π§ Adaptive Graph of Thoughts
@SaptaDey
About π§ Adaptive Graph of Thoughts
LLM Reasoning Framework for Scientific Research
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
No tools detected
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
Overview
What is π§ 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 π§ 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 π§ 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 π§ 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 π§ Adaptive Graph of Thoughts
What are the runtime dependencies for π§ 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 π§ 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 π§ 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.
Frequently asked questions
What are the runtime dependencies for π§ 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 π§ 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 π§ 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.
Basic information
More Other MCP servers
ICSS
chokcocoδΈζ’δΊ CSS
πͺ Windows-MCP
CursorTouchMCP Server for Computer Use in Windows
Activepieces
activepiecesAI Agents & MCPs & AI Workflow Automation β’ (~400 MCP servers for AI agents) β’ AI Automation / AI Agent with MCPs β’ AI Workflows & AI Agents β’ MCPs for AI Agents
Codelf
unbugA search tool helps dev to solve the naming things problem.
MCP Registry
modelcontextprotocolA community driven registry service for Model Context Protocol (MCP) servers.
Comments