🧠 Adaptive Graph of Thoughts

Name: 🧠 Adaptive Graph of Thoughts
Author: SaptaDey

@SaptaDey

Repository Homepage

About 🧠 Adaptive Graph of Thoughts

LLM Reasoning Framework for Scientific Research

#graph-algorithms #mcp #neo4j-graph #graph-of-thoughts #mcp-server

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "Adaptive-Graph-of-Thoughts-MCP-server": {
      "command": "python",
      "args": [
        "src/adaptive_graph_of_thoughts/main.py"
      ]
    }
  }
}

Tools

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Overview

What is 🧠 Adaptive Graph of Thoughts?

Adaptive Graph of Thoughts (AGoT) is a high-performance MCP server that implements the Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) framework. It uses a Neo4j graph database as a dynamic knowledge store and exposes reasoning capabilities through the Model Context Protocol (MCP), enabling seamless integration with AI assistants like Claude Desktop.

How to use 🧠 Adaptive Graph of Thoughts?

Clone the repository, install dependencies with Poetry, run the interactive setup wizard (poetry run python -m agt_setup), then start the server with Uvicorn (poetry run uvicorn adaptive_graph_of_thoughts.main:app --reload). For production, use Docker Compose or a Kubernetes Helm chart. Connect via MCP clients using the /mcp endpoint with Bearer authentication.

Key features of 🧠 Adaptive Graph of Thoughts

8-stage graph-based reasoning pipeline with dynamic confidence scoring
Real-time evidence integration from PubMed, Google Scholar, and Exa Search
High-performance async FastAPI server backed by Neo4j
Native MCP support for Claude Desktop and VS Code
Full Docker and Kubernetes (Helm) deployment support
Interactive setup wizard for credentials and configuration

Use cases of 🧠 Adaptive Graph of Thoughts

Multi-step scientific question answering with evidence-backed reasoning
Hypothesis generation, pruning, and merging from research literature
Construction and exploration of knowledge graph connectomes from queries
Synthesis of conclusions with quantified confidence and audit trails
Cloud-native deployment for scalable scientific AI reasoning

FAQ from 🧠 Adaptive Graph of Thoughts

What are the runtime dependencies for 🧠 Adaptive Graph of Thoughts?

A running Neo4j instance with the APOC library installed is required, along with Python 3.11+, Poetry, and the dependencies defined in pyproject.toml.

How is authentication handled?

All MCP API endpoints require Bearer token authentication. A Bearer token is verified by the Auth Middleware before any request reaches the GoT Processor.

Which external data sources does 🧠 Adaptive Graph of Thoughts integrate with?

It integrates with PubMed, Google Scholar, and Exa Search for real-time evidence retrieval during the reasoning pipeline.

Can 🧠 Adaptive Graph of Thoughts be deployed in containers or cloud environments?

Yes. The project provides a Dockerfile, a production docker-compose.prod.yml, and a Kubernetes Helm chart in the helm/ directory for cloud-ready deployment.

Where does reasoning data live?

All reasoning state (nodes, relationships, sessions) is stored in a Neo4j graph database, either local or remote, as configured in config/settings.yaml or environment variables.

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