I Built an AI Agent to Navigate My Notes—And It's a Glimpse into the Future

Have you ever had that nagging feeling… “I know I wrote this down somewhere”?

We’ve all been there. You have notes scattered across a dozen apps, project docs in one folder, research in another. It’s a digital junk drawer. Finding anything is a chore, and connecting ideas from different places? Forget about it.

For years, I’ve been obsessed with the idea of a “second brain”—a personal knowledge system that doesn’t just store information, but helps you connect it. What if your notes weren’t just a pile of disconnected files, but a web of interconnected ideas? A personal Wikipedia, custom-built for your brain.

Today, we’re going to build one. Not just a static web of notes, but a living, breathing knowledge graph that an AI agent can explore, understand, and reason with. We’re going beyond simple search and into the world of agentic RAG (Retrieval-Augmented Generation), where an AI doesn't just pull information for you, it navigates a complex web of knowledge like you.

Let's get our hands dirty.

First, We Build the Brain: From Flat Files to a Knowledge Graph

Before we can unleash an AI, we need to give it a world to live in. That world is our knowledge graph.

Think of it like this: every single note you write (in our case, a simple markdown file) is a "node" in the graph—like a city on a map. Every link you create between notes, whether it's a standard markdown link [like this](another-note) or a wiki-link [[like this]], becomes a "directed edge"—a road connecting two cities.

To make this happen, we started by building a simple but powerful KnowledgeGraph class in Python. This isn't some off-the-shelf library; it's a custom-built engine designed to do a few things really well:

1. Ingest Markdown: It can take a raw markdown file, find the title, and parse out all the headers and content.
2. Extract Links & Tags: It automatically finds every link to another note and every #tag, building the connections that form our graph.
3. Manage Backlinks: Crucially, it doesn't just know where a note links to; it also knows which other notes link back to it. This is huge for understanding context.

With this foundation, we have a system that treats our notes not as isolated documents, but as a cohesive network of ideas.

Feeding the Brain: A Realistic Developer Knowledge Base

An empty brain isn't very interesting. So, we populated our new knowledge graph with a realistic set of notes for a fictional web development project.

We created eight interconnected documents, starting with a main "Map of Content" called project-index. This note acted as the central hub, linking out to more specific topics:

• authentication
• database-design
• api-design
• frontend-stack
• deployment
• caching-strategies
• performance-notes

These notes weren't isolated. The authentication note linked to database-design, which in turn linked to performance-notes, which linked back to caching-strategies. Suddenly, we have a rich, interconnected web of technical knowledge, exactly like you’d build up over the course of a real project.

How a Human Navigates the Graph

Before letting an AI loose, it’s important to understand how we, as humans, would interact with this graph. We built a few core functions that mimic the command-line tools of a real knowledge management app called IWE.

• find(query): This is our fuzzy search. You can type find("performance") and it will instantly return a scored list of all relevant notes, like performance-notes and caching-strategies.
• tree(key): This gives you a bird's-eye view. Running tree("project-index") shows you a beautiful hierarchy of how all the project documents connect to the main hub.
• retrieve(key): This is more than just opening a file. When you retrieve a note, you can also pull in a little bit of context from the notes that link to it and the notes it links out to. It’s like opening a book and automatically getting a summary of the previous and next chapters.
• squash(key): This is a wild one. It takes a starting note and recursively combines it with every single note it links to, creating one massive, consolidated document. Super useful for exporting a whole topic.
• export_dot(): For the visual learners, this function exports the entire graph structure into a format that can be turned into an image. We used Graphviz to render it, and suddenly, our abstract network of notes became a tangible map we could actually see.

With these tools, we had a fully functional, human-navigable knowledge graph. But we were just getting started.

The First Spark of AI: Simple, Powerful Transformations

Now for the fun part. We wired up our knowledge graph to OpenAI. The first step was to create a set of simple "AI transforms"—small, focused tasks that an AI can perform on any single note.

We created a simple ai_transform function that could take any document's text and perform an action, like:

• Summarize: Condense a long technical document into a few key bullet points.
• Generate Links: Analyze a note and suggest other notes in our graph that should be linked to it.
• Extract To-Dos: Read a document and pull out any actionable tasks into a neat checklist.

We ran our authentication note through the summarizer and got a perfect, concise overview. We asked it to generate links, and it correctly suggested we should probably add a link to [[security-best-practices]]. We pointed it at the performance-notes, and it pulled out a checklist of optimization tasks.

This is already a huge step up from a static note-taking app. It's like having a brilliant assistant who can instantly organize and refine your thoughts.

The Main Event: Unleashing an AI Agent to Navigate Our Graph

This is where everything we've built comes together. We're going to create a true AI agent that can navigate our knowledge graph to answer complex questions.

Here’s how it works: We use OpenAI's "function calling" feature. In plain English, this means we give the AI a list of tools it's allowed to use. And what are those tools? The very same graph navigation functions we built earlier: iwe_find, iwe_retrieve, and iwe_tree.

We wrote a system prompt that essentially told the AI: "You are an assistant with access to a personal knowledge graph. Your job is to answer questions. Use the find tool to discover relevant notes, the retrieve tool to read them, and follow the links between them to build a complete picture. Cite your sources."

Then, we gave it a spin with a few questions.

Question 1: "How does our authentication system work, and what database tables does it depend on?"

The agent didn't just guess. We watched it work, step-by-step:

1. Agent calls: iwe_find(query='authentication')
2. It gets back a list of relevant documents, with authentication at the top.
3. Agent calls: iwe_retrieve(key='authentication', depth=1) (The depth=1 tells it to also fetch the content of any notes linked from the auth document).
4. By reading the retrieved text, it sees that the authentication flow depends on the user table, which is defined in the database-design note.
5. Agent synthesizes the answer: It combines the information from both documents into a perfect, comprehensive summary, explaining the JWT flow and explicitly mentioning the users and sessions tables from the other note.

A simple search could never have done this. The agent had to follow a link—a relationship—to connect the dots.

The AI as a Gardener: Finding Gaps and Planting Seeds

What if the AI could not only read our knowledge graph but also help us improve it?

We built two more functions for this. The first, analyze_knowledge_gaps, gives the AI a high-level overview of all the notes and their connections and asks it to act like a knowledge management consultant. It came back with amazing insights:

• "Missing Topic: The api-design note mentions error handling, but there's no dedicated document explaining the strategy. You should create an error-handling note."
• "Missing Link: The deployment note should probably link to the api-design note, since the CI/CD pipeline is deploying that API."

This is incredible. The AI is now actively helping us curate and grow our knowledge base.

So, we took its advice. We created a second function, generate_new_note, and told it: "Please write a new note about 'Error Handling Strategy', using the api-design and frontend-stack notes for context."

It generated a brand-new, high-quality markdown document, complete with headers, code examples, and even correctly formatted wiki-links to [[api-design]] and [[authentication]]. We added this new note to our graph, and just like that, our knowledge base got smarter, thanks to the AI's own suggestion.

The Final Test: True Multi-Hop Reasoning

To really push the agent, we asked it a final, much harder question—one that required it to synthesize information from across the entire graph:

"If we increase traffic from 1000 RPS to 5000 RPS, what changes would be needed across the entire stack—from database, to caching, to auth, to deployment?"

No single document has this answer. To figure it out, the agent had to perform a series of complex steps:

1. It started by finding and reading the performance-notes, which mentioned the 5000 RPS load testing target.
2. This note linked to database-design and caching-strategies. The agent followed these links.
3. From database-design, it learned about connection pooling.
4. From caching-strategies, it learned about Redis and cache invalidation.
5. It then likely searched for "deployment" and found the deployment note, learning about Kubernetes on GKE.
6. Finally, it synthesized all of this information into a single, cohesive plan, recommending specific changes to database connection pools, Redis cache capacity, and Kubernetes Horizontal Pod Autoscaler settings.

This is multi-hop reasoning. It’s the ability to traverse multiple nodes in a graph, pulling a piece of information from each one, and weaving them together to answer a question that is far greater than the sum of its parts.

This is the future of how we'll interact with our own information. By structuring our knowledge as a graph, we create a landscape that AI agents can explore on our behalf. We're moving from a world where we search for information to one where we have a conversation with it. And honestly, I can't wait to see where this road takes us.