Hey Product Hunt — I'm the maker of Reactor.
I built this because a research question still lives in six gardens. Reddit threads, X, YouTube transcripts, HN, plus Bilibili / Xiaohongshu / V2EX for CN topics. Search APIs mostly don't see them. Chat products each cover one fence.
Reactor is open source and self-hosted. You give a goal; it plans, calls tools, checks results, and revises on failure. Vague ask → it stops and asks. Plan Mode: until you approve, it may only research and write the plan — no DB writes, no code, no report. Hard tasks split to sub-agents (wait, or background).
Tools: web search, RAG, sandbox code, charts, docs. NL2SQL uses Table RAG, then previews SQL. Collectors run in parallel so community threads and public pages share one citation list. Private PDF/Word/PPT/images are parsed/OCR'd and cited the same way. MCP tools stay as names until the agent asks for a schema.
Memory is not the whole chat dumped into the prompt. Facts inject every turn; history is retrieved on demand; reusable workflows live as SKILL.md.
Output lands in the thread (charts, tables, canvas) or exports to PDF/Word/PPTX/HTML.
What I actually use it for:
1. Brief before a meeting — last ~30 days of public posts, cited
2. Data analysis / a simple backtest
3. Competitor teardown with high-vote comments
4. Trip notes from Xiaohongshu/Bilibili
Stack: Java 21 + Spring AI harness, Python sidecar, React workbench. Docker Compose in the repo. Bring your own LLM endpoint (local or remote).
Repo: https://github.com/OWWZO/ai-agent
Would love honest takes on Plan Mode and the Java/Python split — not looking for upvotes, looking for "this part is wrong."
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