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import asyncio
import json
import os

from azure.ai.evaluation import AzureOpenAIModelConfiguration, IntentResolutionEvaluator
from azure.identity import DefaultAzureCredential, get_bearer_token_provider
from dotenv import load_dotenv

from agentframework_learn import run_agent

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# ------------------- Stage 0: Base Stage ------------------------------
FROM python:3.13-alpine AS base

# Install tini, a tiny init for containers
RUN apk add --update --no-cache tini

# Install required packages for cryptography and ml-dtypes packages
# https://cryptography.io/en/latest/installation/#building-cryptography-on-linux
RUN apk add gcc g++ musl-dev python3-dev libffi-dev openssl-dev cargo pkgconfig

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import os

from anthropic import AnthropicFoundry
from dotenv import load_dotenv

load_dotenv()

endpoint = "https://pf-claude-foundry-proje-resource.openai.azure.com/anthropic"
deployment_name = "claude-sonnet-4-5"

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"""Compare documents across two Azure AI Search indexes"""

import argparse
import asyncio
import logging
import os
from collections.abc import Iterable, Mapping
from dataclasses import dataclass, field
from typing import Any, cast

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Agents using MCP servers

Framework	MCP integration	Tool filtering?	Example
Langchain	MultiServerMCPClient accepts a dictionary of MCP servers	Filter with get_tools() after connecting to servers	Example
Agent-framework	MCPStreamableHTTPTool for each MCP server	Filter with allowed_tools on that class, or tools on agent.run()	Example
Pydantic AI	MCPServerStreamableHTTP for each MCP server	Apply .filtered() method to the server and use result as a toolset for agent	Example

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YouTube Talk to Article – Lean MVP Technical Spec

Derived from the original PRD but intentionally simplified for fastest viable implementation. Focus: “folder in, article out” with minimal config, vision-first extraction, and straightforward alignment (no embeddings initially).

1. MVP Goal & Non-Goals

Goal: Given a folder containing a deck.pptx (and optionally a transcript + config.yaml), produce article.md plus slide images using a simple CLI: talk2article <folder>.

Included in MVP:

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description	applyTo
Python coding conventions and guidelines	**/*.py

Python Coding Conventions

Python Instructions

• Write clear and concise comments for each function.

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from langchain.agents.middleware import AgentMiddleware, AgentState, ModelRequest

class ToolCallLimitMiddleware(AgentMiddleware):
    def __init__(self, limit) -> None:
        super().__init__()
        self.limit = limit

    def modify_model_request(
        self, request: ModelRequest, state: AgentState
    ) -> ModelRequest:

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import json
import os

import azure.identity
import openai
from dotenv import load_dotenv
from rich import print

# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
load_dotenv(override=True)

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#!/usr/bin/env python3
"""
Find characters whose repetitions tokenize to one token per character with tiktoken.

A character c is considered "stable single-token" if:
  len(encode(c)) == 1  AND  for all k in [1, max_reps], len(encode(c * k)) == k.

Usage (from repo root with virtual env active):
  python find_single_token_char.py
  python find_single_token_char.py --model text-embedding-3-large --max-reps 32