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 for the full capability set.
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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
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.
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).
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:
- locate the architecture element behind the task before changing anything,
- arm itself with that element's Skills and Rules,
- work test-first (GIVEN-WHEN-THEN), and trace every commit back to the graph,
- 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:
License
Apache License 2.0
