Shandu is a cutting-edge AI research assistant that performs in-depth, multi-source research on any topic using advanced language models, intelligent web scraping, and iterative exploration to generate comprehensive, well-structured reports with proper citations.

Shandu is an intelligent, LLM-powered research system that automates the comprehensive research process - from initial query clarification to in-depth content analysis and report generation. Built on LangGraph's state-based workflow, it recursively explores topics with sophisticated algorithms for source evaluation, content extraction, and knowledge synthesis.

• Academic Research: Generate literature reviews, background information, and complex topic analyses
• Market Intelligence: Analyze industry trends, competitor strategies, and market opportunities
• Content Creation: Produce well-researched articles, blog posts, and reports with proper citations
• Technology Exploration: Track emerging technologies, innovations, and technical developments
• Policy Analysis: Research regulations, compliance requirements, and policy implications
• Competitive Analysis: Compare products, services, and company strategies across industries

Shandu 2.0 introduces a major redesign of the report generation pipeline to produce more coherent, reliable reports:

• Modular Report Generation: Process reports in self-contained sections, enhancing overall system reliability
• Robust Error Recovery: Automatic retry mechanisms with intelligent fallbacks prevent the system from getting stuck
• Section-By-Section Processing: Each section is processed independently, allowing for better error isolation
• Progress Tracking: Detailed progress tracking helps identify exactly where the process is at each stage
• Enhanced Citation Management: More reliable citation handling ensures proper attribution throughout reports
• Intelligent Parallelization: Key processes run in parallel where possible for improved performance
• Comprehensive Fallback Mechanisms: If any step fails, the system gracefully degrades rather than halting

flowchart TB
    subgraph Input
        Q[User Query]
        B[Breadth Parameter]
        D[Depth Parameter]
    end

    subgraph Research[Research Phase]
        direction TB
        DR[Deep Research]
        SQ[SERP Queries]
        PR[Process Results]
        NL[(Sources & Learnings)]
        ND[(Directions)]
    end

    subgraph Report[Report Generation]
        direction TB
        TG[Title Generation]
        TE[Theme Extraction]
        IR[Initial Report]
        ES[Section Enhancement]
        EX[Section Expansion]
        FR[Final Report]
    end

    %% Main Flow
    Q & B & D --> DR
    DR --> SQ --> PR
    PR --> NL
    PR --> ND
    
    DP{depth > 0?}
    NL & ND --> DP

    RD["Next Direction:
    - Prior Goals
    - New Questions
    - Learnings"]

    %% Circular Flow
    DP -->|Yes| RD
    RD -->|New Context| DR

    %% To Report Generation
    DP -->|No| TG
    TG --> TE --> IR --> ES --> EX --> FR

    %% Styling
    classDef input fill:#7bed9f,stroke:#2ed573,color:black
    classDef process fill:#70a1ff,stroke:#1e90ff,color:black
    classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
    classDef output fill:#ff4757,stroke:#ff6b81,color:white
    classDef storage fill:#a8e6cf,stroke:#3b7a57,color:black

    class Q,B,D input
    class DR,SQ,PR,TG,TE,IR,ES,EX process
    class DP,RD recursive
    class FR output
    class NL,ND storage

• Intelligent State-based Workflow: Leverages LangGraph for a structured, step-by-step research process
• Iterative Deep Exploration: Recursively explores topics with dynamic depth and breadth parameters
• Multi-source Information Synthesis: Analyzes data from search engines, web content, and knowledge bases
• Enhanced Web Scraping: Features dynamic JS rendering, content extraction, and ethical scraping practices
• Smart Source Evaluation: Automatically assesses source credibility, relevance, and information value
• Content Analysis Pipeline: Uses advanced NLP to extract key information, identify patterns, and synthesize findings
• Sectional Report Generation: Creates detailed reports by processing individual sections for maximum reliability
• Parallel Processing Architecture: Implements concurrent operations for efficient multi-query execution
• Adaptive Search Strategy: Dynamically adjusts search queries based on discovered information
• Full Citation Management: Properly attributes all sources with formatted citations in multiple styles

# Install from PyPI
pip install shandu

# Install from source
git clone https://github.com/jolovicdev/shandu.git
cd shandu
pip install -e .

# Configure API settings (supports various LLM providers)
shandu configure

# Run comprehensive research
shandu research "Your research query" --depth 2 --breadth 4 --output report.md

# Quick AI-powered search with web scraping
shandu aisearch "Who is the current sitting president of United States?" --detailed

shandu research "Your research query" \
    --depth 3 \                # How deep to explore (1-5, default: 2)
    --breadth 5 \              # How many parallel queries (2-10, default: 4)
    --output report.md \       # Save to file instead of terminal
    --verbose                  # Show detailed progress

You can find example reports in the examples directory:

1. The Intersection of Quantum Computing, Synthetic Biology, and Climate Modeling

   shandu research "The Intersection of Quantum Computing, Synthetic Biology, and Climate Modeling" --depth 3 --breadth 3 --output examples/o3-mini-high.md

from shandu.agents import ResearchGraph
from langchain_openai import ChatOpenAI

# Initialize with custom LLM if desired
llm = ChatOpenAI(model="gpt-4")

# Initialize the research graph
researcher = ResearchGraph(
    llm=llm,
    temperature=0.5
)

# Perform deep research
results = researcher.research_sync(
    query="Your research query",
    depth=3,       # How deep to go with recursive research
    breadth=4,     # How many parallel queries to explore
    detail_level="high"
)

# Print or save results
print(results.to_markdown())

Shandu's research pipeline consists of these key stages:

1. Query Clarification: Interactive questions to understand research needs
2. Research Planning: Strategic planning for comprehensive topic coverage
3. Iterative Exploration:
   • Smart query generation based on knowledge gaps
   • Multi-engine search with parallelized execution
   • Relevance filtering of search results
   • Intelligent web scraping with content extraction
   • Source credibility assessment
   • Information analysis and synthesis
   • Reflection on findings to identify gaps

Shandu 2.0 introduces a robust, modular report generation pipeline:

1. Data Preparation: Registration of all sources and their metadata for proper citation
2. Title Generation: Creating a concise, professional title (with retry mechanisms)
3. Theme Extraction: Identifying key themes to organize the report structure
4. Citation Formatting: Properly formatting all citations for reference
5. Initial Report Generation: Creating a comprehensive draft report
6. Section Enhancement: Individually processing each section to add detail and depth
7. Key Section Expansion: Identifying and expanding the most important sections
8. Report Finalization: Final processing and validation of the complete report

Each step includes:

• Comprehensive error handling
• Automatic retries with exponential backoff
• Intelligent fallbacks when issues occur
• Progress tracking for transparency
• Validation to ensure quality output

• Google Search
• DuckDuckGo
• Wikipedia
• ArXiv (academic papers)
• Custom search engines can be added

• Dynamic JS Rendering: Handles JavaScript-heavy websites
• Content Extraction: Identifies and extracts main content from web pages
• Parallel Processing: Concurrent execution of searches and scraping
• Caching: Efficient caching of search results and scraped content
• Rate Limiting: Respectful access to web resources
• Robots.txt Compliance: Ethical web scraping practices
• Flexible Output Formats: Markdown, JSON, plain text

This project is licensed under the MIT License - see the LICENSE file for details.