ArchGraph

An architecture-graph driven framework for Agentic Engineering.

What is this?

ArchGraph builds a unified language that puts harness design and target product design into one model — so you get a single view to work and observe, and real control over your agents.

Architecture

The global architecture (Layered Viewpoint) shows how the human, the coding agent, ARGO MCP, the intent architecture graph, ArchiMate 3.2, and Enterprise Architect relate in graph-driven agentic engineering:

Editable source: docs/diagrams/global-architecture.excalidraw

Supported Harnesses

ArchGraph deploys the ARGO toolchain to all major coding-agent environments:

| Harness | MCP Server | Skills | Rules / Instructions | Agents | Wakeup Gate | |--------------------|:----------:|:------:|:--------------------:|:------:|:-----------:| | GitHub Copilot | ✓ | ✓ | ✓ | ✓ | — | | Cursor | ✓ | ✓ | ✓ | ✓ | — | | OpenCode | ✓ | ✓ | ✓ | ✓ | ✓ | | DeepSeek Harness | ✓ | ✓ | ✓ | ✓ | ✓ | | OpenClaw | ✓ | ✓ | ✓ | — | ✓ |

A single argo-deploy registers the argo MCP server and installs all artifacts into each harness automatically.

Install

npm install -g archgraph-argo
argo-deploy

Done — the ARGO toolchain, skills, and rules are deployed, and the argo MCP server is registered automatically in GitHub Copilot, Cursor, OpenCode, DeepSeek Harness (dsh), and OpenClaw.

Semantic (Graph RAG) queries also need Neo4j and a vector engine configured in ~/.argo/.env; everything else works out of the box.

How to use

After installing, open your project and start a coding agent. It will:

1. locate the architecture element behind the task before changing anything,
2. arm itself with that element's Skills and Rules,
3. work test-first (GIVEN-WHEN-THEN), and trace every commit back to the graph.

The intent architecture graph — modelled in ArchiMate 3.2 — is the single source of truth.

License

Apache License 2.0