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vercel-labs/ai-python160 installs

ai

AI SDK for Python (the `ai` package). Use for Python model calls (LLMs, image, video, speech, embedding, transcription, reranking, or evaluation), agents, model interaction tests, tool calling, subagents, approvals, durable execution, telemetry, AI SDK UI backends, and custom providers.

How do I install this agent skill?

npx skills add https://github.com/vercel-labs/ai-python --skill ai
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    This skill provides a Python SDK for building AI agents and model interactions. It includes standard security considerations for LLM-based tools, such as the handling of untrusted input and the execution of external tools. The SDK includes built-in mechanisms for argument validation and approval gates to help manage these risks.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

What does this agent skill do?

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.

TaskPageLocal notes
Provider clients, options, discoverybasics/providers.md—
Structured output, complex streamsbasics/streaming.md—
Buffered language-model callsbasics/streaming.md—
Images, video, speech, embeddings, transcription, reranking, evaluationbasics/model-operations.md—
Events and serializationbasics/messages-and-events.md—
Advanced tools, streaming, aggregationbasics/tools.mdstreaming-tools.md
Advanced agent behaviorbasics/agents.md—
Deterministic model and agent testsbasics/testing.md—
Subagents and multi-agentbasics/subagents-and-multi-agent.mdstreaming-tools.md
Custom agent loopsbasics/custom-loops.mdcustom-loops.md
Approvals and hooksbasics/human-in-the-loop.md—
Serverless resumebasics/human-in-the-loop.mdserverless.md
Durable executionbasics/durable-execution.mddurable.md
Telemetry and tracingbasics/telemetry.md—
AI SDK UI backendsbasics/ai-sdk-ui.mdui.md
Custom providersbasics/providers.mdcustom-provider.md

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

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

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