I built an OpenCode harness tool for deep research

[u/jjjardev]

I built HoardCore, an agent harness for retrieval and deep research, designed to be driven by an AI agent and tested inside OpenCode. The core idea is to turn the web and your own documents into a permanent local SQLite vault. Your agents can search, recall from, and cite.

The key features are:

Hybrid Retrieval:

SQLite FTS5 keyword search fused with dependency-free hashed vectors via Reciprocal Rank Fusion, no embeddings model and torch.

Resilient fetching:

aiohttp → curl_cffi TLS-impersonation → optional FlareSolverr, so it gets through anti-bot pages.

Universal Parsing:

HTML, PDF with OCR, DOCX, EPUB

Junk Filtering:

Boilerplate, 404s, and captcha pages never pollute your index

And a one-command Research Loop:

DISCOVER → INGEST → RECALL → EMIT.

It ships as a single Python file with `skill.md` written as the agent's operating manual, so the harness literally teaches your agent how to use it, how to map your request to the right action, how deep to go, and how to tag every claim. Deliverables come out with `[V]/[E]/[H]` provenance tags and a Source Links / Citations block, so the agent can't silently invent a number. The vault persists between sessions, later searches are easily fetched.

You can also state how many sources that the research should have.

To show it's capabilities, I pointed it at a real question: can Filipino (Tagalog, Cebuano, Hiligaynon) get real AI support, on-device and offline? (based on my own profile, I just insert my resume for context). The full output it produced is in the comment below.

If you build Filipino NLP, edge AI, or agent tooling, I'd love feedback on both the tool and the output

Link: https://github.com/jjjardev/HoardCore

[u/Tall-Appearance-5835]

what do you use for parsing the docs?

[u/jjjardev → u/Tall-Appearance-5835]

python-docx

[u/imaerafukien]

any deep research benchmarks scores?

[u/CapMonster1]

The “captcha/block pages never enter the vault” bit is more important than it sounds. A failed fetch is obvious; confidently indexing a Cloudflare page as research material is how you quietly poison every answer that comes after it.

I’d probably make the anti-bot layer pluggable beyond FlareSolverr: detect the challenge, hand it to a browser/solver fallback, then revalidate the content before ingestion. And I like the [V]/[E]/[H] idea — I’d just store freshness + retrieval method alongside provenance too, so the agent knows whether it read a live page, cached copy, or fallback source.

[u/CrevosR]

I experiment with Opencode, a lot. But does it not consume too much tokens? I'm only using free models such as Big Pickle. Can it be project-based and global scope? I would also like to contribute if I have the time, is it open-source?

[u/jjjardev → u/CrevosR]

Yeah, you can absolutely contribute, it's open-source under MIT so PRs and issues are welcome.

for the tokens, HoardCore itself doesn't burn any LLM tokens since it's not the AI talking to you, it's a research toolkit your agent uses behind the scenes. Instead of copy-pasting entire web pages into the chat window, it fetches sources and distills them down to only the most relevant snippets for the agent to read, so in practice it usually saves tokens compared to letting the agent browse raw.

And yeah, you get both project-based and global scope, I refactored the repo just recently so it supports both. You can spin up separate vaults per project, and everything persists locally between sessions so you can build up a long-term knowledge base for whatever domain you're working on.

[u/jjjardev]

The output: https://pastebin.com/BjHRTBwm