AI-powered deep research agent — market analysis, trend prediction, topic research. MCP server + CLI. Multi-LLM support (DeepSeek, Gemini, GLM, OpenAI).
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.
| Traditional Search | DeepSeek/DeepSeek | 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, DeepSeek) | No | No | No | Yes |
| Multi-LLM (DeepSeek, DeepSeek, GLM, OpenAI) | No | No | Limited | Yes (auto-detect) |
| PDF reports with embedded charts | No | No | Basic | Yes |
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"
pip install sibyl-research
export DEEPSEEK_API_KEY=sk-... # or OPENAI_API_KEY, GEMINI_API_KEY, etc.
# Standard research
sibyl "Canadian housing market outl
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