# GreatArrow.ai for vibe coders

> The software-building workflow: project memory, agent tasks, GitHub and Vercel context, cost guardrails and agent-discoverable launch pages.

Canonical page: https://www.greatarrow.ai/vibe-coders
Site map for agents: https://www.greatarrow.ai/llms.txt (full text: https://www.greatarrow.ai/llms-full.txt)

What people build with it: SaaS MVPs, Internal tools, AI workflows, Client portals, Automation dashboards, Launch pages.

## What you get

### Project memory that survives every chat

Decisions, docs, repo notes, specs, and conversations become searchable workspace memory that Claude, ChatGPT, Cursor, Codex, and other clients can reuse.

### 21 agents for the build loop

Use specialist agents for planning, docs, triage, meetings, reviews, CRM, and execution while the orchestrator keeps the work threaded. Agents run on Pro and above.

### Kanban tasks agents can pick up

Turn product feedback, meeting notes, GitHub work, and roadmap ideas into board cards with labels, priorities, context, and agent ownership.

### 571 MCP actions

Expose memory, tasks, meetings, documents, GitHub actions, Vercel context, and business workflows to any compatible AI client through scoped MCP tools.

### GitHub and deployment-aware workflows

Create issues, draft PRs, review work, trigger Actions, and keep deployment status visible alongside the product plan.

### Cost guardrails for AI-heavy builds

Track usage by workspace, agent, model, and MCP client so an experimental build sprint does not become a surprise invoice.

## The ship loop

1. **Capture the idea** — Drop specs, screenshots, customer notes, and repo context into one workspace memory.
2. **Plan the next shippable slice** — Ask an agent to break the idea into tickets, acceptance criteria, docs, and implementation order.
3. **Build with connected tools** — Use MCP actions for GitHub, documents, tasks, meetings, web context, and deployment-aware operations.
4. **Launch and keep learning** — Publish SEO-ready pages, expose llms.txt context, and route user reports back into the app board.

## Agent discovery

- **llms.txt** — A plain-text product map for AI crawlers and agentic browsers, including software-building capabilities and access URLs.
- **MCP endpoint** — OAuth-secured Streamable HTTP MCP endpoint at /api/mcp with scoped tools for read, write, agents, integrations, and admin.
- **Structured data** — SoftwareApplication, WebPage, FAQ, and ItemList JSON-LD so search engines and answer agents can identify the offer.

## Questions

### Is GreatArrow.ai for technical founders or non-technical vibe coders?

Both. Technical builders get MCP tools, repo context, GitHub workflows, and deployment visibility. Non-technical founders get a project memory, task board, agents, and launch workflows that make AI-assisted software building less fragile.

### Can agents remember the whole software project?

GreatArrow.ai stores decisions, documents, transcripts, tasks, and connected integration context in a shared workspace memory, then exposes that memory to supported AI clients through MCP and app surfaces.

### Does it help after the product launches?

Yes. Reports can become board cards, agent runs are logged, costs are tracked, docs stay searchable, and SEO or agent-discovery pages can stay aligned with the product surface.