archgraph

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.

Prerequisites and configuration

Everything works out of the box except semantic (Graph RAG) queries, which need:

• Neo4j graph database — stores the structural projection of your architecture graph. During
  argo-deploy you configure ARGO_NEO4J_DATABASE_URL, ARGO_NEO4J_DATABASE_USERNAME, and
  ARGO_NEO4J_DATABASE_PASSWORD in ~/.argo/.env.
• Embedding / vector engine — powers semantic Graph RAG retrieval. Configure
  ARGO_EMBEDDING_BASE_URL, ARGO_EMBEDDING_MODEL, ARGO_EMBEDDING_PROVIDER,
  ARGO_EMBEDDING_MODEL_VERSION, ARGO_EMBEDDING_DIMENSIONS, plus the API key QWEN_KEY.

Where do the values come from? The Neo4j credentials come from the Neo4j instance you own or
provision (URI, username, password). The embedding configuration and QWEN_KEY come from your
embedding provider's dashboard — for example Alibaba DashScope. argo-deploy walks you through the
prompt (existing non-empty values in ~/.argo/.env are kept); you can also edit the file afterwards
and re-run.

How to use

Step 0 — initialize the workspace. In a fresh project, ask your coding agent to run argo init
(the initializeWorkspace MCP call). It creates a starter design/KG/SystemArchitecture.json when
missing, performs the first JSON → Neo4j sync, initializes the semantic (Graph RAG) lifecycle, and
verifies the architecture. From then on, the intent graph is the source of truth for the project.

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.

Community

ArchGraph runs on open co-building. Join the community hub to share, browse and reuse architecture
subgraphs across projects, and follow the governance & contribution guides:

• Community site — https://argo.derekworkspacev5.com/archgraph/ (subgraph library, docs, blog)
• graph-wiki repository — https://github.com/derekhu0002/graph-wiki (graph-asset home: contribute
  a subgraph from your project, or pull one back to reuse)

License

Apache License 2.0