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<h1 id="adaptive-graph-of-thoughts">π§ Adaptive Graph of Thoughts</h1>
<p><strong>Transforming Scientific Discovery with Intelligent Graph-Based Reasoning</strong></p>
<p><a class="md-button md-button--primary" href="getting_started/">Get Started</a>
<a class="md-button" href="#key-features">Explore Features</a>
<a class="md-button" href="https://github.com/SaptaDey/Adaptive-Graph-of-Thoughts-MCP-server">View on GitHub</a></p>
<h4 align="center"><strong>Intelligent Scientific Reasoning through Graph-of-Thoughts</strong></h4>
<p align="center">
<a href="https://saptadey.github.io/Adaptive-Graph-of-Thoughts-MCP-server/"><img src="https://img.shields.io/badge/version-0.1.0-blue.svg" alt="Version"></a>
<a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.11+-blue.svg" alt="Python"></a>
<a href="https://github.com/SaptaDey/Adaptive-Graph-of-Thoughts-MCP-server/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache_2.0-green.svg" alt="License"></a>
<a href="https://github.com/SaptaDey/Adaptive-Graph-of-Thoughts-MCP-server/blob/main/Dockerfile"><img src="https://img.shields.io/badge/docker-ready-brightgreen.svg" alt="Docker"></a>
<a href="https://fastapi.tiangolo.com"><img src="https://img.shields.io/badge/FastAPI-0.111.0-009688.svg" alt="FastAPI"></a>
<a href="https://networkx.org"><img src="https://img.shields.io/badge/NetworkX-3.3-orange.svg" alt="NetworkX"></a>
<a href="https://github.com/SaptaDey/Adaptive-Graph-of-Thoughts-MCP-server/blob/main/.md/CHANGELOG.md"><img src="https://img.shields.io/badge/last_updated-June_2025-lightgrey.svg" alt="Last Updated"></a>
</p>
<div align="center">
<p><strong>π Next-Generation AI Reasoning Framework for Scientific Research</strong></p>
<p><em>Leveraging graph structures to transform how AI systems approach scientific reasoning</em></p>
</div>
<h2 id="overview">π Overview</h2>
<p>Adaptive Graph of Thoughts leverages a <strong>Neo4j graph database</strong> to perform sophisticated scientific reasoning, with graph operations managed within its pipeline stages. It implements the <strong>Model Context Protocol (MCP)</strong> to integrate with AI applications like Claude Desktop, providing an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework designed for complex research tasks.</p>
<p><strong>Key highlights:</strong>
- 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</p>
<h2 id="key-features">π Key Features</h2>
<h3 id="8-stage-reasoning-pipeline">8-Stage Reasoning Pipeline</h3>
<pre class="mermaid"><code>graph TD
A[π± Stage 1: Initialization] --> B[π§© Stage 2: Decomposition]
B --> C[π¬ Stage 3: Hypothesis/Planning]
C --> D[π Stage 4: Evidence Integration]
D --> E[βοΈ Stage 5: Pruning/Merging]
E --> F[π Stage 6: Subgraph Extraction]
F --> G[π Stage 7: Composition]
G --> H[π€ 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</code></pre>
<p>The core reasoning process follows a sophisticated 8-stage pipeline:</p>
<ol>
<li><strong>π± Initialization</strong></li>
<li>Creates root node from query with multi-dimensional confidence vector</li>
<li>Establishes initial graph structure with proper metadata</li>
<li>
<p>Sets baseline confidence across empirical, theoretical, methodological, and consensus dimensions</p>
</li>
<li>
<p><strong>π§© Decomposition</strong></p>
</li>
<li>Breaks query into key dimensions: Scope, Objectives, Constraints, Data Needs, Use Cases</li>
<li>Identifies potential biases and knowledge gaps from the outset</li>
<li>
<p>Creates dimensional nodes with initial confidence assessments</p>
</li>
<li>
<p><strong>π¬ Hypothesis/Planning</strong></p>
</li>
<li>Generates 3-5 hypotheses per dimension with explicit falsification criteria</li>
<li>Creates detailed execution plans for each hypothesis</li>
<li>
<p>Tags with disciplinary provenance and impact estimates</p>
</li>
<li>
<p><strong>π Evidence Integration</strong></p>
</li>
<li>Iteratively selects hypotheses based on confidence-to-cost ratio and impact</li>
<li>Gathers and links evidence using typed edges (causal, temporal, correlative)</li>
<li>
<p>Updates confidence vectors using Bayesian methods with statistical power assessment</p>
</li>
<li>
<p><strong>βοΈ Pruning/Merging</strong></p>
</li>
<li>Removes nodes with low confidence and impact scores</li>
<li>Consolidates semantically similar nodes</li>
<li>
<p>Optimizes graph structure while preserving critical relationships</p>
</li>
<li>
<p><strong>π Subgraph Extraction</strong></p>
</li>
<li>Identifies high-value subgraphs based on multiple criteria</li>
<li>Focuses on nodes with high confidence and impact scores</li>
<li>
<p>Extracts patterns relevant to the original query</p>
</li>
<li>
<p><strong>π Composition</strong></p>
</li>
<li>Synthesizes findings into coherent narrative</li>
<li>Annotates claims with node IDs and edge types</li>
<li>
<p>Provides comprehensive answers with proper citations</p>
</li>
<li>
<p><strong>π€ Reflection</strong></p>
</li>
<li>Performs comprehensive quality audit</li>
<li>Evaluates coverage, bias detection, and methodological rigor</li>
<li>Provides final confidence assessment and improvement recommendations</li>
</ol>
<h3 id="advanced-technical-capabilities">Advanced Technical Capabilities</h3>
<div align="center">
<table>
<tr>
<td align="center">π <b>Multi-Dimensional<br>Confidence</b></td>
<td align="center">π§ <b>Graph-Based<br>Knowledge</b></td>
<td align="center">π <b>MCP<br>Integration</b></td>
<td align="center">β‘ <b>FastAPI<br>Backend</b></td>
</tr>
<tr>
<td align="center">π³ <b>Docker<br>Deployment</b></td>
<td align="center">π§© <b>Modular<br>Design</b></td>
<td align="center">βοΈ <b>Configuration<br>Management</b></td>
<td align="center">π <b>Type<br>Safety</b></td>
</tr>
<tr>
<td align="center">π <b>Interdisciplinary<br>Bridge Nodes</b></td>
<td align="center">π <b>Hyperedge<br>Support</b></td>
<td align="center">π <b>Statistical<br>Power Analysis</b></td>
<td align="center">π― <b>Impact<br>Estimation</b></td>
</tr>
</table>
</div>
<h3 id="architectural-highlights">Architectural Highlights</h3>
<p>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.</p>
<ul>
<li><strong>8-Stage Pipeline Design</strong>: The core reasoning process is broken down into eight distinct stages, from initialization to reflection. Each stage has a well-defined responsibility.</li>
<li><strong>Stage-Specific Logic and Neo4j Interaction</strong>: 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 <code>neo4j_utils</code> for database communication. This means the graph representation is persisted and manipulated directly within Neo4j.</li>
<li><strong>Orchestration by <code>GoTProcessor</code></strong>: The <code>GoTProcessor</code> 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.</li>
<li><strong>Data Flow Between Stages</strong>: Data is passed between stages using <code>GoTProcessorSessionData</code> and <code>accumulated_context</code>. Each stage receives context from previous stages and can contribute its findings to the <code>accumulated_context</code>, which is then available to subsequent stages. This allows for a progressive build-up of insights as the pipeline executes.</li>
</ul>
<p><strong>Core Features:</strong>
- <strong>π§ Graph Knowledge Representation</strong>: Utilizes a <strong>Neo4j graph database</strong> to model complex relationships. Graph interactions and manipulations are performed by individual pipeline stages using Cypher queries via <code>neo4j_utils</code>.
- <strong>π Dynamic Confidence Vectors</strong>: Four-dimensional confidence assessment (empirical support, theoretical basis, methodological rigor, consensus alignment)
- <strong>π Interdisciplinary Bridge Nodes</strong>: Automatically connects insights across different research domains
- <strong>π Advanced Edge Types</strong>: Supports causal, temporal, correlative, and custom relationship types
- <strong>π Statistical Rigor</strong>: Integrated power analysis and effect size estimation
- <strong>π― Impact-Driven Prioritization</strong>: Focuses on high-impact research directions
- <strong>π MCP Server</strong>: Seamless Claude Desktop integration with Model Context Protocol
- <strong>β‘ High-Performance API</strong>: Modern FastAPI implementation with async support</p>
<h2 id="technology-stack">π οΈ Technology Stack</h2>
<div align="center">
<table>
<tr>
<td align="center"><img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/python/python-original.svg" alt="Python logo" width="38" height="38"/><br>Python 3.11+</td>
<td align="center"><img src="https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png" alt="FastAPI logo" width="38" height="38"/><br>FastAPI</td>
<td align="center"><img src="https://networkx.org/documentation/stable/_static/networkx_logo.svg" width="38" height="38"/><br>NetworkX</td>
<td align="center"><img src="https://raw.githubusercontent.com/devicons/devicon/master/icons/docker/docker-original.svg" width="38" height="38"/><br>Docker</td>
</tr>
<tr>
<td align="center"><img src="https://docs.pytest.org/en/7.3.x/_static/pytest_logo_curves.svg" width="38" height="38"/><br>Pytest</td>
<td align="center"><img src="https://docs.pydantic.dev/latest/img/logo-white.svg" width="38" height="38"/><br>Pydantic</td>
<td align="center"><img src="https://python-poetry.org/images/logo-origami.svg" width="38" height="38"/><br>Poetry</td>
<td align="center"><img src="https://raw.githubusercontent.com/tomchristie/uvicorn/master/docs/uvicorn.png" width="38" height="38"/><br>Uvicorn</td>
</tr>
</table>
</div>
<p><em>For detailed setup, usage, and contribution guidelines, please refer to the respective sections in this documentation.</em></p>
</article>
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