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Sibyl

by chriswu727
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Python
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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

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - A tier

How are these scores calculated?

Resources

Tools

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