ai

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npx skills add vercel-labs/py-ai --skill ai

Documentation

SKILL.md

npx skills add ...

Documentation

SKILL.md

AI SDK for Python (the `ai` package). Use when writing Python that calls LLMs or dedicated image, video, speech, embedding, transcription, or reranking models; builds agents; tests model interactions; or implements tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.

This repo is now called vercel-labs/ai-python. Both names install the same content, but the install count here only covers this one.

npx skills add vercel-labs/py-ai --skill ai

Documentation

SKILL.mdView source

AI SDK for Python

Package: ai. Requires Python 3.12+. Install with uv add ai.

Unprefixed model IDs use AI Gateway and AI_GATEWAY_API_KEY. Direct providers use provider:model, their API key, and an extra:

uv add "ai[openai]"     # OPENAI_API_KEY,    ai.get_model("openai:gpt-5")
uv add "ai[anthropic]"  # ANTHROPIC_API_KEY, ai.get_model("anthropic:claude-sonnet-4")

Basic use

Use ai.stream for one model call without Python tool execution:

import ai
 
model = ai.get_model("anthropic/claude-sonnet-4")
messages = [
    ai.system_message("Be concise."),
    ai.user_message("Write a haiku about rain."),
]
 
async with ai.stream(model, messages) as stream:
    async for event in stream:
        if isinstance(event, ai.events.TextDelta):
            print(event.chunk, end="", flush=True)
 
answer = stream.output
message = stream.message

Use ai.Agent for a loop that executes Python tools and manages history:

@ai.tool
async def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return "Sunny"
 
 
agent = ai.Agent(tools=[get_weather])
async with agent.run(model, messages) as run:
    async for event in run:
        if isinstance(event, ai.events.TextDelta):
            print(event.chunk, end="", flush=True)
 
answer = run.output
history = run.messages

These examples are sufficient for basic model calls, messages, tools, and agents.

Advanced work

For an advanced task, fetch its page under https://ai-python.dev/docs/ and read the listed local notes before writing code.

Task	Page	Local notes
Provider clients, options, discovery	`basics/providers.md`	—
Structured output, complex streams	`basics/streaming.md`	—
Buffered language-model calls	`basics/streaming.md`	—
Images, video, speech, embeddings, transcription, reranking	`basics/model-operations.md`	—
Events and serialization	`basics/messages-and-events.md`	—
Advanced tools, streaming, aggregation	`basics/tools.md`	streaming-tools.md
Advanced agent behavior	`basics/agents.md`	—
Deterministic model and agent tests	`basics/testing.md`	—
Subagents and multi-agent	`basics/subagents-and-multi-agent.md`	streaming-tools.md
Custom agent loops	`basics/custom-loops.md`	custom-loops.md
Approvals and hooks	`basics/human-in-the-loop.md`	—
Serverless resume	`basics/human-in-the-loop.md`	serverless.md
Durable execution	`basics/durable-execution.md`	durable.md
Telemetry and tracing	`basics/telemetry.md`	—
AI SDK UI backends	`basics/ai-sdk-ui.md`	ui.md
Custom providers	`basics/providers.md`	custom-provider.md

For exact APIs, use reference.md and the relevant reference/*.md page.

npx skills add vercel-labs/py-ai --skill ai

Documentation

SKILL.md

This repo is now called vercel-labs/ai-python. Both names install the same content, but the install count here only covers this one.

npx skills add vercel-labs/py-ai --skill ai

Documentation

SKILL.mdView source

AI SDK for Python

Package: ai. Requires Python 3.12+. Install with uv add ai.

Unprefixed model IDs use AI Gateway and AI_GATEWAY_API_KEY. Direct providers use provider:model, their API key, and an extra:

uv add "ai[openai]"     # OPENAI_API_KEY,    ai.get_model("openai:gpt-5")
uv add "ai[anthropic]"  # ANTHROPIC_API_KEY, ai.get_model("anthropic:claude-sonnet-4")

Basic use

Use ai.stream for one model call without Python tool execution:

import ai
 
model = ai.get_model("anthropic/claude-sonnet-4")
messages = [
    ai.system_message("Be concise."),
    ai.user_message("Write a haiku about rain."),
]
 
async with ai.stream(model, messages) as stream:
    async for event in stream:
        if isinstance(event, ai.events.TextDelta):
            print(event.chunk, end="", flush=True)
 
answer = stream.output
message = stream.message

Use ai.Agent for a loop that executes Python tools and manages history:

@ai.tool
async def get_weather(city: str) -> str:
    """Get the weather for a city."""
    return "Sunny"
 
 
agent = ai.Agent(tools=[get_weather])
async with agent.run(model, messages) as run:
    async for event in run:
        if isinstance(event, ai.events.TextDelta):
            print(event.chunk, end="", flush=True)
 
answer = run.output
history = run.messages

These examples are sufficient for basic model calls, messages, tools, and agents.

Advanced work

For an advanced task, fetch its page under https://ai-python.dev/docs/ and read the listed local notes before writing code.

Task	Page	Local notes
Provider clients, options, discovery	`basics/providers.md`	—
Structured output, complex streams	`basics/streaming.md`	—
Buffered language-model calls	`basics/streaming.md`	—
Images, video, speech, embeddings, transcription, reranking	`basics/model-operations.md`	—
Events and serialization	`basics/messages-and-events.md`	—
Advanced tools, streaming, aggregation	`basics/tools.md`	streaming-tools.md
Advanced agent behavior	`basics/agents.md`	—
Deterministic model and agent tests	`basics/testing.md`	—
Subagents and multi-agent	`basics/subagents-and-multi-agent.md`	streaming-tools.md
Custom agent loops	`basics/custom-loops.md`	custom-loops.md
Approvals and hooks	`basics/human-in-the-loop.md`	—
Serverless resume	`basics/human-in-the-loop.md`	serverless.md
Durable execution	`basics/durable-execution.md`	durable.md
Telemetry and tracing	`basics/telemetry.md`	—
AI SDK UI backends	`basics/ai-sdk-ui.md`	ui.md
Custom providers	`basics/providers.md`	custom-provider.md

For exact APIs, use reference.md and the relevant reference/*.md page.