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# 🧠 Adaptive Graph of Thoughts
**Transforming Scientific Discovery with Intelligent Graph-Based Reasoning**
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<h4 align="center"><strong>Intelligent Scientific Reasoning through Graph-of-Thoughts</strong></h4>
<p align="center">
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</p>
<div align="center">
<p><strong>🚀 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>
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## 🔍 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
```mermaid
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**
- Creates root node from query with multi-dimensional confidence vector
- Establishes initial graph structure with proper metadata
- Sets baseline confidence across empirical, theoretical, methodological, and consensus dimensions
2. **🧩 Decomposition**
- Breaks query into key dimensions: Scope, Objectives, Constraints, Data Needs, Use Cases
- Identifies potential biases and knowledge gaps from the outset
- Creates dimensional nodes with initial confidence assessments
3. **🔬 Hypothesis/Planning**
- Generates 3-5 hypotheses per dimension with explicit falsification criteria
- Creates detailed execution plans for each hypothesis
- Tags with disciplinary provenance and impact estimates
4. **📊 Evidence Integration**
- Iteratively selects hypotheses based on confidence-to-cost ratio and impact
- Gathers and links evidence using typed edges (causal, temporal, correlative)
- Updates confidence vectors using Bayesian methods with statistical power assessment
5. **✂️ Pruning/Merging**
- Removes nodes with low confidence and impact scores
- Consolidates semantically similar nodes
- Optimizes graph structure while preserving critical relationships
6. **🔍 Subgraph Extraction**
- Identifies high-value subgraphs based on multiple criteria
- Focuses on nodes with high confidence and impact scores
- Extracts patterns relevant to the original query
7. **📝 Composition**
- Synthesizes findings into coherent narrative
- Annotates claims with node IDs and edge types
- Provides comprehensive answers with proper citations
8. **🤔 Reflection**
- Performs comprehensive quality audit
- Evaluates coverage, bias detection, and methodological rigor
- Provides final confidence assessment and improvement recommendations
### Advanced Technical Capabilities
<div align="center">
<table>
<tr>
<td align="center">🔄 <b>Multi-Dimensional<br>Confidence</b></td>
<td align="center">🧠 <b>Graph-Based<br>Knowledge</b></td>
<td align="center">🔌 <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">⚙️ <b>Configuration<br>Management</b></td>
<td align="center">🔒 <b>Type<br>Safety</b></td>
</tr>
<tr>
<td align="center">🌐 <b>Interdisciplinary<br>Bridge Nodes</b></td>
<td align="center">🔗 <b>Hyperedge<br>Support</b></td>
<td align="center">📊 <b>Statistical<br>Power Analysis</b></td>
<td align="center">🎯 <b>Impact<br>Estimation</b></td>
</tr>
</table>
</div>
### 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
<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>
*For detailed setup, usage, and contribution guidelines, please refer to the respective sections in this documentation.*