Stories

I spent a year building agent memory on knowledge graphs. Here are my 5 mistakes

Article news.ycombinator.com

I spent a year building agent memory on knowledge graphs. Here are my 5 mistakes

I spent the past year building a unified memory layer for my AI agents using knowledge graphs and ontologies on top of MongoDB. I followed every trend first. I reached for the shiny frameworks and tried to design the perfect upfront ontology. I made basically every mistake possible.

Naive memory fails at scale. File search bloats the context window once memory gets big. Claude Code handles it this way out of the box. Even semantic search over history can't traverse the relationships between people, topics, objects, locations, and preferences. The fix was to stop treating memory as a retrieval problem and treat it as a *data-modeling problem*.

Here are the 5 mistakes I made:

1. I reached for frameworks first. I tried LangGraph and CrewAI. The moment I needed custom ontology constraints, immutable observation logs, composite IDs, and multi-hop traversal, I was fighting the framework. Lesson: Own the memory and the harness yourself because frameworks encode assumptions your system rarely matches.

2. I overthought the ontology. Knowing it's a data-modeling problem, I tried to design the perfect ontology upfront. This froze projects for months. Lesson: Ontology design is a data-exploration loop. Start with POLE+O (Person / Object / Location / Event / Organization) and extend only on collisions. For example, I had a run tagging "Claude Code" as a Person when it's an Object.

3. I confused resolution with deduplication. Naming is not identity. Confusing them corrupts the graph. Resolution normalizes names, while deduplication decides identity from the entities' context. Lesson: Use specific thresholds: ≥0.95 auto-merges, >0.85 triggers human review, and ≤0.85 creates a new node. This stops "Apple" the company from merging with "Apple" the fruit.

4. I only built short-term and long-term memory. The agent repeated failed strategies because I skipped reasoning memory. This is a trace per run including the strategy, tools used, and the success or failure. Lesson: Reasoning memory is like RL at the database layer instead of the weights. Honest caveat: it can backfire because bad traces reinforce bad strategies, and it's overkill for one-off tasks.

5. I tried to build an immutable log layer before materializing the graph into the database because it sounded fancy, as it adds versioning and temporality to the graph. The con is that it puts a ton of pressure on your VM's RAM, which is crazy expensive. Lesson: Do that ONLY if you really need it.

The schema decides everything about the system's performance. Settling on edges as first-class documents in MongoDB allowed for native `$graphLookup` and finally let the system scale. This approach avoids relationship duplication and makes writes much simpler.

If you want to understand the whole reasoning behind these mistakes supported by the system of my agentic memory via KG and ontologies, consider going over my latest 6 LinkedIn posts:

1. 3 ways to model your ontologies for GraphRAG → https://www.linkedin.com/feed/update/urn:li:share:7446856909179027456

2. LangGraph/CrewAI or from scratch? → https://www.linkedin.com/feed/update/urn:li:share:7449362677560221696

3. A year building GraphRAG from scratch → https://www.linkedin.com/feed/update/urn:li:share:7449366886603128833

4. The third memory type: reasoning memory → https://www.linkedin.com/feed/update/urn:li:share:7454454641939034113

5. Building a production-grade personal AI assistant → https://www.linkedin.com/feed/update/urn:li:share:7456973563858821120

6. Designing Your Agents' Unified Memory → https://www.linkedin.com/feed/update/urn:li:share:7464580605327060992

Discussion 0 comments · 3 points · pauliusztin · 2026-05-30
Open on HN
Loading the discussion…

Domain filters

Stories from these domains are hidden from every list. Subdomains match too: blocking substack.com also hides danluu.substack.com. Type a domain, or paste a link to block its site, and press Enter.

New collection

Delete this collection?

About YAVCHN

YAVCHN is a reader for Hacker News and Lobsters, with articles and discussions in separate windows or Classic pages.

Created by Paul Parks and built with PUDL.

YAVCHN source code on GitHub

Privacy policy · Terms of use

Help

Keyboard

j / k
Move down and up the story list. The arrow keys scroll whatever has focus.
Enter
Read the marked story in the article reader.
[ / ]
Read the story above or below in the list, in the same article reader. Back returns to the story you came from.
p
Pin or unpin the marked story, which keeps it in Pinned.
n / N
Move to the next or previous top-level comment in the window in front.
c
Collapse or expand that comment.
f
Hide or show the story list.
Esc
Close a menu or this help.
Access key m
Go to the menu bar. Most browsers take it with Alt on Windows and Linux, and Safari with Control and Option.
?
Show this help.

Windows

Each story opens in a window holding its article above its discussion; drag the bar between them to share the room differently. A window can be moved by its title bar, resized from any edge, snapped to a half or a corner by dragging it there, maximised, or minimised to the bar at the foot of the page. Use Window > New reader window to open an empty reader, or Story > Open in new reader window to open another reader for the current article. Docked readers keep their articles when you select another story from the sidebar. Minimized readers can be restored and reused for their site. A window's Previous and Next buttons read up and down the list in the same window.

A link in a comment or an article to another Hacker News or Lobsters thread opens that thread in a window too. A link to a single HN comment opens the comment above its replies.

While a story's window is in front, the Story and Discussion menus in the menu bar hold its commands: pinning, Previous and Next story, sorting, collapsing every thread, jumping to the first new comment. Each window also remembers where you were in its article and discussion, so a reload, or Back to a story that Next took you past, finds your place again. Closing a window forgets it.

The whole arrangement lives in the address, so a bookmark or a shared link brings it back, and Back undoes the last change. Moving between Hacker News, Lobsters, their lists, Pinned and Find changes only the list, and leaves the windows open.

The list

The pin at the start of a row keeps the story in Pinned, and the cross at its end hides it. Pinned can be narrowed by words in the title, site or author, by source, and to the stories you haven't opened yet, and ordered by when you pinned them, by points or by comments; the filters are part of the address, so a filtered view can be bookmarked. Scroll past the end of the list to load more. Domain filters, in the View menu, hide every story from a site.

Collections are named lists of stories. Story > Add to collection files the story in front into one or more of them, and the Collections feed shows them all or one at a time; the menu that chooses collections also creates, renames, and deletes them. A note is your own text on a story. Choose Add note in a story's toolbar to write one; it saves as you type. Rows with a note carry the note mark, and the Notes feed lists every noted story and searches the text of your notes.

Browsing view

View > Windowed and View > Classic select the browsing view and save your default in this browser. Window view reuses a reader for each feed. Classic view opens stories and applets as pages. Open as a page is a one-off action that does not change your saved default. Use the Windowed selector to return an article to a window. Direct page links always open as pages.

Applets

The Applets menu in the menu bar holds three tools, each a window of its own. Replies to me takes your Hacker News user name and lists the replies to your last thirty comments and stories, checking again every three minutes while it is open, and marking what is new since you last marked them read. Look up a user opens a profile on Hacker News or Lobsters, with their submissions and recent comments, as a commenter's name in any discussion does; the bar at the top of a profile looks up someone else in the same window, and Back returns to the one before. Who is hiring? filters the posts of HN's monthly hiring threads by the words you type.

They read only what the sites publish to everyone, so none of them asks for a login, and your user name stays in this browser anonymously and is included in retained applet state when signed in.

Find

Find takes any link and lists every time it was submitted to Hacker News and Lobsters, so you can read each discussion of it.

About

YAVCHN never sees your Hacker News or Lobsters login. The discussion is fetched from each site's public API; to vote or reply, follow the link above the discussion, or the arrow beside a comment, to the source's own site. Without a YAVCHN account, your data stays in this browser. When signed in, pins, collections, notes, blocked domains, and retained reading state are stored with your account and synchronized across devices. Hidden stories and layout stay in this browser. The privacy policy has the details.

Open source: github.com/paulmooreparks/yavchn. Built with PUDL.