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https://github.com/diegonogueiradev/mcp-graph-workflow

MCP local-first CLI tool that converts PRD text files into persistent execution graphs (SQLite) for structured agentic workflows
https://github.com/diegonogueiradev/mcp-graph-workflow

agent-tooling agentic-workflows ai-tools cli local-first mcp mcp-server model-context-protocol open-source prd rag semantic-search sqlite task-graph typescript

Last synced: 19 days ago
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MCP local-first CLI tool that converts PRD text files into persistent execution graphs (SQLite) for structured agentic workflows

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README

          


mcp-graph

mcp-graph


CI
npm
AGPL v3
Commercial license

---

**mcp-graph** turns your PRD into a persistent task graph that your AI agent navigates instead of improvising.

PRD → graph (SQLite) → TDD → production. 100% local, zero cloud.

---

## The problem

Every AI coding session has the same three problems:

- **The agent forgets** — each new chat starts from zero, the plan gets reinvented
- **PRDs become walls of text** — nobody re-reads them, the agent improvises
- **No traceability** — you can't tell what was done, what's blocked, or why

## How it solves them

1. `import_prd` — reads the PRD and creates tasks with acceptance criteria in the graph
2. `start_task` — agent picks the next task with compressed context in hand
3. `finish_task` — validates DoD (12 checks), status → done, suggests the next

Everything lives in `workflow-graph/graph.db` — local SQLite, gitignored, never in the cloud.

## Installation

```bash
npm install -g @mcp-graph-workflow/mcp-graph
```

Add to your project's `.mcp.json` (Claude Code, Cursor, Copilot):

```json
{
"mcpServers": {
"mcp-graph": {
"command": "npx",
"args": ["-y", "@mcp-graph-workflow/mcp-graph"]
}
}
}
```

Initialize in your project:

```bash
mcp-graph init
```

**Prerequisites:** Node.js ≥ 18. No Docker. No LLM API key required.

## Getting started

```bash
# 1. Import a PRD (or create tasks manually)
mcp-graph import my-prd.md

# 2. See the graph
mcp-graph status

# 3. Get next task
mcp-graph next

# 4. Start → implement with TDD → finish
mcp-graph start
# ... write test, implement ...
mcp-graph finish
```

The AI agent does this automatically via MCP tools (`start_task`, `finish_task`, `next`).

## What's inside

| Layer | What it does |
|---|---|
| 54 MCP tools | Graph, tasks, memory, RAG, sprint, deploy |
| 130+ REST endpoints | Web dashboard, IDE integration |
| Local RAG (FTS5 + BM25) | Compressed context without external LLM |
| React dashboard | Kanban, visual graph, logs, insights |
| 9-phase lifecycle | ANALYZE → DESIGN → PLAN → IMPLEMENT → VALIDATE → REVIEW → HANDOFF → DEPLOY → LISTENING |

## Privacy

Zero telemetry. Zero phone-home. Runs offline after installation.

LLM, embeddings, and external integrations are **opt-in** — nothing active by default.

## Documentation

- [Quickstart](docs/getting-started/QUICKSTART.md) — 60 seconds
- [Full guide](docs/getting-started/GUIDE.md)
- [Cheatsheet](docs/getting-started/CHEATSHEET.md) — all commands
- [Architecture](docs/ARCHITECTURE.md)
- [Sample PRD](docs/examples/sample-prd.md)

## License

[AGPL-3.0-or-later](LICENSE) — open source with strong copyleft.
Proprietary commercial use: [commercial license available](COMMERCIAL.md).