vercel-ai-sdk 0.0.1.dev1

The AI Toolkit for Python

ai-sdk

A Python version of the AI SDK.

Quick Start

uv add ai-sdk

import ai_sdk as ai

llm = ai.openai.OpenAIModel(model="gpt-4", api_key="...")

async for msg in ai.execute(my_agent, llm, "Hello"):
    print(msg.text)

API Reference

@ai.tool

Decorator that turns an async function into a tool. Parameters are auto-extracted from type hints and docstrings become the tool description.

@ai.tool
async def get_weather(city: str, units: str = "celsius") -> str:
    """Get current weather for a city."""
    return f"72°F in {city}"

ai.stream_text(llm, messages, label=None)

Streams text from the LLM without tool support. Returns a Stream that can be awaited or async-iterated.

result = await ai.stream_text(llm, messages)
# or iterate for real-time updates
async for msg in ai.stream_text(llm, messages):
    print(msg.text_delta, end="")

ai.stream_loop(llm, messages, tools, label=None)

Streams LLM responses and automatically executes tool calls in a loop until complete. This is the main function for agentic workflows.

result = await ai.stream_loop(llm, messages, tools=[get_weather])

ai.execute(root_fn, *args)

Runs an agent function and yields all messages from nested streams. Use this as the top-level entry point for any agent workflow.

async def my_agent(llm, query):
    return await ai.stream_loop(llm, messages, tools)

async for msg in ai.execute(my_agent, llm, "What's the weather?"):
    print(msg)

ai.Message

Universal message type with role ("user", "assistant", "system") and parts. Access text via msg.text. The label field tags messages for multi-agent routing.

ai.TextPart, ai.ToolPart, ai.ReasoningPart

Message parts. TextPart holds text content. ToolPart contains tool invocation details and results. ReasoningPart holds model reasoning/thinking output.

MCP Integration

ai.mcp.get_http_tools(url, headers={}, tool_prefix="")

Connects to an MCP server over HTTP and returns tools. Optional tool_prefix namespaces tool names.

tools = await ai.mcp.get_http_tools(
    "https://mcp.example.com/mcp",
    headers={"API_KEY": "..."},
    tool_prefix="docs"
)

ai.mcp.get_stdio_tools(cmd, *args, tool_prefix="")

Spawns an MCP server process via stdio. Useful for local MCP servers like npx packages.

tools = await ai.mcp.get_stdio_tools(
    "npx", "-y", "@upstash/context7-mcp",
    "--api-key", os.environ["CONTEXT7_API_KEY"],
    tool_prefix="context7"
)

Multi-Agent Example

async def multiagent(llm, query):
    # Run two agents in parallel
    stream1, stream2 = await asyncio.gather(
        ai.stream_loop(llm, msgs1, tools=[add_one], label="agent1"),
        ai.stream_loop(llm, msgs2, tools=[multiply], label="agent2"),
    )

    # Combine results and summarize
    combined = stream1[-1].text + stream2[-1].text
    return await ai.stream_text(llm, make_messages(combined), label="summarizer")

async for msg in ai.execute(multiagent, llm, "10"):
    print(f"[{msg.label}] {msg.text_delta}", end="")