TrendR
Trend Research — Automated literature review + platform trend monitoring + Obsidian knowledge management.
4 Agents · 8 Skills · 9-source search · 9-platform trends · Basic / Full install
Tell your agent one sentence. TrendR handles the rest.
You: "Survey the latest advances in agentic RAG 2025"
TrendR:
→ 9-source parallel search, 81 candidate papers found
→ Deep-read 11 papers, structured notes + comparison matrix
→ 14KB literature review (taxonomy, gap analysis, BibTeX)
→ Auto-archived to Obsidian, paper pool persisted
→ Notifies you: Done ✅
Inspired by karpathy/autoresearch — redesigned from “LLM training optimization” to “paper search + literature review.”
TrendR is a research-agent harness system, evolving toward a domain-specific agent OS.
What Problem It Solves
| Step | Manual | TrendR |
|---|---|---|
| Cross-platform paper search | 3–4 hrs | 5 min (9 sources parallel) |
| Filter relevant papers | 2–3 hrs | Auto score 1–5 + dedup |
| Deep read + notes | 8–12 hrs | Structured extraction (problem / method / result / limitation) |
| Write literature review | 6–8 hrs | Auto-generated (taxonomy + gap analysis + trends) |
| BibTeX references | 1–2 hrs | Automatic |
| Archive to knowledge base | 1 hr | Auto-sync to Obsidian |
| Total | ~20–30 hrs | ~30 min wait |
Architecture
System Overview
┌──────────────────────────────────────────────────────┐
│ User (Telegram / 飞书 / Web / CLI) │
└─────────────────────┬────────────────────────────────┘
▼
┌──────────────────────────────────────────────────────┐
│ OpenClaw Gateway (runs locally) │
│ │
│ ┌─ main agent ─────────────────────────────────┐ │
│ │ receive → decompose → dispatch → synthesize│ │
│ └──────┬──────────────┬──────────────┬──────────┘ │
│ ▼ ▼ ▼ │
│ ┌────────────┐ ┌────────────┐ ┌──────────┐ ┌──────┐ │
│ │paper-scout │ │paper- │ │review- │ │verif-│ │
│ │search·score│ │analyzer │ │lead │ │ier │ │
│ │dedup │ │read·extract│ │orchestr. │ │verify│ │
│ └────────────┘ └────────────┘ └──────────┘ └──────┘ │
│ │
│ ┌── Skills (executable Markdown knowledge files) ──┐ │
│ │ paper-scout · paper-analyzer · review-writer │ │
│ │ verifier · trendr-watchdog · platform-hotspots │ │
│ │ chrome-cdp-setup · research-vault │ │
│ └──────────────────────┬───────────────────────────┘ │
│ ▼ │
│ ┌── v2 engine (state machine / validators / watchdog)┐│
│ │ INIT→DISCOVERY→ANALYSIS→GAP_CHECK→WRITING→VERIFY│ │
│ │ Basic: 9×academic APIs (free, no extra MCP) │ │
│ │ Full: +Scrapling +Nano-pdf +Context7 +Zotero │ │
│ │ Fallback: Playwright (JS gaps / login only) │ │
│ └──────────────────────────────────────────────────┘ │
└──────────────┬───────────────────────┬───────────────┘
▼ ▼
┌─────────────────────┐ ┌───────────────────────┐
│ 9 Academic APIs │ │ Obsidian Vault │
│ arXiv·S2·OA·PubMed │ │ paper pool / reviews │
│ CrossRef·DBLP··· │ │ cards / daily logs │
└─────────────────────┘ └───────────────────────┘
v2 State Machine
INIT → DISCOVERY → ANALYSIS → GAP_CHECK → WRITING → VERIFY → DONE
↑ ↓
└────── coverage gaps ─────┘
VERIFY fail:
WRITING ← verify.json.pass=false (max 2 repair rounds)
Pipeline
User prompt
│
▼
Phase 1 · Search ────── paper-scout: 3–5 APIs parallel
│ → candidates.csv (40–100, scored 1–5)
▼
Phase 2 · Deep Read ─── paper-analyzer: reads score ≥ 4
│ → notes/*.md + matrix.csv
▼
Phase 3 · Gap Check ─── enough coverage? → Ph.4 : loop back
▼
Phase 4 · Write ──────── review-lead: full literature review
│ → review.md (15–25KB) + references.bib
▼
Phase 5 · Verify ──────── verifier: citation/claim/taxonomy check
│ fail → Ph.4 (max 2 rounds) | pass → Ph.6
▼
Phase 6 · Persist ──────── Basic: ~/research/ Full: Obsidian+Zotero
▼
Notify user (Telegram / 飞书)
Contents
Core (Basic + Full)
| Type | Name | Role |
|---|---|---|
| Agent | paper-scout | 9-source search + score + dedup |
| Agent | paper-analyzer | Deep read + structured notes + matrix |
| Agent | review-lead | Pipeline orchestration + survey writing |
| Agent | verifier | Citation validity / taxonomy consistency |
| Skill | paper-scout | 9 academic API playbooks (10KB) |
| Skill | paper-analyzer | Structured extraction templates |
| Skill | review-writer | Survey template + quality checklist |
| Skill | verifier | VERIFY rules + verify.json protocol |
| Skill | research-vault | Obsidian persistence + paper pool index |
| Skill | trendr-watchdog | Runtime supervision + auto-resume |
| Skill | platform-hotspots | 9-platform trend scraping |
| Skill | chrome-cdp-setup | Chrome 146+ CDP dual-instance + cookie sync |
| Runtime | engine/ | v2: state machine + validators + watchdog |
| Runtime | cli.py | Standalone CLI: run / resume / status |
Full Mode Extras
| Component | Function | Without it |
|---|---|---|
| Scrapling | JS-rendered page crawling | Static API only, lower coverage |
| Zotero | Auto-import DOI to library | BibTeX still generated locally |
| Obsidian + obsidian-cli | Paper cards + review archive + daily logs | Results saved to ~/research/ |
| Nano-pdf | Full-text PDF reading | Abstract/metadata only |
| Context7 | Precise library docs for codex-coder | Falls back to web search |
Fallback Layer
| Component | Trigger |
|---|---|
| Playwright | JS rendering gaps, login-gated pages, or explicit user request only |
9 Search Sources
All APIs are publicly free — called via web_fetch, no extra MCP needed.
| # | Source | Coverage | Key Required |
|---|---|---|---|
| 1 | arXiv | CS / math / physics preprints | No |
| 2 | Semantic Scholar | 200M+ papers, citation graph | Recommended (free) |
| 3 | OpenAlex | 250M+ works, fully open | No |
| 4 | PubMed | 36M+ biomedical | No |
| 5 | CrossRef | 140M+ DOI registry | No |
| 6 | DBLP | Computer science bibliography | No |
| 7 | Europe PMC | 40M+ life sciences | No |
| 8 | bioRxiv | Biology preprints | No |
| 9 | Papers with Code | ML papers + code repos | No |
Agent auto-selects 3–5 most relevant sources per topic.
Platform Trend Monitoring
Beyond academic papers, TrendR monitors 9 platforms in real time via Chrome CDP:
You: "What's trending in AI today?"
TrendR:
→ Chrome CDP automation (dedicated instance with login state)
→ Zhihu · Xiaohongshu · X/Twitter · Reddit
→ YouTube · GitHub Trending · Hacker News · Product Hunt
→ Cross-platform tech trend summary
Compatible Runtimes
| Platform | Support | Notes |
|---|---|---|
| OpenClaw | Full | Native multi-agent + browser automation |
| Standalone CLI | v2 engine | python cli.py run --topic "..." --depth B |
| Claude Code | Skills readable | via CLAUDE.md, WebFetch / Agent tool |
| Codex | Skills readable | via AGENTS.md, curl/fetch, sequential |
| Other agents | Skills readable | Standard Markdown, API URLs copyable |
Anti-Forgetting Mechanism
When using non-frontier models (e.g. MiniMax M2.5), agents may skip reading Skill files. TrendR uses a 3-layer defense:
| Layer | Mechanism |
|---|---|
AGENTS.md | Hard rule: “task description must include ‘read skills/xxx/SKILL.md first’” |
SOUL.md | Top warning: “⚠️ Step 1: read skills/xxx/SKILL.md” |
SKILL.md | Complete copy-paste commands, not abstract instructions |
Obsidian Vault Structure
[Vault]/Research/
├── _index/
│ └── paper-pool.csv ← paper pool (cross-project, cumulative)
├── papers/
│ └── 2301.12345.md ← paper card (YAML frontmatter + wiki-links)
├── reviews/
│ └── project-name/
│ ├── review.md
│ ├── references.bib
│ └── matrix.csv
├── daily/
│ └── 2026-03-10.md ← daily research log
└── templates/
Paper pool CSV tracks state: candidate → analyzed → cited_in_review
Installation
git clone https://github.com/gy-hou/trendr.git
cd trendr
chmod +x install.sh
./install.sh
Choose Basic for zero extra dependencies, or Full for Scrapling + Obsidian + Zotero + Nano-pdf.
Known Limitations
- Not real-time: academic APIs have rate limits (arXiv: 3s/request); full search takes a few minutes
- Network policy variance: some DNS/proxy routes academic domains to
198.18.x.x(fake-ip); TrendR has fallback search but coverage may drop - Non-frontier model forgetting: MiniMax M2.5 may occasionally skip Skill files despite 3-layer defense
- Full-text reading (Basic): abstract only; Full mode with Nano-pdf enables PDF deep reading
- No dual-AI review: extensible (see paper-distill-mcp dual-review mode)
Credits
- karpathy/autoresearch — inspiration for autonomous research loops
- paper-distill-mcp — multi-source search architecture reference
- OpenClaw — agent runtime infrastructure