MCP Run Python

How Python does AI

uv add pydantic-ai pydantic-ai-harness

from pydantic_ai import Agent
from pydantic_ai.capabilities import WebSearch
from pydantic_ai_harness import Advisor, Coder

agent = Agent(
    'anthropic:claude-fable-5',
    capabilities=[
        Coder(),  # files, shell, repo context, planning, sub-agents, context management
        WebSearch(),  # look up docs and error messages on the web
        Advisor('openai:gpt-5.6-sol'),  # a second opinion from another model when stuck
    ],
)
agent.to_cli_sync()

capabilities = [
    FileSystem('.'), Shell(cwd='.'), RepoContext(), Planning(), SubAgents(...),
    ClearToolResults(), WarnNearLimits(), ToolOutputLimits(),
]

uvx --with pydantic-ai-harness clai -a pydantic_ai_harness.coder:coder_agent -m anthropic:claude-fable-5

uv add pydantic-ai

from typing import Literal

from pydantic import BaseModel, Field

from pydantic_ai import Agent, RunContext


class Sentiment(BaseModel):
    label: Literal['positive', 'negative', 'neutral']
    score: float = Field(ge=-1, le=1)


agent = Agent('openai:gpt-5.6-sol', output_type=Sentiment)


@agent.tool
def recent_reviews(ctx: RunContext[None], product: str) -> list[str]:
    """Fetch recent review snippets for a product."""
    return ['The new release fixed everything I complained about!']


result = agent.run_sync('How are people feeling about the Extract app?')
print(result.output)
#> label='positive' score=0.9

uv add "pydantic-ai[temporal]"

from temporalio import workflow

from pydantic_ai import Agent
from pydantic_ai.capabilities import WebFetch, WebSearch
from pydantic_ai.durable_exec.temporal import PydanticAIWorkflow, TemporalDurability

agent = Agent(
    'openai:gpt-5.6-sol',
    instructions='Research the topic and write a structured brief.',
    name='researcher',
    capabilities=[WebSearch(), WebFetch(), TemporalDurability()],
)


@workflow.defn
class ResearchWorkflow(PydanticAIWorkflow):
    __pydantic_ai_agents__ = [agent]

    @workflow.run
    async def run(self, topic: str) -> str:
        result = await agent.run(f'Write a brief on: {topic}')
        return result.output

uv add "pydantic-ai[openai-realtime]"

import asyncio

from pydantic_ai import Agent
from pydantic_ai.capabilities import MCP

agent = Agent(
    instructions='You are a helpful voice assistant.',
    capabilities=[MCP('https://internal.example.com/mcp')],  # capabilities work in voice too
)

@agent.tool_plain
def order_status(order_id: str) -> str:
    """Look up the status of an order."""
    return f'Order {order_id}: shipped, arriving Thursday.'

async with agent.realtime('openai:gpt-realtime-2.1').session() as session:
    microphone = asyncio.create_task(session.send_audio(microphone_chunks()))  # your microphone → the model
    speaker = asyncio.create_task(play_audio(session.stream_audio()))  # model audio → your speaker
    async for part in session.stream_transcripts():
        print(f'{part.speaker}: {part.transcript}')