Yesterday a solo dev dropped a tool called HoardCore on r/PromptEngineering, and it’s the kind of build that fixes a problem most of us just learned to live with: agents that make up facts and hand them to you like they’re gospel. You ask a research agent a question, it comes back confident, cites nothing, and you’re left doing the fact check yourself anyway. That defeats the whole point of delegating the research in the first place.
Here’s what’s new. HoardCore is a single Python file that plugs into agent harnesses like OpenCode. Point it at the web or your own files, and it builds a local SQLite vault. No cloud database, no embeddings model, no torch install eating your disk. That’s the part worth sitting with: most “agent memory” tools these days assume you want a vector database spinning in Docker somewhere, or a hosted service with its own bill. HoardCore skips all of that. One file, one database, runs on a laptop with nothing fancy installed. The agent searches that vault instead of freestyling, and every claim it pulls back comes stapled to a source link, so when it tells you something, you can click through and check it yourself in two seconds instead of five minutes.
The twist is where this gets good. HoardCore ships with a skill.md file that acts as the agent’s operating manual before it touches the web. That file teaches it to tag every claim as [V] Verified, [E] Extracted, or [H] Hypothesis, and the protocol forces the agent to go back and confirm a [V] tag against the vault before it presents it to you. It literally can’t sneak in an invented number without the tag system catching it. Think of it like a citation style guide, except the agent is graded on it in real time. If it wants to call something Verified, it has to point at the exact row in the vault that backs it up, and if that row doesn’t exist, the claim gets demoted to Hypothesis automatically.
How it actually runs, step by step:
- 🔍 DISCOVER: set your source budget with –discover N, and it goes and finds sources. You control the ceiling, so a quick sanity check might be N=5 while a real strategic brief might run N=20 or higher, and the tool respects that budget instead of scraping the entire internet by default.
- 📥 INGEST: parses HTML, PDF (with OCR fallback), DOCX, EPUB, and filters out boilerplate and captcha junk before indexing. That OCR fallback matters more than it sounds. A lot of research tools choke the second they hit a scanned PDF or an image-based report, and HoardCore just runs it through OCR and keeps moving instead of silently dropping the source.
- 🧠 RECALL: hybrid search over the vault, FTS5 for exact keywords plus hashed vectors for near matches, merged with reciprocal rank fusion. That combo is doing real work: FTS5 catches the exact phrase you typed, hashed vectors catch the paraphrase you didn’t, and reciprocal rank fusion blends the two rankings so you get the best of both instead of picking one and losing the other.
- 📝 EMIT: writes your brief with the [V]/[E]/[H] tags attached to every claim, so the final document reads less like a polished essay hiding its sourcing and more like a lab notebook you can actually audit line by line.
The builder ran a live test on whether on-device LLM inference is actually production-ready in 2026. Eight discovery and recall passes, eighteen sources ingested, full strategic brief with citations. Total cost: $0.0074 running on DeepSeek V4 Flash. That’s not a typo, that’s under a cent for a brief that would normally take a human analyst an afternoon to assemble, and it came with receipts attached to every claim instead of a summary you have to take on faith.
Pro tip: the vault persists between sessions, so once you’ve built it out on a topic, your next round of questions on that same subject runs instant and offline. No repeat API spend for follow-up digging. That means if you’re tracking something ongoing, like a competitor’s product roadmap or a fast-moving regulatory story, you can keep appending to the same vault over weeks and every new question benefits from everything you already ingested, instead of starting the research from zero each time you check back in. Set up one vault per client or per project, and by the third or fourth session the agent is answering from a knowledge base that’s basically yours now, built for pennies instead of a subscription.
If you’re running research agents and tired of fact-checking their homework, go dig through the repo: github.com/jjjardev/HoardCore 🦜
I built an OpenCode toolkit that make AI agents to cite every claim, and is cheap to run
by u/jjjardev in PromptEngineering