# 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.

It doubles as a **long-term memory for coding agents**: an ArchiMate 3.2 intent graph exposed through
a single read/write MCP interface. Writes are deduplicated, so the graph stays clean and semantic
recall stays precise. See the [home page](https://archgraph.org/) for the full capability set.

![alt text](docs/diagrams/image.png)

## 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:

![Global architecture — Layered Viewpoint](docs/diagrams/global-architecture.svg)

Editable source: [`docs/diagrams/global-architecture.excalidraw`](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

```powershell
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`.
  It points at **any OpenAI-compatible embedding endpoint** — a cloud provider, or a self-hosted
  server for offline / intranet / private deployments via `ARGO_EMBEDDING_PROFILE=openai-compatible`
  (see the [self-hosted embedding guide](docs/self-hosted-embedding-deployment.md)).

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 — or from a self-hosted
OpenAI-compatible server. `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,
4. reuse an existing element, relationship, or view instead of creating a duplicate — the write path
   deduplicates by identity and flags a semantically near element of the same type.

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](LICENSE)