ai-wingman
The right* way to wirestreaming chatinto your Next.js app.
Scaffolds production-ready AI patterns into your existing project — chat, RAG, agents, tools, audio, multimodal, evals, and more. No lock-in. Just plain TypeScript files you own.
npx ai-wingman add chatHow it works
ai-wingman wires together Vercel AI SDK, shadcn/ui, and ai-elements into 18 battle-tested AI patterns — then generates the files and exits. There is no runtime dependency on ai-wingman after scaffolding. Pick the pattern that matches your use case, or combine several to build a complete product.
Use with your AI coding assistant
Add the ai-wingman skill file to your AI coding assistant so it knows how to scaffold patterns for you — which command to run, which flags to pass, and how to combine patterns for common use cases.
npx ai-wingman skillDownloads SKILL.md to your current directory. Add it to your assistant's context or skills directory — once loaded, your assistant will use wingman whenever you ask it to add an AI feature to your project.
Patterns
Use cases
Most real products combine two or more patterns. Here are ten reference use cases — each one maps directly to a set of patterns you can scaffold with a few commands.
Customer support copilot
Answers questions from your docs, remembers each user's history across sessions, and blocks policy-violating messages before they reach the model.
Voice-powered knowledge base
Speak a question, get an answer grounded in your documents. Transcribe audio input, retrieve the most relevant chunks, stream the grounded response.
Contract review pipeline
Upload a PDF, extract key terms and clauses as typed JSON, flag risky content against a policy, and require explicit human sign-off before proceeding.
Personalised research assistant
An autonomous agent that runs multi-step research, surfaces the most relevant results via hybrid keyword + vector search, and remembers your preferences across sessions.
Async research report
Kick off a long-running research task without hitting serverless timeouts. An orchestrator delegates sub-topics to specialist agents and synthesises the findings.
Generative dashboard
A natural language query triggers tool calls that fetch live data. Results stream back and render progressively as cards, charts, and tables — no page reload.
Photo-to-structured data
Upload a photo of a receipt, business card, or whiteboard. The model reads the visual content and returns a typed, validated JSON object your app can use directly.
Content creation studio
Draft copy and generate matching visuals from a single prompt. Stream the text field-by-field as it generates while the image renders alongside it.
Adaptive onboarding flow
An assistant that remembers what each user has already completed and renders personalised next-step components — checklists, tips, or prompts — as the conversation progresses.
AI deployment quality gate
Every pull request runs a dataset eval. A structured output captures per-record judge scores, pass/fail status, and mean score — ready to log, diff, or display in a dashboard.
Is this right for you?
Good fit
- ✓Streaming chat assistant or copilot
- ✓Structured data extraction from input or documents
- ✓PDF and file document intelligence pipeline
- ✓Agent that calls your APIs from natural language
- ✓Knowledge base Q&A grounded in your own data
- ✓Multimodal features that understand images and files
- ✓Voice interfaces — speech-to-text, text-to-speech
- ✓Automated quality gates for AI outputs
- ✓Per-user long-term memory across sessions
- ✓Hybrid keyword + vector search with reranking
- ✓Content moderation and policy classification layer
- ✓AI-generated UI components streamed in real time
- ✓Image generation with display and export
- ✓Multi-agent system with orchestrator + specialist workers
- ✓Background agent running outside the request cycle
- →Video generation with prompt and playbackcoming soon
- →AI layer for real-time collaborative editingcoming soon
- →React Native and Expo mobile appscoming soon
- →Vue, Svelte, and Angular supportcoming soon
Not a fit
- ✕Model training, fine-tuning, or data science pipelinesA completely different toolchain (Python, PyTorch, Jupyter) that is already well-served by dedicated frameworks.