS Cookbook: recipes and apps](https://github.com/libraos/cookbook?ref=blog.meganova.ai) - [Security & Private Deployment](https://libraos.com/security/?ref=blog.meganova.ai) ### **What’s Next?** [**Sign up**](https://console.meganova.ai/register?referral=e0602d28&utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fmn%5Flaunch&utm%5Fcontent=body%5Flink&ref=blog.meganova.ai) and explore now. 🔍 **Learn more:** Visit [**our blog**](https://blog.meganova.ai/?utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fmn%5Flaunch&utm%5Fcontent=body%5Flink) and [**documents**](https://docs.meganova.ai/?utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fmn%5Flaunch&utm%5Fcontent=body%5Flink) for more insights or [contact us](https://www.meganova.ai/contact-us?utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fmn%5Flaunch&utm%5Fcontent=body%5Flink) for a demo of how MegaNova can power your AI products. 📬 **Get in touch**: Join our [Discord community](https://discord.com/invite/QBTnGurDw4?utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fai%5Flaunch&utm%5Fcontent=body%5Flink) for help or [Contact Us](https://www.meganova.ai/contact-us?utm%5Fsource=blog&utm%5Fmedium=referral&utm%5Fcampaign=blog%5Fmn%5Flaunch&utm%5Fcontent=body%5Flink).