sibyl-research 0.1.0

AI-powered deep research agent — market analysis, trend prediction, topic research with multi-LLM support

Sibyl

AI-powered deep research agent. Ask any question — Sibyl searches the web across multiple sources, reads dozens of pages, cross-references findings, and generates an executive-quality research report with analysis, predictions, and citations.

Not just another search summarizer. Sibyl is a research analysis platform — it does structured comparisons, SWOT analysis, Google Trends tracking, event timelines, and financial data visualization. All from a single question.

What Makes Sibyl Different

	Traditional Search	ChatGPT/Perplexity	GPT Researcher	Sibyl
Web search + summary	Yes	Yes	Yes	Yes
Multi-source (news, Reddit, Wikipedia)	No	Partial	Partial	Yes (4 engines)
Sub-question decomposition	No	No	Yes	Yes
Iterative gap-filling (search → analyze → identify gaps → search again)	No	No	Partial	Yes
Cross-source analysis (sentiment, consensus, disagreements)	No	No	No	Yes
Structured comparison tables	No	No	No	Yes
SWOT analysis	No	No	No	Yes
Google Trends data	No	No	No	Yes
Event timelines	No	No	No	Yes
Financial data + charts	No	No	No	Yes
MCP server (Claude Code, Cursor)	No	No	No	Yes
Multi-LLM (DeepSeek, Gemini, GLM, OpenAI)	No	No	Limited	Yes (auto-detect)
PDF reports with embedded charts	No	No	Basic	Yes

Quick Start

MCP Server (for Claude Code / Cursor)

pip install sibyl-research
claude mcp add sibyl -e DEEPSEEK_API_KEY=sk-... -- sibyl-mcp

Then in Claude Code:

"Research the impact of AI on software engineering jobs over the next 5 years"

"Compare NVIDIA vs AMD vs Intel for AI workloads"

"SWOT analysis of Tesla in 2026"

CLI

pip install sibyl-research
export DEEPSEEK_API_KEY=sk-...   # or OPENAI_API_KEY, GEMINI_API_KEY, etc.

# Standard research
sibyl "Canadian housing market outlook 2026"

# Deep research with predictions + market data + PDF
sibyl "Will NVIDIA maintain AI chip dominance?" -d 3 --symbols NVDA,AMD,INTC --pdf

# Chinese output
sibyl "加拿大移民政策变化" -l zh --pdf -o reports/

How It Works

You ask a question
  │
  ├─ Step 1: Decompose into 3-5 focused sub-questions
  ├─ Step 2: Generate 15-20 diverse search queries
  ├─ Step 3: Search across 4 engines (DuckDuckGo, Google News, Reddit, Wikipedia)
  ├─ Step 4: Scrape 15-20 sources (realistic browser headers, retry, Google Cache fallback)
  ├─ Step 5: Filter sources by relevance (LLM-scored)
  ├─ Step 6: Analyze each sub-question independently
  ├─ Step 7: Identify knowledge gaps → auto-search for missing info
  ├─ Step 8: Cross-reference sources (sentiment, consensus, disagreements)
  ├─ Step 9: Section-by-section synthesis (Summary, Findings, Analysis, Predictions)
  ├─ Step 10: Review and refine draft
  └─ Output: PDF/Markdown report with Table of Contents, citations, charts

Research Tools (11 MCP tools)

Core Research

Tool	What it does
research(query, depth, language)	Full research cycle: search → scrape → analyze → report. Depth 1-3.
quick_search(query)	Fast web search, returns raw results
read_url(url)	Extract clean text from any URL
analyze(text, question)	Analyze provided text with LLM

Analysis Tools (unique to Sibyl)

Tool	What it does
compare(items)	Structured side-by-side comparison table with metrics and recommendation
swot(subject)	Strengths / Weaknesses / Opportunities / Threats with evidence
trends(keywords)	Real Google Trends data: interest level, direction, rising searches
timeline(topic)	Chronological event table with dates and impact assessment

Financial Data

Tool	What it does
fetch_market_data(symbols)	Real stock/ETF prices, trends, moving averages, 52-week range
chart(symbols)	Generate price trend charts (PNG)

Output

Tool	What it does
save_report(format)	Save as PDF (with embedded charts) and/or Markdown

Research Depth

Depth	What happens	LLM calls	Time
1 (quick)	2-3 search queries, basic synthesis	~3	20-30s
2 (standard)	Sub-question decomposition, per-question analysis, cross-referencing, review	~10	60-90s
3 (deep)	+ Knowledge gap filling, predictions with bull/bear/base case, confidence rating	~13	90-120s

Multi-Provider Support

Sibyl works with any LLM. Auto-detects from environment variables:

Provider	Env var	Model
DeepSeek	DEEPSEEK_API_KEY	deepseek/deepseek-chat
OpenAI	OPENAI_API_KEY	gpt-4o-mini
Anthropic	ANTHROPIC_API_KEY	claude-sonnet-4-20250514
Gemini	GEMINI_API_KEY	gemini/gemini-2.5-flash
GLM (ZhipuAI)	ZHIPUAI_API_KEY	glm-4-flash

Or configure multiple providers with roles:

# sibyl.yaml
providers:
  - model: deepseek/deepseek-chat
    api_key: sk-xxx
    role: analysis

  - model: gemini/gemini-2.5-flash
    api_key: xxx
    role: fast

  - model: openai/glm-4-flash
    api_key: xxx
    api_base: https://open.bigmodel.cn/api/paas/v4
    role: chinese

Example Reports

Reports generated by Sibyl on real topics:

• Federal Reserve interest rate outlook 2026-2027 — 5 pages, 12 findings, 6 sources, analysis of "higher-for-longer" vs "steady easing" debate
• Impact of Trump tariffs on trade 2026 — 5 pages, 10 findings, 4 sources, historical comparison to Smoot-Hawley, second-order effects on AI labor displacement
• AI industry landscape 2026 — Market size ($538B), investment trends ($2.9T infrastructure), regulatory outlook, with NVDA/GOOGL/META stock charts

Requirements

• Python 3.10+
• At least one LLM API key
• No other API keys needed (all search engines are free)

License

MIT