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Add Provider Package

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vercel
add-provider-package

Guide for adding first-party AI provider packages to the AI SDK. Use when creating a provider package under packages/ to integrate an external AI service.

Overview

Publishervercel
Repositoryai
Skill nameadd-provider-package
Stars
27.1K
Forks
5.2K
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vercel on GitHub. Read the source before you install it.

Installation

Install the Add Provider Package AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vercel/ai/tree/main/skills/add-provider-package
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vercel/ai.git /tmp/ai
mkdir -p .claude/skills
cp -r /tmp/ai/skills/add-provider-package .claude/skills/add-provider-package
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Add Provider Package in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Add Provider Package on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Add Provider Package is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Add a Provider Package

Add a complete first-party @ai-sdk/<provider> package that follows the current provider interfaces, repository conventions, security requirements, and release process.

Read the Current Sources of Truth

Before implementing anything, read:

Use PR #18595 as a recent end-to-end example, but choose the current provider package whose API shape and model types most closely resemble the new provider as the implementation reference.

Third parties can publish provider packages outside this repository. A new first-party @ai-sdk/<provider> package requires prior discussion in an issue. Confirm that agreement exists before implementing the package.

Discover the API Contract

Before designing model classes, look for an official, versioned OpenAPI or Swagger specification in the provider's documentation or repositories. Prefer first-party specifications and record the source URL plus its version, publication date, or commit in the implementation notes or pull request.

Use the specification and official documentation to identify:

  • base URLs and authentication schemes
  • supported endpoints, model types, and capabilities
  • request parameters and response shapes
  • streaming transports and event formats
  • error response envelopes
  • asynchronous polling and download URL flows

Treat an OpenAPI specification as implementation evidence, not unquestioned truth. Specifications are often incomplete for server-sent events, streaming deltas, polymorphic content, tool calls, nullable fields, and errors. Do not add a generated client or generated production types by default. Implement minimal hand-written types and Zod schemas, then verify them against official documentation and captured API responses.

If no official specification exists, derive the contract from official documentation and real response fixtures, and note that limitation in the pull request.

Plan the Provider Shape

Determine which AI SDK model interfaces the provider supports, such as LanguageModelV4, EmbeddingModelV4, ImageModelV4, SpeechModelV4, TranscriptionModelV4, RerankingModelV4, or Experimental_VideoModelV4.

Before introducing a dependency, public API pattern, or new abstraction, read contributing/decisions/README.md and relevant accepted ADRs. Prefer existing provider utilities and implementation patterns.

Scaffold the Package

Create packages/<provider>/ by adapting a current, comparable provider package. A typical package contains:

text
packages/<provider>/
├── src/
│   ├── index.ts
│   ├── version.ts
│   ├── <provider>-provider.ts
│   ├── <provider>-provider.test.ts
│   ├── <provider>-<model-type>-model.ts
│   ├── <provider>-<model-type>-model.test.ts
│   └── <provider>-<model-type>-options.ts
├── CHANGELOG.md
├── README.md
├── package.json
├── tsconfig.json
├── tsconfig.build.json
├── tsdown.config.ts
├── turbo.json
├── vitest.node.config.js
└── vitest.edge.config.js

Preserve current package conventions rather than recreating configuration from memory:

  • Set the repository package version to exactly 2.0.0, with no prerelease suffix.
  • Create CHANGELOG.md with an initial # @ai-sdk/<provider> heading.
  • Use the shared tsdown configuration and the current package-version injection pattern from a comparable provider package.
  • Extend ./node_modules/@vercel/ai-tsconfig/ts-library.json, enable a composite project, and add package references for workspace dependencies.
  • Include the standard build, clean, type-check, Node test, and Edge test scripts.
  • Include the standard files, documentation prepack, repository, bugs, engines, and public provenance publishing metadata.
  • Use workspace:* for AI SDK workspace dependencies. Add @ai-sdk/test-server only when tests use it.
  • Support the repository's Zod 3 and Zod 4 peer dependency range and use zod/v4 for new implementation schemas.

Run pnpm update-references after adding or changing workspace dependencies.

Implement the Provider Factory

Follow the current provider factory pattern:

  • Define a provider interface that extends ProviderV4.
  • Export create<Provider>(settings) and a default provider instance.
  • Make the provider callable when it has a meaningful default model type and comparable providers follow that pattern; otherwise return a provider object.
  • Set provider.specificationVersion = 'v4'.
  • Implement the fully specified factory methods required by ProviderV4, such as languageModel, embeddingModel, and imageModel.
  • Add short aliases such as chat, embedding, or image only when they improve the provider's API.
  • Throw NoSuchModelError from unsupported required model factories.
  • Support provider-appropriate settings such as apiKey, baseURL, headers, and a custom fetch implementation.
  • Load credentials with loadApiKey or the appropriate shared utility, normalize configurable base URLs, and include the package version in the user-agent suffix.
  • Export the provider factory, default instance, public option types, model ID types, and VERSION from src/index.ts.

Implement Model Classes

Implement each supported model using the appropriate interface from @ai-sdk/provider and shared utilities from @ai-sdk/provider-utils.

Provider option types and schemas must follow the repository naming and export rules. User-facing option fields should use .optional() unless null is meaningful. Response schemas should be minimal, tolerate unused provider fields, and use .nullish() where the API may omit or return null.

All model classes must implement the workflow serialization contract described in Provider development notes:

  • make model configuration headers optional
  • add WORKFLOW_SERIALIZE and WORKFLOW_DESERIALIZE static methods
  • use serializeModel, serializeModelOptions, or the matching existing pattern
  • ensure authentication and other non-serializable functions can be restored from request options or the workflow environment

Handle Responses, Errors, and URLs Safely

  • Never use JSON.parse in production code. Use parseJSON or safeParseJSON from @ai-sdk/provider-utils.
  • Validate successful and failed responses with minimal schemas and shared response handlers such as createJsonResponseHandler and createJsonErrorResponseHandler.
  • Introduce a custom AISDKError subclass only when the package needs a new public SDK error type; ordinary provider HTTP failures should use the shared API error handling.
  • Set validateUrl explicitly on every getFromApi call.
  • Use validateUrl: true when the URL host or scheme comes from a provider response, and false when it is derived from a developer-configured base URL.
  • Use trustedOrigin for legitimate response URLs that may point to a configured private or self-hosted endpoint.
  • Use credentialedOrigin when credentials may be sent on the first hop, so they are withheld from off-origin URLs and redirects.

Read Secure URL handling before implementing any polling or provider-supplied download URL flow.

Test Against the Real Contract

Add focused tests for:

  • provider defaults, custom settings, factory aliases, and unsupported model types
  • request serialization and response parsing
  • streaming events, usage, finish reasons, warnings, tool calls, and provider metadata where supported
  • error response parsing and malformed responses
  • workflow serialization and deserialization
  • URL trust decisions for polling or downloads
  • both Node.js and Edge runtimes

Use real provider responses as fixtures when practical. Read the capture API response fixture skill before capturing them. Trim oversized fixtures only when doing so does not change their semantics.

Add Examples and Repository Integration

Read the AI Functions example skill before adding examples.

For each supported model type, put the entry example at:

text
examples/ai-functions/src/<function>/<provider>/basic.ts

Put additional examples in the same provider directory with descriptive kebab-case.ts names. Do not create flat provider files such as src/generate-text/<provider>.ts.

Also update the relevant repository integration points:

  • add @ai-sdk/<provider> to examples/ai-functions/package.json
  • add its project reference to examples/ai-functions/tsconfig.json
  • add required credentials to examples/ai-functions/.env.example
  • add credential names to the root turbo.json environment configuration when needed
  • run pnpm update-references to update root and package TypeScript references

Run representative examples against the real API and confirm both non-streaming and streaming behavior when supported.

Add Package and Provider Documentation

  • Write the package README.md with installation, authentication, configuration, supported models, and basic usage.
  • Add content/providers/01-ai-sdk-providers/<last number + 10>-<provider>.mdx with setup, model capabilities, provider options, and examples.
  • Configure the package's documentation prepack script to include that provider page.

Prepare the Release

Create a major changeset for the new provider package. The repository package remains at plain 2.0.0; do not add -beta, -canary, or another prerelease suffix.

Before the first automated release, coordinate with the Vercel IT team to bootstrap an empty @ai-sdk/<provider> package on npm at 0.0.0 and configure its Trusted Publisher. Follow Bootstrapping a new @ai-sdk/* package. The temporary npm bootstrap version is separate from the repository package version.

When main is in prerelease mode, do not backport the new package to a stable vX.Y branch.

Verify the Complete Change

Run, at minimum:

bash
pnpm --filter @ai-sdk/<provider> build
pnpm --filter @ai-sdk/<provider> test
pnpm --filter @ai-sdk/<provider> type-check
pnpm type-check:full
pnpm check

Also run the new examples with the required provider credentials. Run the root build when changes to shared packages or build configuration make it relevant.

Completion Checklist

  • First-party package approved in an issue
  • Official OpenAPI specification reviewed, or its absence documented
  • API specification checked against documentation and real responses
  • Comparable current provider implementation selected
  • Repository package created at version 2.0.0
  • Package, TypeScript, build, test, provenance, and documentation configuration added
  • Provider factory implements ProviderV4
  • Supported model classes and public option types implemented
  • Workflow serialization implemented for every model class
  • Response parsing, error handling, and URL fetching follow repository security rules
  • Node and Edge tests pass with representative response fixtures
  • Nested AI Functions examples added and run successfully
  • Example dependencies, TypeScript references, environment variables, and Turbo configuration updated
  • README and provider documentation added
  • Major changeset added
  • npm package and Trusted Publisher bootstrap coordinated
  • Package build, tests, full type check, and repository checks pass

Frequently asked questions

What does the Add Provider Package AI skill do?

Guide for adding first-party AI provider packages to the AI SDK. Use when creating a provider package under packages/ to integrate an external AI service.

Why use Add Provider Package on TypingMind?

Because you install it once and use it with any model. Add Provider Package is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Add Provider Package in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel/ai/tree/main/skills/add-provider-package. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Add Provider Package?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Add Provider Package?

As many as you like. As long as a model supports skills, you can use Add Provider Package with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Add Provider Package AI skill free?

It is published on GitHub by vercel. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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