@arizeai/phoenix-mcp

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "SUknhuHKyc-E" }, "source": [ "# <center>OpenAI agent pattern: parallelization</center>\n", "\n", "A starter guide for building an agent with parallel LLM and tool calling using the `openai-agents` library.\n", "\n", "In this example, we are building a stock research agent. If we want to research 5 stocks, we can force the agent to run multiple tool calls, instead of sequentially." ] }, { "cell_type": "markdown", "metadata": { "id": "n69HR7eJswNt" }, "source": [ "### Install Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install base libraries for OpenAI\n", "!pip install -q openai openai-agents yfinance\n", "\n", "# Install optional libraries for OpenInference/OpenTelemetry tracing\n", "!pip install -q arize-phoenix-otel openinference-instrumentation-openai-agents openinference-instrumentation-openai" ] }, { "cell_type": "markdown", "metadata": { "id": "jQnyEnJisyn3" }, "source": [ "### Setup Keys\n", "\n", "Add your OpenAI API key to the environment variable `OPENAI_API_KEY`.\n", "\n", "Copy your Phoenix `API_KEY` from your settings page at [app.phoenix.arize.com](https://app.phoenix.arize.com)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from getpass import getpass\n", "\n", "if \"OPENAI_API_KEY\" not in os.environ:\n", " os.environ[\"OPENAI_API_KEY\"] = getpass(\"🔑 Enter your OpenAI API key: \")\n", "\n", "if \"PHOENIX_API_KEY\" not in os.environ:\n", " os.environ[\"PHOENIX_API_KEY\"] = getpass(\"🔑 Enter your Phoenix API key: \")\n", "\n", "if \"PHOENIX_COLLECTOR_ENDPOINT\" not in os.environ:\n", " os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = getpass(\"🔑 Enter your Phoenix Collector Endpoint\")" ] }, { "cell_type": "markdown", "metadata": { "id": "kfid5cE99yN5" }, "source": [ "### Setup Tracing" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from phoenix.otel import register\n", "\n", "tracer_provider = register(\n", " project_name=\"openai-agents\",\n", " auto_instrument=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating your agent" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "\n", "import yfinance as yf\n", "\n", "\n", "def get_stock_data(ticker_symbol):\n", " stock = yf.Ticker(ticker_symbol)\n", " data = stock.info\n", " return {\n", " \"symbol\": data.get(\"symbol\"),\n", " \"current_price\": data.get(\"currentPrice\"),\n", " \"market_cap\": data.get(\"marketCap\"),\n", " \"sector\": data.get(\"sector\"),\n", " \"industry\": data.get(\"industry\"),\n", " \"description\": data.get(\"longBusinessSummary\"),\n", " \"trailing_pe\": data.get(\"trailingPE\"),\n", " \"forward_pe\": data.get(\"forwardPE\"),\n", " \"dividend_yield\": data.get(\"dividendYield\"),\n", " \"beta\": data.get(\"beta\"),\n", " \"fifty_two_week_high\": data.get(\"fiftyTwoWeekHigh\"),\n", " \"fifty_two_week_low\": data.get(\"fiftyTwoWeekLow\"),\n", " \"fifty_day_moving_average\": data.get(\"fiftyDayAverage\"),\n", " \"two_hundred_day_moving_average\": data.get(\"twoHundredDayAverage\"),\n", " \"recommendation_key\": data.get(\"recommendationKey\"),\n", " \"revenue_growth\": data.get(\"revenueGrowth\"),\n", " \"earnings_growth\": data.get(\"earningsGrowth\"),\n", " \"profit_margins\": data.get(\"profitMargins\"),\n", " }\n", "\n", "\n", "pprint(get_stock_data(\"AAPL\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pprint import pprint\n", "from textwrap import dedent\n", "\n", "from agents import Agent, Runner, TResponseInputItem, WebSearchTool, function_tool\n", "from agents.model_settings import ModelSettings\n", "\n", "\n", "@function_tool\n", "def get_stock_data_tool(ticker_symbol: str) -> dict:\n", " \"\"\"\n", " Get stock data for a given ticker symbol.\n", " Args:\n", " ticker_symbol: The ticker symbol of the stock to get data for.\n", " Returns:\n", " A dictionary containing stock data such as price, market cap, and more.\n", " \"\"\"\n", " return get_stock_data(ticker_symbol)\n", "\n", "\n", "research_agent = Agent(\n", " name=\"FinancialSearchAgent\",\n", " instructions=dedent(\n", " \"\"\"You are a research assistant specializing in financial topics. Given a stock ticker, use web search to retrieve up‑to‑date context and produce a short summary of at most 50 words. Focus on key numbers, events, or quotes that will be useful to a financial analyst.\"\"\"\n", " ),\n", " model=\"gpt-4.1\",\n", " tools=[WebSearchTool(), get_stock_data_tool],\n", " model_settings=ModelSettings(tool_choice=\"required\", parallel_tool_calls=True),\n", ")\n", "\n", "user_input = input(\"Enter the stock tickers you want to research: \")\n", "input_items: list[TResponseInputItem] = [{\"content\": user_input, \"role\": \"user\"}]\n", "\n", "orchestrator = await Runner.run(research_agent, input_items)\n", "orchestrator_output = orchestrator.final_output\n", "pprint(orchestrator_output)" ] } ], "metadata": { "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }