🧠 Adaptive Graph of Thoughts

Transforming Scientific Discovery with Intelligent Graph-Based Reasoning

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🚀 Next-Generation AI Reasoning Framework for Scientific Research

Leveraging graph structures to transform how AI systems approach scientific reasoning

🔍 Overview

Adaptive Graph of Thoughts leverages a Neo4j graph database to perform sophisticated scientific reasoning, with graph operations managed within its pipeline stages. It implements the Model Context Protocol (MCP) to integrate with AI applications like Claude Desktop, providing an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework designed for complex research tasks.

Key highlights: - Process complex scientific queries using graph-based reasoning - Dynamic confidence scoring with multi-dimensional evaluations - Built with modern Python and FastAPI for high performance - Dockerized for easy deployment - Modular design for extensibility and customization - Integration with Claude Desktop via MCP protocol

🌟 Key Features

8-Stage Reasoning Pipeline

graph TD
    A[🌱 Stage 1: Initialization] --> B[🧩 Stage 2: Decomposition]
    B --> C[🔬 Stage 3: Hypothesis/Planning]
    C --> D[📊 Stage 4: Evidence Integration]
    D --> E[✂️ Stage 5: Pruning/Merging]
    E --> F[🔍 Stage 6: Subgraph Extraction]
    F --> G[📝 Stage 7: Composition]
    G --> H[🤔 Stage 8: Reflection]

    A1[Create root node<br/>Set initial confidence<br/>Define graph structure] --> A
    B1[Break into dimensions<br/>Identify components<br/>Create dimensional nodes] --> B
    C1[Generate hypotheses<br/>Create reasoning strategy<br/>Set falsification criteria] --> C
    D1[Gather evidence<br/>Link to hypotheses<br/>Update confidence scores] --> D
    E1[Remove low-value elements<br/>Consolidate similar nodes<br/>Optimize structure] --> E
    F1[Identify relevant portions<br/>Focus on high-value paths<br/>Create targeted subgraphs] --> F
    G1[Synthesize findings<br/>Create coherent insights<br/>Generate comprehensive answer] --> G
    H1[Evaluate reasoning quality<br/>Identify improvements<br/>Final confidence assessment] --> H

    style A fill:#e1f5fe
    style B fill:#f3e5f5
    style C fill:#e8f5e8
    style D fill:#fff3e0
    style E fill:#ffebee
    style F fill:#f1f8e9
    style G fill:#e3f2fd
    style H fill:#fce4ec

The core reasoning process follows a sophisticated 8-stage pipeline:

1. 🌱 Initialization
2. Creates root node from query with multi-dimensional confidence vector
3. Establishes initial graph structure with proper metadata

4. Sets baseline confidence across empirical, theoretical, methodological, and consensus dimensions

5. 🧩 Decomposition

6. Breaks query into key dimensions: Scope, Objectives, Constraints, Data Needs, Use Cases
7. Identifies potential biases and knowledge gaps from the outset

8. Creates dimensional nodes with initial confidence assessments

9. 🔬 Hypothesis/Planning

10. Generates 3-5 hypotheses per dimension with explicit falsification criteria
11. Creates detailed execution plans for each hypothesis

12. Tags with disciplinary provenance and impact estimates

13. 📊 Evidence Integration

14. Iteratively selects hypotheses based on confidence-to-cost ratio and impact
15. Gathers and links evidence using typed edges (causal, temporal, correlative)

16. Updates confidence vectors using Bayesian methods with statistical power assessment

17. ✂️ Pruning/Merging

18. Removes nodes with low confidence and impact scores
19. Consolidates semantically similar nodes

20. Optimizes graph structure while preserving critical relationships

21. 🔍 Subgraph Extraction

22. Identifies high-value subgraphs based on multiple criteria
23. Focuses on nodes with high confidence and impact scores

24. Extracts patterns relevant to the original query

25. 📝 Composition

26. Synthesizes findings into coherent narrative
27. Annotates claims with node IDs and edge types

28. Provides comprehensive answers with proper citations

29. 🤔 Reflection

30. Performs comprehensive quality audit
31. Evaluates coverage, bias detection, and methodological rigor
32. Provides final confidence assessment and improvement recommendations

Advanced Technical Capabilities

🔄 Multi-Dimensional Confidence	🧠 Graph-Based Knowledge	🔌 MCP Integration	⚡ FastAPI Backend
🐳 Docker Deployment	🧩 Modular Design	⚙️ Configuration Management	🔒 Type Safety
🌐 Interdisciplinary Bridge Nodes	🔗 Hyperedge Support	📊 Statistical Power Analysis	🎯 Impact Estimation

Architectural Highlights

Adaptive Graph of Thoughts is built around a flexible 8-stage pipeline architecture, where each stage encapsulates specific reasoning logic. This design promotes modularity and clarity.

• 8-Stage Pipeline Design: The core reasoning process is broken down into eight distinct stages, from initialization to reflection. Each stage has a well-defined responsibility.
• Stage-Specific Logic and Neo4j Interaction: Graph operations and interactions with the Neo4j database are primarily handled within individual stages. Each stage formulates and executes Cypher queries relevant to its task, utilizing neo4j_utils for database communication. This means the graph representation is persisted and manipulated directly within Neo4j.
• Orchestration by GoTProcessor: The GoTProcessor acts as the central orchestrator. It manages the flow through the 8-stage pipeline, invoking each stage in sequence. It does not manage a central graph object in memory; rather, it facilitates the overall process.
• Data Flow Between Stages: Data is passed between stages using GoTProcessorSessionData and accumulated_context. Each stage receives context from previous stages and can contribute its findings to the accumulated_context, which is then available to subsequent stages. This allows for a progressive build-up of insights as the pipeline executes.

Core Features: - 🧠 Graph Knowledge Representation: Utilizes a Neo4j graph database to model complex relationships. Graph interactions and manipulations are performed by individual pipeline stages using Cypher queries via neo4j_utils. - 🔄 Dynamic Confidence Vectors: Four-dimensional confidence assessment (empirical support, theoretical basis, methodological rigor, consensus alignment) - 🌐 Interdisciplinary Bridge Nodes: Automatically connects insights across different research domains - 🔗 Advanced Edge Types: Supports causal, temporal, correlative, and custom relationship types - 📊 Statistical Rigor: Integrated power analysis and effect size estimation - 🎯 Impact-Driven Prioritization: Focuses on high-impact research directions - 🔌 MCP Server: Seamless Claude Desktop integration with Model Context Protocol - ⚡ High-Performance API: Modern FastAPI implementation with async support

🛠️ Technology Stack

Python 3.11+	FastAPI	NetworkX	Docker
Pytest	Pydantic	Poetry	Uvicorn

For detailed setup, usage, and contribution guidelines, please refer to the respective sections in this documentation.