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for="__nav_8" id="__nav_8_label" tabindex="0"> <span class="md-ellipsis"> Extending Adaptive Graph of Thoughts </span> <span class="md-nav__icon md-icon"></span> </label> <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false"> <label class="md-nav__title" for="__nav_8"> <span class="md-nav__icon md-icon"></span> Extending Adaptive Graph of Thoughts </label> <ul class="md-nav__list" data-md-scrollfix> <li class="md-nav__item"> <a href="extending/custom_stages/" class="md-nav__link"> <span class="md-ellipsis"> Custom Stages </span> </a> </li> <li class="md-nav__item"> <a href="extending_with_claude/" class="md-nav__link"> <span class="md-ellipsis"> Claude API Integration </span> </a> </li> </ul> </nav> </li> <li class="md-nav__item"> <a href="CONTRIBUTING/" class="md-nav__link"> <span class="md-ellipsis"> Contributing </span> </a> </li> <li class="md-nav__item"> <a href="ROADMAP/" class="md-nav__link"> <span class="md-ellipsis"> Roadmap </span> </a> </li> </ul> </nav> </div> </div> </div> <div class="md-content" data-md-component="content"> <article class="md-content__inner md-typeset"> <h1 id="adaptive-graph-of-thoughts">🧠 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>πŸš€ 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">πŸ” 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">🌟 Key Features</h2> <h3 id="8-stage-reasoning-pipeline">8-Stage Reasoning Pipeline</h3> <pre class="mermaid"><code>graph TD A[🌱 Stage 1: Initialization] --&gt; B[🧩 Stage 2: Decomposition] B --&gt; C[πŸ”¬ Stage 3: Hypothesis/Planning] C --&gt; D[πŸ“Š Stage 4: Evidence Integration] D --&gt; E[βœ‚οΈ Stage 5: Pruning/Merging] E --&gt; F[πŸ” Stage 6: Subgraph Extraction] F --&gt; G[πŸ“ Stage 7: Composition] G --&gt; H[πŸ€” Stage 8: Reflection] A1[Create root node&lt;br/&gt;Set initial confidence&lt;br/&gt;Define graph structure] --&gt; A B1[Break into dimensions&lt;br/&gt;Identify components&lt;br/&gt;Create dimensional nodes] --&gt; B C1[Generate hypotheses&lt;br/&gt;Create reasoning strategy&lt;br/&gt;Set falsification criteria] --&gt; C D1[Gather evidence&lt;br/&gt;Link to hypotheses&lt;br/&gt;Update confidence scores] --&gt; D E1[Remove low-value elements&lt;br/&gt;Consolidate similar nodes&lt;br/&gt;Optimize structure] --&gt; E F1[Identify relevant portions&lt;br/&gt;Focus on high-value paths&lt;br/&gt;Create targeted subgraphs] --&gt; F G1[Synthesize findings&lt;br/&gt;Create coherent insights&lt;br/&gt;Generate comprehensive answer] --&gt; G H1[Evaluate reasoning quality&lt;br/&gt;Identify improvements&lt;br/&gt;Final confidence assessment] --&gt; 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>🌱 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>🧩 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>πŸ”¬ 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>πŸ“Š 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>βœ‚οΈ 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>πŸ” 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>πŸ“ 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>πŸ€” 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">πŸ”„ <b>Multi-Dimensional<br>Confidence</b></td> <td align="center">🧠 <b>Graph-Based<br>Knowledge</b></td> <td align="center">πŸ”Œ <b>MCP<br>Integration</b></td> <td align="center">⚑ <b>FastAPI<br>Backend</b></td> </tr> <tr> <td align="center">🐳 <b>Docker<br>Deployment</b></td> <td align="center">🧩 <b>Modular<br>Design</b></td> <td align="center">βš™οΈ <b>Configuration<br>Management</b></td> <td align="center">πŸ”’ <b>Type<br>Safety</b></td> </tr> <tr> <td align="center">🌐 <b>Interdisciplinary<br>Bridge Nodes</b></td> <td align="center">πŸ”— <b>Hyperedge<br>Support</b></td> <td align="center">πŸ“Š <b>Statistical<br>Power Analysis</b></td> <td align="center">🎯 <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>🧠 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>πŸ”„ Dynamic Confidence Vectors</strong>: Four-dimensional confidence assessment (empirical support, theoretical basis, methodological rigor, consensus alignment) - <strong>🌐 Interdisciplinary Bridge Nodes</strong>: Automatically connects insights across different research domains - <strong>πŸ”— Advanced Edge Types</strong>: Supports causal, temporal, correlative, and custom relationship types - <strong>πŸ“Š Statistical Rigor</strong>: Integrated power analysis and effect size estimation - <strong>🎯 Impact-Driven Prioritization</strong>: Focuses on high-impact research directions - <strong>πŸ”Œ MCP Server</strong>: Seamless Claude Desktop integration with Model Context Protocol - <strong>⚑ High-Performance API</strong>: Modern FastAPI implementation with async support</p> <h2 id="technology-stack">πŸ› 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> </div> <script>var tabs=__md_get("__tabs");if(Array.isArray(tabs))e:for(var set of document.querySelectorAll(".tabbed-set")){var labels=set.querySelector(".tabbed-labels");for(var tab of tabs)for(var label of labels.getElementsByTagName("label"))if(label.innerText.trim()===tab){var input=document.getElementById(label.htmlFor);input.checked=!0;continue e}}</script> <script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script> </div> <button type="button" class="md-top md-icon" data-md-component="top" hidden> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8z"/></svg> Back to top </button> </main> <footer class="md-footer"> <div class="md-footer-meta md-typeset"> <div class="md-footer-meta__inner md-grid"> <div class="md-copyright"> Made with <a href="https://squidfunk.github.io/mkdocs-material/" target="_blank" rel="noopener"> Material for MkDocs </a> </div> </div> </div> </footer> </div> <div class="md-dialog" data-md-component="dialog"> <div class="md-dialog__inner md-typeset"></div> </div> <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.sections", "toc.integrate", "navigation.top", "search.suggest", "search.highlight", "content.tabs.link", "content.code.annotation", "content.code.copy", "navigation.indexes", "navigation.expand", "header.autohide"], "search": "assets/javascripts/workers/search.d50fe291.min.js", "tags": null, "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}, "version": null}</script> <script src="assets/javascripts/bundle.13a4f30d.min.js"></script> <script src="https://unpkg.com/mermaid@10.4.0/dist/mermaid.min.js"></script> </body> </html>

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