Skip to main content
Glama
Sign In
ArindamBanerji

graph-attention-engine

by ArindamBanerji
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Hybrid

Graph Attention Engine (GAE)

Standalone, pip-installable Python library implementing Cross-Graph Attention. Zero domain knowledge. Zero external dependencies (numpy only). Apache 2.0.

Install

pip install graph-attention-engine
# or editable dev install:
pip install -e ".[dev]"

Related MCP server: coord-mem MCP Server

Quick start

import numpy as np
from gae import scaled_dot_product_attention

Q = np.random.randn(4, 8)   # (seq, d_k)
K = np.random.randn(6, 8)
V = np.random.randn(6, 16)
out, weights = scaled_dot_product_attention(Q, K, V)
# out: (4, 16)  weights: (4, 6)

Math reference

Full specification: docs/gae_design_v5.md Blog: https://www.dakshineshwari.net/post/cross-graph-attention-mathematical-foundation-with-experimental-validation

MCP Server

Graph Attention Engine includes a built-in Model Context Protocol (MCP) server that exposes decision scoring as MCP tools. Any MCP-compatible client (Claude Desktop, Cursor, etc.) can connect and use GAE's cross-graph attention scoring directly.

Install

pip install "graph-attention-engine[mcp]"

Usage

# stdio transport (for Claude Desktop, Cursor)
rgi-mcp-server --categories alert_triage,incident_response --actions investigate,escalate --factors severity,confidence,blast_radius

# HTTP transport (for remote/hosted deployments)
rgi-mcp-server --http --port 8080 --categories alert_triage,incident_response --actions investigate,escalate --factors severity,confidence,blast_radius

Docker

docker build -f Dockerfile.mcp -t gae-mcp .
docker run -p 8080:8080 gae-mcp

MCP Tools

Tool

Description

score_decision

Score a decision using cross-graph attention and compounding intelligence

get_trajectory

Retrieve the learning trajectory showing how decisions compound over time

get_conservation_status

Check conservation law compliance and scoring integrity

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "graph-attention-engine": {
      "command": "rgi-mcp-server",
      "args": ["--categories", "alert_triage,incident_response", "--actions", "investigate,escalate", "--factors", "severity,confidence,blast_radius"]
    }
  }
}

Testing

pytest tests/ -v

This server cannot be deployed

Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • coord-mem MCP Server

    khalildh
    A
    license
    Not graded
    quality
    D
    maintenance
    Memory-palace graph MCP server for AI agent memory, exposing memory_add / get / like / record_window / walk / retrieve / neighbors / rooms / misshelved / rehome operations over a zero-dependency, append-only, provenance-tracking knowledge graph with decay, PMI-derived adjacency, and spatial coordinates.
    MIT

  • worldbrain-mcp

    genesis-plan
    A
    license
    Not graded
    quality
    F
    maintenance
    Enables AI agents to perform auditable A* reasoning, import world graphs, incorporate physical carrier constraints, and maintain a learning knowledge base through a zero-dependency local MCP stdio service.
    MIT
  • Algenta by thyn-ai

    Algentaofficial

    thyn-ai
    A
    license
    A
    quality
    A
    maintenance
    Official MCP server for Algenta, a local-first AI engine powered by Mojo CPU/GPU kernels, 525+ Mojo libraries and 6,000+ compiled functions. Governed data discovery, exact queries, decisions, simulations, persistent decision memory and agent runs with approvals and execution receipts. Runs on your infrastructure. Source: packages/mcp/algenta_mcp. Install: pip install algenta-mcp (stdio).
    140
    1
    Apache 2.0