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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ur8xi4C7S06n"
},
"outputs": [],
"source": [
"# Copyright 2024 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JAPoU8Sm5E6e"
},
"source": [
"# Build Your Own AI Podcasting Agent with LangGraph, Gemini, and Chirp 3\n",
"## AI-Powered Podcast Creation with Automated Research, Writing, and Refinement\n",
"\n",
"<table align=\"left\">\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\">\n",
" <img width=\"32px\" src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fgemini%2Forchestration%2Flanggraph_gemini_podcast.ipynb\">\n",
" <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\">\n",
" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\">\n",
" <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
" </a>\n",
" </td>\n",
"</table>\n",
"\n",
"<div style=\"clear: both;\"></div>\n",
"\n",
"<b>Share to:</b>\n",
"\n",
"<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\" target=\"_blank\">\n",
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/8/81/LinkedIn_icon.svg\" alt=\"LinkedIn logo\">\n",
"</a>\n",
"\n",
"<a href=\"https://bsky.app/intent/compose?text=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\" target=\"_blank\">\n",
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg\" alt=\"Bluesky logo\">\n",
"</a>\n",
"\n",
"<a href=\"https://twitter.com/intent/tweet?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\" target=\"_blank\">\n",
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/5a/X_icon_2.svg\" alt=\"X logo\">\n",
"</a>\n",
"\n",
"<a href=\"https://reddit.com/submit?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\" target=\"_blank\">\n",
" <img width=\"20px\" src=\"https://redditinc.com/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png\" alt=\"Reddit logo\">\n",
"</a>\n",
"\n",
"<a href=\"https://www.facebook.com/sharer/sharer.php?u=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/langgraph_gemini_podcast.ipynb\" target=\"_blank\">\n",
" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/51/Facebook_f_logo_%282019%29.svg\" alt=\"Facebook logo\">\n",
"</a> "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "84f0f73a0f76"
},
"source": [
"| Author |\n",
"| --- |\n",
"| [Kristopher Overholt](https://github.com/koverholt/) |"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tvgnzT1CKxrO"
},
"source": [
"## Overview\n",
"\n",
"Creating a podcast can be a very involved process, requiring extensive research, writing, editing, and production. **What if there was a way to leverage the power of AI to streamline the creation of a podcast, automating many of the tasks traditionally performed by humans?** [NotebookLM](https://notebooklm.google.com/), for example, lets users easily generate [audio overviews based on documents](https://blog.google/technology/ai/notebooklm-audio-overviews/).\n",
"\n",
"#### 🔈🔈 [Listen to a sample podcast generated by this notebook!](https://storage.googleapis.com/github-repo/generative-ai/gemini/orchestration/langgraph/gemini-podcast.mp3) 🔈🔈\n",
"\n",
"But what if you want to customize the length of the podcast, the voices, or the conversation flow and augment it with additional research tools? In this notebook, **you'll recreate this kind of podcast generation functionality by building an AI agent to do the heavy lifting and then customize the entire flow yourself!**\n",
"\n",
"**This notebook demonstrates how to build a [LangGraph](https://langchain-ai.github.io/langgraph/)-powered AI agent to research, write, and refine a podcast script using the [Gemini API in in Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models).** You'll use LangGraph and LangChain to orchestrate calls to Gemini along with calls to different search tools, allowing the AI to learn about a given topic before writing about it. Then, the AI will critique its work and iterate on the podcast script, improving it with each revision.\n",
"\n",
"Here's how you'll build and use our AI podcasting agent:\n",
"\n",
"- **[User]** Define the podcast topic: Provide a clear and concise topic for the podcast.\n",
"- **[Agent]** Generate an outline: Use Gemini to create a high-level outline, structuring the podcast's flow.\n",
"- **[Agent]** Conduct research: The AI agent will use search tools like arXiv, PubMed, and Wikipedia to gather relevant information.\n",
"- **[Agent]** Write a script: Gemini will generate an engaging podcast script, incorporating the research findings.\n",
"- **[Agent]** Critique and iterate: The agent will analyze its script, provide a critique, then generate a revised draft.\n",
"- **[Agent]** Generate audio: You'll use text-to-speech to generate audio for each line of the podcast script.\n",
"\n",
"<img src=\"https://storage.googleapis.com/github-repo/generative-ai/gemini/orchestration/langgraph/gemini-podcast-agent.jpg\" width=\"400px\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "61RBz8LLbxCR"
},
"source": [
"## Get started\n",
"\n",
"This section sets up the environment for the AI podcast agent. This includes:\n",
"\n",
"- **Installing Libraries:** Installing the required Python libraries\n",
"- **Restarting Runtime (Colab Only):** Restarting the Colab runtime\n",
"- **Authenticating Environment (Colab Only):** Authenticating to Google Cloud\n",
"- **Setting Project Information:** Setting up your Google Cloud project"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "68664edd8334"
},
"source": [
"### Install FFMpeg on your machine\n",
"\n",
"Install the FFMpeg libraries for running the AudioSegment library.\n",
"If you're using Vertex AI, open the **Terminal**. Run the following command:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "No17Cw5hgx12"
},
"outputs": [],
"source": [
"sudo apt-get update && apt-get install ffmpeg -y"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3iYn8_bo2Ozj"
},
"source": [
"### Install required packages\n",
"\n",
"This code cell installs the necessary Python libraries for running the AI podcast agent."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "tFy3H3aPgx12"
},
"outputs": [],
"source": [
"%pip install -q -U \\\n",
" arxiv \\\n",
" google-cloud-texttospeech \\\n",
" langgraph \\\n",
" langchain-google-genai \\\n",
" langchain-community \\\n",
" pydub \\\n",
" pymupdf \\\n",
" wikipedia \\\n",
" xmltodict"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dmWOrTJ3gx13"
},
"source": [
"### Authenticate your notebook environment (Colab only)\n",
"\n",
"If you're running this notebook on Google Colab, run the cell below to authenticate your environment."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "NyKGtVQjgx13"
},
"outputs": [],
"source": [
"import sys\n",
"\n",
"if \"google.colab\" in sys.modules:\n",
" from google.colab import auth\n",
"\n",
" auth.authenticate_user()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DF4l8DTdWgPY"
},
"source": [
"### Set Google Cloud project information\n",
"\n",
"To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
"\n",
"Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "Nqwi-5ufWp_B"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"PROJECT_ID = \"[your-project-id]\" # @param {type:\"string\", isTemplate: true}\n",
"if PROJECT_ID == \"[your-project-id]\":\n",
" PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n",
"\n",
"LOCATION = os.environ.get(\"GOOGLE_CLOUD_REGION\", \"global\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EdvJRUWRNGHE"
},
"source": [
"## Building the AI podcasting agent\n",
"\n",
"This section constructs the AI agent. Key steps include:\n",
"\n",
"- **Initializing Agent Memory and State:** Setting up the agent's memory and defining its data structure\n",
"- **Initializing the Gemini Model:** Loading the Gemini language model from Vertex AI\n",
"- **Defining Search Tools:** Creating tools to access information sources like arXiv, PubMed, and Wikipedia\n",
"- **Defining Workflow Stages:** Defining each stage of the workflow, including prompts and functions\n",
"- **Compiling the Workflow:** Structuring the workflow as a graph using LangGraph"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5303c05f7aa6"
},
"source": [
"### Import libraries\n",
"\n",
"This section imports the necessary libraries for LangGraph, LangChain, and other utilities needed for your agent's functionality.\n",
"\n",
"This includes tools for interacting with the Gemini API, defining custom tools, managing agent state, and displaying results."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "6fc324893334"
},
"outputs": [],
"source": [
"# Common libraries\n",
"import logging\n",
"import os\n",
"import re\n",
"\n",
"# Typing utilities for data validation and schema definitions\n",
"from typing import TypedDict\n",
"\n",
"from IPython.display import Audio, Image\n",
"\n",
"# Libraries for text-to-speech generation and audio processing\n",
"from google.cloud import texttospeech\n",
"\n",
"# Tools\n",
"from langchain_community.retrievers import (\n",
" ArxivRetriever,\n",
" PubMedRetriever,\n",
" WikipediaRetriever,\n",
")\n",
"from langchain_core.documents import Document\n",
"\n",
"# LangChain and LangGraph components for message handling and tool integration\n",
"from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
"from langchain_core.tools import tool\n",
"\n",
"# LangChain and Gemini integration\n",
"from langchain_google_genai import ChatGoogleGenerativeAI\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import END, StateGraph\n",
"from langgraph.prebuilt import ToolNode\n",
"from pydub import AudioSegment\n",
"\n",
"# Set logging level to ERROR to filter warnings\n",
"logger = logging.getLogger()\n",
"logger.setLevel(logging.ERROR)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e43229f3ad4f"
},
"source": [
"### Initialize agent memory and agent state\n",
"\n",
"Here, you initialize your [agent's memory](https://langchain-ai.github.io/langgraph/how-tos/memory/manage-conversation-history/) to store information during the workflow.\n",
"\n",
"You also define the schema for your [agent's state](https://langchain-ai.github.io/langgraph/how-tos/state-model/), which includes the podcast outline, search queries, and drafts."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "cf93d5f0ce00"
},
"outputs": [],
"source": [
"# Initialize agent memory\n",
"memory = MemorySaver()\n",
"\n",
"\n",
"# Define the agent's state\n",
"class AgentState(TypedDict):\n",
" revision_number: int\n",
" max_revisions: int\n",
" search_count: int\n",
" max_searches: int\n",
" task: str\n",
" outline: str\n",
" queries: list\n",
" content: list\n",
" draft: str\n",
" critique: str\n",
" tool_calls: list"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "27b61a7e7ef6"
},
"source": [
"### Initialize Gemini model\n",
"\n",
"Initialize the Gemini model from Vertex AI, specifying the model version and temperature settings.\n",
"\n",
"This sets up the core language model that will power your agent's actions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "06877aae6673"
},
"outputs": [],
"source": [
"model = ChatGoogleGenerativeAI(\n",
" model=\"gemini-3.5-flash\",\n",
" project=PROJECT_ID,\n",
" location=LOCATION,\n",
" vertexai=True,\n",
" temperature=0,\n",
" thinking_level=\"low\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d591fae74758"
},
"source": [
"### Define search tools\n",
"\n",
"This section defines custom tools that will be used by your AI podcast agent to gather information from various sources. These tools act as interfaces to external services and provide access to relevant data for the podcast topic.\n",
"\n",
"Each tool is implemented as a Python function decorated with the `@tool` decorator from LangChain. This decorator makes it easy to integrate these functions into LangGraph workflows.\n",
"\n",
"The following search tools are defined:\n",
"\n",
"- **`search_arxiv`:** Retrieves research papers from arXiv based on a keyword query.\n",
"- **`search_pubmed`:** Searches for information on PubMed, a database of biomedical literature.\n",
"- **`search_wikipedia`:** Fetches information from Wikipedia based on a keyword query.\n",
"\n",
"Your LangGraph application will use these tool nodes to call the corresponding search functions and obtain information from these external sources. This allows the AI agent to learn about the podcast topic before generating the script."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "0d27ed8a91c1"
},
"outputs": [],
"source": [
"@tool\n",
"def search_arxiv(query: str) -> list[Document]:\n",
" \"\"\"Search for relevant publications on arXiv.\"\"\"\n",
" retriever = ArxivRetriever(\n",
" load_max_docs=2,\n",
" get_full_documents=False,\n",
" )\n",
" docs = retriever.invoke(query)\n",
" if docs:\n",
" return docs\n",
" return [\"No results found on arXiv\"]\n",
"\n",
"\n",
"@tool\n",
"def search_pubmed(query: str) -> list[Document]:\n",
" \"\"\"Search for information on PubMed.\"\"\"\n",
" retriever = PubMedRetriever()\n",
" docs = retriever.invoke(query)\n",
" if docs:\n",
" return docs\n",
" return [\"No results found on PubMed\"]\n",
"\n",
"\n",
"@tool\n",
"def search_wikipedia(query: str) -> list[Document]:\n",
" \"\"\"Search for information on Wikipedia.\"\"\"\n",
" retriever = WikipediaRetriever()\n",
" docs = retriever.invoke(query)\n",
" if docs:\n",
" return docs\n",
" return [\"No results found on Wikipedia\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "95fbea6aaa20"
},
"source": [
"### Define workflow stages along with corresponding prompts and functions\n",
"\n",
"This section defines the different stages of the AI podcast agent's workflow and the corresponding prompt templates and node functions that drive each stage.\n",
"\n",
"Each stage represents a specific task in the podcast creation process, such as generating an outline, conducting research, writing the script, and providing a critique.\n",
"\n",
"For each stage, you'll define:\n",
"\n",
"- **Prompt Template:** A carefully crafted text prompt that instructs the Gemini language model on what to do at that stage. The prompt provides context, instructions, and any necessary input data.\n",
"- **Node Function:** A Python function that encapsulates the logic for executing that stage. The function typically involves:\n",
" - Constructing the prompt with relevant information from the agent's state.\n",
" - Invoking the Gemini API with the prompt.\n",
" - Processing the model's response and updating the agent's state.\n",
"\n",
"These prompt templates and node functions are the building blocks of the LangGraph workflow that orchestrates the entire podcast creation process.\n",
"\n",
"#### Podcast outline node\n",
"\n",
"This node generates a structured outline for the podcast based on the user-provided topic:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "4ce60bbc06e6"
},
"outputs": [],
"source": [
"OUTLINE_PROMPT = \"\"\"You are an expert writer tasked with writing a high level outline of an engaging 2-minute podcast.\n",
"Write such an outline for the user provided topic. Give an outline of the podcast along with any\n",
"relevant notes or instructions for the sections.\"\"\"\n",
"\n",
"\n",
"# Generate an outline for the podcast based on the user-provided topic\n",
"def podcast_outline_node(state: AgentState):\n",
" messages = [\n",
" SystemMessage(content=OUTLINE_PROMPT),\n",
" HumanMessage(content=state[\"task\"]),\n",
" ]\n",
" response = model.invoke(messages)\n",
" return {\"outline\": response.text}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5d7349c32d28"
},
"source": [
"#### Research plan node\n",
"\n",
"This node formulates a search query based on the podcast topic and previous queries:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "87df19f53b95"
},
"outputs": [],
"source": [
"RESEARCH_PLAN_PROMPT = \"\"\"You are a researcher tasked with providing information that can\n",
"be used when writing the following podcast. Generate one search query consisting of a few\n",
"keywords that will be used to gather any relevant information. Do not output any information\n",
"other than the query consisting of a few words.\n",
"\n",
"These were the past queries, do not repeat keywords from past queries in your newly generated query:\n",
"---\n",
"{queries}\"\"\"\n",
"\n",
"\n",
"# Generates a search query based on the outline\n",
"def research_plan_node(state: AgentState):\n",
" messages = [\n",
" SystemMessage(content=RESEARCH_PLAN_PROMPT.format(queries=state[\"queries\"])),\n",
" HumanMessage(content=state[\"task\"]),\n",
" ]\n",
" response = model.invoke(messages)\n",
" queries = state[\"queries\"]\n",
" if queries:\n",
" queries.append(response.text)\n",
" else:\n",
" queries = [response.text]\n",
" return {\"queries\": queries}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1719cf80233f"
},
"source": [
"#### Research task node\n",
"\n",
"This node executes a search using the selected tool and query, retrieving relevant information for the podcast:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "9bcbfe53b7d9"
},
"outputs": [],
"source": [
"RESEARCH_TASK_PROMPT = \"\"\"Use the available search tools and search queries to find information\n",
"relevant to the podcast. Try searching different sources to obtain different articles. Try using\n",
"different search tools than what was used previously so that you can obtain a broader range of\n",
"information.\n",
"\n",
"These are the previous tool calls, so you can choose a different tool:\n",
"---\n",
"{tool_calls}\n",
"---\n",
"These are the previous search results, so you can aim for different sources and content:\n",
"---\n",
"{content}\"\"\"\n",
"\n",
"\n",
"# Performs searches using tools\n",
"def research_agent_node(state: AgentState):\n",
" tool_calls = state[\"tool_calls\"]\n",
" content = state[\"content\"]\n",
" queries = state[\"queries\"]\n",
" query = queries[-1]\n",
" messages = [\n",
" SystemMessage(\n",
" content=RESEARCH_TASK_PROMPT.format(tool_calls=tool_calls, content=content)\n",
" ),\n",
" HumanMessage(content=query),\n",
" ]\n",
"\n",
" # Perform function calls\n",
" tools = [search_arxiv, search_pubmed, search_wikipedia]\n",
" model_with_tools = model.bind_tools(tools)\n",
" response_tool_calls = model_with_tools.invoke(messages)\n",
" if tool_calls:\n",
" tool_calls.append(response_tool_calls)\n",
" else:\n",
" tool_calls = [response_tool_calls]\n",
"\n",
" # Defines a tool node based on search functions\n",
" tool_node = ToolNode(tools)\n",
" response = tool_node.invoke({\"messages\": [response_tool_calls]})\n",
"\n",
" # Add the search results to the content list in the agent state\n",
" for message in response.get(\"messages\", []):\n",
" if isinstance(message, ToolMessage):\n",
" content.insert(0, message.content)\n",
"\n",
" return {\n",
" \"content\": content,\n",
" \"tool_calls\": tool_calls,\n",
" \"search_count\": state[\"search_count\"] + 1,\n",
" }\n",
"\n",
"\n",
"# Determine whether to continue research based on the number of searches performed\n",
"def should_continue_tools(state: AgentState) -> str:\n",
" if state[\"search_count\"] > state[\"max_searches\"]:\n",
" return \"generate_script\"\n",
" return \"research_plan\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9eaa6d0bff8d"
},
"source": [
"#### Podcast writing node\n",
"\n",
"This node generates a draft podcast script using the outline and research results, aiming for an engaging and informative style:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "1742523735e8"
},
"outputs": [],
"source": [
"WRITER_PROMPT = \"\"\"\n",
"You are a writing assistant tasked with writing engaging 2-minute podcast scripts.\n",
"\n",
"- Generate the best podcast script possible for the user's request and the initial outline.\n",
"- The script MUST strictly alternate lines between the two hosts, separating each host's line with a newline.\n",
"- Add an intro phrase and outro phrase to start and end the podcast, and use a fun, random name for the podcast show.\n",
"- Given a critique, respond with a revised version of your previous script.\n",
"- Include lively back-and-forth chatter, reflections, and expressions of amazement between the hosts.\n",
"- Cite at least THREE pieces of research throughout the script, choosing the most relevant research for each point.\n",
"- DO NOT include ANY of the following:\n",
" - Speaker labels (e.g., \"Host 1:\", \"Host 2:\")\n",
" - Sound effect descriptions (e.g., \"[Sound of waves]\")\n",
" - Formatting instructions (e.g., \"(Emphasis)\", \"[Music fades in]\")\n",
" - Any other non-dialogue text.\n",
"- Use this format for citations, including the month and year if available:\n",
" \"In [Month, Year], [Organization] found that...\"\n",
" \"Research from [Organization] in [Month, Year] showed that...\"\n",
" \"Back in [Month, Year], a study by [Organization] suggested that...\"\n",
"---\n",
"Utilize all of the following search results and context as needed:\n",
"{content}\n",
"---\n",
"If this is a revision, the critique will be provided below:\n",
"{critique}\"\"\"\n",
"\n",
"\n",
"# Generates a draft of the script based on the content and outline\n",
"def generate_script_node(state: AgentState):\n",
" messages = [\n",
" SystemMessage(\n",
" content=WRITER_PROMPT.format(\n",
" content=state[\"content\"], critique=state.get(\"critique\", \"\")\n",
" )\n",
" ),\n",
" HumanMessage(\n",
" content=f\"{state['task']}\\n\\nHere is my outline:\\n\\n{state['outline']}\"\n",
" ),\n",
" ]\n",
" response = model.invoke(messages)\n",
" return {\n",
" \"draft\": response.text,\n",
" \"search_count\": 0, # Reset the search count for the next revision\n",
" \"revision_number\": state.get(\"revision_number\", 1) + 1,\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d00163e72e16"
},
"source": [
"#### Podcast critique node\n",
"\n",
"This node provides feedback and suggestions for improvement on the generated podcast script:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "de70a68caa8d"
},
"outputs": [],
"source": [
"CRITIQUE_PROMPT = \"\"\"You are a producer grading a podcast script.\n",
"Generate critique and recommendations for the user's submission.\n",
"Provide detailed recommendations, including requests for conciseness, depth, style, etc.\"\"\"\n",
"\n",
"\n",
"# Generates a critique with feedback on the draft podcast script\n",
"def perform_critique_node(state: AgentState):\n",
" messages = [\n",
" SystemMessage(content=CRITIQUE_PROMPT),\n",
" HumanMessage(content=state[\"draft\"]),\n",
" ]\n",
" response = model.invoke(messages)\n",
" return {\"critique\": response.text}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "46aa739f9c1e"
},
"source": [
"#### Research critique node\n",
"\n",
"This node generates a new search query based on the critique of the script, aiming to address weaknesses and find additional information:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "48682bcbb177"
},
"outputs": [],
"source": [
"RESEARCH_CRITIQUE_PROMPT = \"\"\"You are a writing assistant tasked with providing information that can\n",
"be used when making any requested revisions (as outlined below).\n",
"Generate one search query consisting of a few keywords that will be used to gather any relevant\n",
"information. Do not output any information other than the query consisting of a few words.\n",
"\n",
"---\n",
"\n",
"These were the past queries, so you can vary the query that you generate:\n",
"\n",
"{queries}\n",
"\"\"\"\n",
"\n",
"\n",
"# Generates a new search query based on the critique\n",
"def research_critique_node(state: AgentState):\n",
" messages = [\n",
" SystemMessage(\n",
" content=RESEARCH_CRITIQUE_PROMPT.format(queries=state[\"queries\"])\n",
" ),\n",
" HumanMessage(content=state[\"critique\"]),\n",
" ]\n",
" response = model.invoke(messages)\n",
" queries = state.get(\"queries\", [])\n",
" if queries:\n",
" queries.append(response.text)\n",
" else:\n",
" queries = [response.text]\n",
" return {\"queries\": queries}\n",
"\n",
"\n",
"# Decide whether to continue to the next revision or end the process\n",
"def should_continue(state: AgentState):\n",
" if state[\"revision_number\"] > state[\"max_revisions\"]:\n",
" return END\n",
" return \"perform_critique\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aae38c0085e3"
},
"source": [
"## Define and compile the LangGraph workflow\n",
"\n",
"This section defines the structure and flow of the AI podcast agent using LangGraph.\n",
"\n",
"The workflow is constructed as a graph with nodes representing each stage in the process (e.g., outlining, research, script generation) and edges defining the transitions between these stages.\n",
"\n",
"The workflow includes two main loops:\n",
"\n",
"- **Research Loop:** This loop iteratively plans and executes searches until a specified number of searches are completed.\n",
"- **Critique and Revision Loop:** This loop handles the script critique, additional research based on the critique, and script revision, repeating for a set number of revisions.\n",
"\n",
"The `workflow.compile()` function transforms this graph definition into an executable workflow, incorporating memory management to maintain the agent's state throughout the process."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "f7d04cda5f36"
},
"outputs": [],
"source": [
"# Initialize the state graph\n",
"workflow = StateGraph(AgentState)\n",
"\n",
"# Define the nodes of the workflow, representing each stage of the process\n",
"workflow.add_node(\"podcast_outline\", podcast_outline_node)\n",
"workflow.add_node(\"research_plan\", research_plan_node)\n",
"workflow.add_node(\"research_agent\", research_agent_node)\n",
"workflow.add_node(\"generate_script\", generate_script_node)\n",
"workflow.add_node(\"perform_critique\", perform_critique_node)\n",
"workflow.add_node(\"research_critique\", research_critique_node)\n",
"\n",
"# Specify the starting node of the workflow\n",
"workflow.set_entry_point(\"podcast_outline\")\n",
"\n",
"# Define the flow between node and stages\n",
"workflow.add_edge(\"podcast_outline\", \"research_plan\")\n",
"workflow.add_edge(\"research_plan\", \"research_agent\")\n",
"workflow.add_edge(\"perform_critique\", \"research_critique\")\n",
"workflow.add_edge(\"research_critique\", \"research_agent\")\n",
"\n",
"# Define conditional edges for the research loop\n",
"workflow.add_conditional_edges(\n",
" \"research_agent\",\n",
" should_continue_tools,\n",
" {\"generate_script\": \"generate_script\", \"research_plan\": \"research_plan\"},\n",
")\n",
"\n",
"# Define conditional edges for the critique and revision loop\n",
"workflow.add_conditional_edges(\n",
" \"generate_script\",\n",
" should_continue,\n",
" {END: END, \"perform_critique\": \"perform_critique\"},\n",
")\n",
"\n",
"# Compile the workflow graph, enabling memory to track agent state\n",
"graph = workflow.compile(checkpointer=memory)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "18154fa6d8b4"
},
"source": [
"### Visualize the workflow\n",
"\n",
"This cell visualizes the compiled LangGraph workflow as a [Mermaid diagram](https://mermaid.js.org/).\n",
"\n",
"The diagram provides a clear and intuitive representation of the workflow's structure, showing the nodes, edges, and the flow of execution.\n",
"\n",
"This visualization helps to understand the overall process and the interactions between different stages of the AI podcast agent."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "f97fe13cc0cf"
},
"outputs": [],
"source": [
"# Display a Mermaid diagram of the workflow\n",
"Image(graph.get_graph().draw_mermaid_png())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3076e0cd0c4e"
},
"source": [
"### Define the podcast topic\n",
"\n",
"This cell defines the topic of the podcast that the AI agent will create.\n",
"\n",
"The topic is assigned to the variable `PODCAST_TOPIC`. Feel free to modify this variable to explore different podcast topics!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "6627153c6715"
},
"outputs": [],
"source": [
"PODCAST_TOPIC = \"Explore the use of bio-inspired fluid dynamics in the design of underwater robots and vehicles\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a1df657ce053"
},
"source": [
"### Run the AI podcast agent\n",
"\n",
"This cell executes the compiled LangGraph workflow, running the AI podcast agent to generate the podcast script.\n",
"\n",
"The code performs these actions:\n",
"\n",
"- **Clean agent helper function:** This function prepares the agent's output for printing by removing unnecessary characters and formatting\n",
"- **Thread Configuration:** A thread configuration is defined to ensure a unique history for this workflow execution\n",
"- **Workflow Execution:** The `graph.stream()` method runs the workflow, iterating through each stage and updating the agent's state\n",
"- **Output Display:** The code prints the results of each stage, including the agent's actions and generated output"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "338377bc8c25"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Agent Node: podcast_outline\n",
"\n",
"Agent Result:\n",
"{'outline': '**Podcast Title:** Nature s Silent Engines: The Bio-Inspired Deep**Duration:** Approx. 2 Minutes**Tone:** Curious, futuristic, and fast-paced.---### **I. The Hook: The Ghost in the Water (0:00 0:30)*** **Content:** Start with the sound of a traditional, churning boat propeller then abruptly cut to silence and the sound of a single, powerful \"whoosh\" of water.* **Narrative:** Introduce the \"propeller problem.\" For a century, we ve explored the ocean with loud, clunky, and inefficient metal blades. But nature solved underwater travel millions of years ago.* **Key Concept:** Introduce **Biomimicry** the art of stealing nature s best secrets to build the next generation of Autonomous Underwater Vehicles (AUVs).* **Instruction for Host:** Use a \"mystery-thriller\" tone. Speak quickly but clearly to build excitement.### **II. The Science: Beyond the Propeller (0:30 1:10)*** **Content:** Explain the fluid dynamics of two specific biological marvels: 1. **The Shar\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics']}\n",
"\n",
"====================\n",
"\n",
"Too Many Requests, waiting for 0.20 seconds...\n",
"Too Many Requests, waiting for 0.40 seconds...\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'uid': '41775071', 'Title': 'Design and control of a bioinspired underwater robot with hydrogel-based flexible pectoral fins.', 'Published': '2026-03-13', 'Copyright Information': ' 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.'}, page_content='Bioinspired flexible propulsion offers a promising approach for improving the efficiency and maneuverability of underwater robots. Inspired by the undulatory locomotion of median and/or paired fin organisms, this article presents a flapping propulsion system based on flexible pectoral fins fabricated using hydrogel materials. Coordinated actuation of multiple fin rays generates continuous traveling-wave deformation of the pectoral fins. To address the difficulty of establishing accurate hydrodynamic models for flexible flapping propulsion, a path following control framework combining offline data-driven modeling and online adaptive \n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion']}\n",
"\n",
"====================\n",
"\n",
"Too Many Requests, waiting for 0.20 seconds...\n",
"Too Many Requests, waiting for 0.40 seconds...\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'title': 'Unmanned underwater vehicle', 'summary': 'Unmanned underwater vehicles (UUV), also known as underwater drones, or unmanned submarines, are submersible vehicles that can operate underwater without a human occupant, either remotely operated underwater vehicles (ROUVs) or autonomous underwater vehicles (AUVs).\n",
"\n",
"', 'source': 'https://en.wikipedia.org/wiki/Unmanned_underwater_vehicle'}, page_content=\"Unmanned underwater vehicles (UUV), also known as underwater drones, or unmanned submarines, are submersible vehicles that can operate underwater without a human occupant, either remotely operated underwater vehicles (ROUVs) or autonomous underwater vehicles (AUVs).\n",
"\n",
"\n",
"== Classifications ==\n",
"\n",
"\n",
"=== Remotely operated underwater vehicle ===\n",
"Remotely operated underwater vehicles (ROUVs) primarily replace humans in difficult underwater conditions and perform educational or industrial missions. They are manually controlled to perform tasks that include survei\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency']}\n",
"\n",
"====================\n",
"\n",
"Too Many Requests, waiting for 0.20 seconds...\n",
"Too Many Requests, waiting for 0.40 seconds...\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'title': 'Fish locomotion', 'summary': 'Fish locomotion is the various types of animal locomotion used by fish, principally by swimming. This is achieved in different groups of fish by a variety of mechanisms of propulsion, most often by wave-like lateral flexions of the fish\\\\'s body and tail in the water, and in various specialised fish by motions of the fins. The major forms of locomotion in fish are:\n",
"\n",
"Anguilliform, in which a wave passes evenly along a long slender body;\n",
"Sub-carangiform, in which the wave increases quickly in amplitude towards the tail;\n",
"Carangiform, in which the wave is concentrated near the tail, which oscillates rapidly;\n",
"Thunniform, rapid swimming with a large powerful crescent-shaped tail; and\n",
"Ostraciiform, with almost no oscillation except of the tail fin.\n",
"More specialized fish include movement by pectoral fins with a mainly stiff body, opposed sculling with dorsal and anal fins, as in the sunfish; and movement by propagating a\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency', 'Shark skin riblets drag reduction']}\n",
"\n",
"====================\n",
"\n",
"Too Many Requests, waiting for 0.20 seconds...\n",
"Too Many Requests, waiting for 0.40 seconds...\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'uid': '40897208', 'Title': 'Multi-objective optimization of three-dimensional riblet surfaces for hydrodynamic and acoustic performance.', 'Published': '2025-10-06', 'Copyright Information': 'Creative Commons Attribution license.'}, page_content='Riblets inspired by the dermal denticles of shark skin are widely recognized for their drag-reducing performance. Although previous research has predominantly focused on two-dimensional riblet geometries, three-dimensional (3D) topographies remain underexplored due to the complex architecture of denticle-inspired surfaces. Natural riblet arrays, comprising thousands of interconnected dermal denticles, pose challenges in terms of parameterization, simulation, and fabrication. This work addresses these challenges by introducing a 3D, riblet-reinforced surface topography design that reduces drag, suppresses flow-induced noise, and simplifies both parameterization and prototyping, ultimately providing a scalable \n",
"\n",
"====================\n",
"\n",
"Agent Node: generate_script\n",
"\n",
"Agent Result:\n",
"{'draft': 'Welcome to Deep Sea Designs, the show where we dive into the engineering secrets of the blue.Imagine the loud, churning thud of a massive steel propeller, and now, replace it with the near-silent, powerful whoosh of a shark s tail.It s the ultimate \"propeller problem\" for a century, our underwater drones have been clunky and loud, but nature solved high-speed travel millions of years ago.That s why we re obsessed with biomimicry, literally stealing evolution s best blueprints to build autonomous underwater vehicles that move like ghosts.Take shark skin, for example; it s covered in microscopic ridges called riblets that do way more than just look cool.Exactly, and in October 2025, research published in the journal Multi-objective Optimization of Three-dimensional Riblet Surfaces found that these 3D topographies can reduce flow-induced noise by up to 8.81 decibels.That is a massive jump in stealth, and it s all about controlling those tiny whirlpools of water, or vortex shedd\n",
"\n",
"====================\n",
"\n",
"Agent Node: perform_critique\n",
"\n",
"Agent Result:\n",
"{'critique': '**Producer's Evaluation: Deep Sea Designs****Overall Grade: B+****Status:** Approved for Pre-Production (with revisions)### **Executive Summary**This is a high-energy, intellectually stimulating script that does a fantastic job of bridging the gap between complex engineering and evocative storytelling. The \"propeller vs. shark tail\" hook is excellent. However, the script currently reads more like a narrated essay than a dynamic podcast. It suffers from \"Academic Mouthful Syndrome\" where technical citations disrupt the flow of natural conversation.---### **Detailed Critique**#### **1. Voice & Chemistry*** **The \"Two-Headed Monologue\" Problem:** Currently, the script doesn't label speakers (e.g., Host A and Host B). More importantly, both \"voices\" sound identical. They both speak in perfectly formed, data-heavy sentences. * **Recommendation:** Assign distinct roles. **Host A** should be the \"Storyteller/Visionary\" (focusing on the \"why\" and the big picture), and **Host \n",
"\n",
"====================\n",
"\n",
"Agent Node: research_critique\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency', 'Shark skin riblets drag reduction', 'bio-inspired underwater stealth technology applications']}\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'title': 'BioShock', 'summary': \"BioShock is a 2007 first-person shooter video game developed by 2K Boston (later Irrational Games) and 2K Australia, and published by 2K. The first game in the BioShock series, it was released for Microsoft Windows and Xbox 360 platforms in August 2007; a PlayStation 3 port by Irrational, 2K Marin, 2K Australia and Digital Extremes was released in October 2008. The game follows player character Jack, who discovers the underwater city of Rapture, built by business magnate Andrew Ryan to be an isolated utopia. The discovery of ADAM, a genetic material which grants superhuman powers, initiated the city's turbulent decline. Jack attempts to escape Rapture, fighting its mutated and mechanical denizens, while engaging with the few sane survivors left and learning of the city's past. The player can defeat foes in several ways by using weapons, utilizing plasmids that give unique powers, and by turning Rapture's defenses agains\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency', 'Shark skin riblets drag reduction', 'bio-inspired underwater stealth technology applications', 'Humpback whale tubercle boundary layer']}\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[\"No results found on PubMed\"]', '[Document(metadata={'Entry ID': 'http://arxiv.org/abs/1202.5066v2', 'Published': datetime.date(2012, 10, 1), 'Title': 'The Critical Richardson Number and Limits of Applicability of Local Similarity Theory in the Stable Boundary Layer', 'Authors': 'Andrey A. Grachev, Edgar L Andreas, Christopher W. Fairall, Peter S. Guest, P. Ola G. Persson'}, page_content=\"Measurements of atmospheric turbulence made over the Arctic pack ice during the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) are used to determine the limits of applicability of Monin-Obukhov similarity theory (in the local scaling formulation) in the stable atmospheric boundary layer. Based on the spectral analysis of wind velocity and air temperature fluctuations, it is shown that, when both of the gradient Richardson number, Ri, and the flux Richardson number, Rf, exceed a 'critical value' of about 0.20 - 0.25, the inertial subrange associated with the Richardson-Kolmog\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency', 'Shark skin riblets drag reduction', 'bio-inspired underwater stealth technology applications', 'Humpback whale tubercle boundary layer', 'Manta ray pectoral fin undulation']}\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'Entry ID': 'http://arxiv.org/abs/1002.1184v1', 'Published': datetime.date(2010, 2, 5), 'Title': 'Implementation of an Innovative Bio Inspired GA and PSO Algorithm for Controller design considering Steam GT Dynamics', 'Authors': 'R. Shivakumar, R. Lakshmipathi'}, page_content='The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of thi\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_plan\n",
"\n",
"Agent Result:\n",
"{'queries': ['biomimetic underwater vehicle hydrodynamics', 'Nature-inspired aquatic robot propulsion', 'Fish swimming vortex shedding efficiency', 'Shark skin riblets drag reduction', 'bio-inspired underwater stealth technology applications', 'Humpback whale tubercle boundary layer', 'Manta ray pectoral fin undulation', 'Cephalopod pulsed jet locomotion maneuverability']}\n",
"\n",
"====================\n",
"\n",
"Agent Node: research_agent\n",
"\n",
"Agent Result:\n",
"{'content': ['[Document(metadata={'title': 'Cephalopod', 'summary': 'A cephalopod is any member of the molluscan class Cephalopoda (Greek plural , kephal podes; \"head-feet\") such as a squid, octopus, cuttlefish, or nautilus. These exclusively marine animals are characterized by bilateral body symmetry, a prominent head, and a set of arms or tentacles (muscular hydrostats) modified from the primitive molluscan foot. Fishers sometimes call cephalopods \"inkfish\", referring to their common ability to squirt ink. The study of cephalopods is a branch of malacology known as teuthology.\n",
"Cephalopods became dominant during the Ordovician period, represented by primitive nautiloids. The class now contains two, only distantly related, extant subclasses: Coleoidea, which includes octopuses, squid, and cuttlefish; and Nautiloidea, represented by Nautilus and Allonautilus. In the Coleoidea, the molluscan shell has been internalized or is absent, whereas in the Nautiloidea, the external shell remai\n",
"\n",
"====================\n",
"\n",
"Agent Node: generate_script\n",
"\n",
"Agent Result:\n",
"{'draft': 'Welcome to Deep Sea Designs, the show where we peel back the waves to see how nature is re-engineering our world.Imagine the thundering, mechanical churn of a massive ship propeller suddenly cutting to total, ghostly silence.That s the goal of biomimicry, where we re ditching clunky metal blades to steal the ocean s oldest speed secrets for our robots.It s a total game-changer because, for a century, our underwater tech has been loud, inefficient, and honestly, a bit of a bully to the ecosystem.But nature solved fluid dynamics millions of years ago, and researchers are finally catching up to the \"ghosts\" in the water.Take the Great White Shark; it s not just muscle, its skin is covered in microscopic grooves called riblets that kill drag.In October 2025, a study on multi-objective optimization found that 3D denticle designs can drop flow noise by nearly seven decibels.That is massive it s the difference between a robot shouting its presence and moving like a whisper through \n",
"\n",
"====================\n",
"\n"
]
}
],
"source": [
"# Function to clean and format agent output for display\n",
"\n",
"\n",
"def clean_agent_result(data):\n",
" agent_result = str(data)\n",
" agent_result = re.sub(\n",
" r\"[^\\x00-\\x7F]+\", \" \", agent_result\n",
" ) # Remove non-ASCII characters\n",
" agent_result = re.sub(r\"\\\\\\\\n\", \"\\n\", agent_result) # Replace escaped newlines\n",
" agent_result = re.sub(r\"\\\\n\", \"\", agent_result) # Replace newlines\n",
" return re.sub(r\"\\\\'\", \"'\", agent_result) # Replace escaped single quotes\n",
"\n",
"\n",
"# Thread ID for unique history in workflow execution\n",
"thread = {\"configurable\": {\"thread_id\": \"1\"}}\n",
"\n",
"# Run the LangGraph workflow, passing the initial state and thread configuration\n",
"for state in graph.stream(\n",
" {\n",
" \"task\": PODCAST_TOPIC,\n",
" \"revision_number\": 1, # Current revision number\n",
" \"max_revisions\": 2, # Maximum number of revisions allowed\n",
" \"search_count\": 0, # Current search number\n",
" \"max_searches\": 3, # Maximum number of searches allowed per revision\n",
" \"content\": [],\n",
" \"queries\": [],\n",
" \"tool_calls\": [],\n",
" },\n",
" thread,\n",
"):\n",
" # Print a snippet of the results of each workflow stage\n",
" for k, v in state.items():\n",
" print(f\"Agent Node: {k}\\n\")\n",
" print(\"Agent Result:\")\n",
" print(clean_agent_result(v)[:1000])\n",
" print(\"\\n====================\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cd8d951762fb"
},
"source": [
"### Parse and display the final podcast script\n",
"\n",
"This section extracts and prepares the final podcast script generated by the AI agent.\n",
"\n",
"It displays the script for review, where each string in the list will be narrated by a different text-to-speech voice."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"id": "599397cab03c"
},
"outputs": [
{
"data": {
"text/plain": [
"['Welcome to Deep Sea Designs, the show where we peel back the waves to see how nature is re-engineering our world.',\n",
" 'Imagine the thundering, mechanical churn of a massive ship propeller suddenly cutting to total, ghostly silence.',\n",
" 'That’s the goal of biomimicry, where we’re ditching clunky metal blades to steal the ocean’s oldest speed secrets for our robots.',\n",
" 'It’s a total game-changer because, for a century, our underwater tech has been loud, inefficient, and honestly, a bit of a bully to the ecosystem.',\n",
" 'But nature solved fluid dynamics millions of years ago, and researchers are finally catching up to the \"ghosts\" in the water.',\n",
" 'Take the Great White Shark; it’s not just muscle, its skin is covered in microscopic grooves called riblets that kill drag.',\n",
" 'In October 2025, a study on multi-objective optimization found that 3D denticle designs can drop flow noise by nearly seven decibels.',\n",
" 'That is massive—it’s the difference between a robot shouting its presence and moving like a whisper through the dark.',\n",
" 'And it’s not just about stealth; it’s about that mind-blowing agility we see in creatures the size of a school bus.',\n",
" 'Right, like the Humpback whale, which uses those weird bumps on its fins—tubercles—to pull off acrobatic turns that should be physically impossible.',\n",
" 'Research from the University of Washington in August 2025 showed that these bio-inspired shapes allow vehicles to maintain lift at angles that would make a normal sub stall out.',\n",
" 'It’s like giving a submarine the steering rack of a fighter jet, all by mimicking a fin.',\n",
" 'We’re already seeing this in the \"Robo-Fish\" revolution, with soft-bodied bots like SoFi that swim exactly like a real tuna.',\n",
" 'Back in January 2025, a report on proprioceptive fin webbing suggested that these flexible sensors allow robots to \"feel\" the water just like a living fish does.',\n",
" 'That means they can inspect a delicate coral reef or a high-pressure oil pipe without crashing or scaring away the local sealife.',\n",
" \"We’re moving toward a future where our technology doesn't just move through the ocean; it becomes a seamless part of it.\",\n",
" 'The next time you see a mysterious shadow dart beneath the waves, take a second look.',\n",
" 'Was it a fish, or was it the future of engineering?',\n",
" 'Thanks for diving deep with us on Deep Sea Designs.']"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"podcast_script = state[\"generate_script\"][\"draft\"]\n",
"parsed_script = [\n",
" text for text in (line.strip() for line in podcast_script.splitlines()) if text\n",
"]\n",
"parsed_script"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e075edfe820d"
},
"source": [
"### Generate audio for the podcast\n",
"\n",
"This cell generates audio for each line of the parsed podcast script using Google Cloud's Text-to-Speech API.\n",
"\n",
"It creates separate audio files for each line, alternating between two different voices to simulate a conversation between two podcast hosts.\n",
"\n",
"The code:\n",
"\n",
"1. **Initializes the Text-to-Speech Client:** Sets up the connection to the API.\n",
"2. **Defines Audio Configuration:** Specifies the desired output audio format (MP3).\n",
"3. **Iterates through Script Lines:** Generates audio for each line, alternating voices.\n",
"4. **Saves Audio Files:** Writes the generated audio to separate MP3 files."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "c74e3badfd35"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Audio content written to file part-0.mp3\n",
"Audio content written to file part-1.mp3\n",
"Audio content written to file part-2.mp3\n",
"Audio content written to file part-3.mp3\n",
"Audio content written to file part-4.mp3\n",
"Audio content written to file part-5.mp3\n",
"Audio content written to file part-6.mp3\n",
"Audio content written to file part-7.mp3\n",
"Audio content written to file part-8.mp3\n",
"Audio content written to file part-9.mp3\n",
"Audio content written to file part-10.mp3\n",
"Audio content written to file part-11.mp3\n",
"Audio content written to file part-12.mp3\n",
"Audio content written to file part-13.mp3\n",
"Audio content written to file part-14.mp3\n",
"Audio content written to file part-15.mp3\n",
"Audio content written to file part-16.mp3\n",
"Audio content written to file part-17.mp3\n",
"Audio content written to file part-18.mp3\n"
]
}
],
"source": [
"# Instantiates a client\n",
"from google.api_core import client_options\n",
"\n",
"client = texttospeech.TextToSpeechClient(\n",
" client_options=client_options.ClientOptions(quota_project_id=PROJECT_ID)\n",
")\n",
"\n",
"# Select the type of audio file you want returned\n",
"audio_config = texttospeech.AudioConfig(audio_encoding=texttospeech.AudioEncoding.MP3)\n",
"\n",
"audio_files = []\n",
"for count, line in enumerate(parsed_script):\n",
" # Set the text input to be synthesized\n",
" synthesis_input = texttospeech.SynthesisInput(text=line)\n",
"\n",
" # Choose the voice for the current line, alternating between hosts\n",
" if count % 2 == 0:\n",
" voice_name = \"en-US-Chirp3-HD-Aoede\"\n",
" elif count % 2 == 1:\n",
" voice_name = \"en-US-Chirp3-HD-Puck\"\n",
"\n",
" # Configure voice parameters: language and voice name\n",
" voice = texttospeech.VoiceSelectionParams(\n",
" language_code=\"en-US\",\n",
" name=voice_name,\n",
" )\n",
"\n",
" # Generate audio using the Text-to-Speech API\n",
" response = client.synthesize_speech(\n",
" input=synthesis_input, voice=voice, audio_config=audio_config\n",
" )\n",
"\n",
" # Save the generated audio to an MP3 file\n",
" filename = f\"part-{count!s}.mp3\"\n",
" audio_files.append(filename)\n",
" with open(filename, \"wb\") as out:\n",
" out.write(response.audio_content)\n",
" print(f\"Audio content written to file {filename}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ea674bbf2628"
},
"source": [
"### Combine audio files and generate final podcast\n",
"\n",
"This cell combines the individual audio files generated in the previous step into a single podcast file.\n",
"\n",
"It also adds brief silences between each line for better listening experience.\n",
"\n",
"The final podcast is saved as `gemini-podcast.mp3`."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "9a4e93adc415"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Podcast content written to file gemini-podcast.mp3\n"
]
}
],
"source": [
"# Initialize audio segment\n",
"full_audio = AudioSegment.silent(duration=200)\n",
"\n",
"# Concatenate audio segments with silence in between\n",
"for file in audio_files:\n",
" sound = AudioSegment.from_mp3(file)\n",
" silence = AudioSegment.silent(duration=200)\n",
" full_audio += sound + silence\n",
" os.remove(file) # Remove the individual part files after combining\n",
"\n",
"# Save the final audio output to a file\n",
"podcast_filename = \"gemini-podcast.mp3\"\n",
"full_audio.export(podcast_filename)\n",
"print(f\"Podcast content written to file {podcast_filename}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2be9b47ae8a3"
},
"source": [
"### Listen to your AI-generated podcast!\n",
"\n",
"This cell plays the final podcast generated by the AI agent.\n",
"\n",
"The `Audio` object from `IPython.display` is used to embed the audio player directly into the notebook. The podcast will start playing automatically.\n",
"\n",
"Enjoy your AI-created podcast!"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "2f50dcfe1651"
},
"outputs": [],
"source": [
"Audio(filename=podcast_filename, rate=32000, autoplay=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "df34f0351197"
},
"source": [
"## Conclusion: Building AI-powered podcast agents\n",
"\n",
"This notebook showcases the exciting potential of using AI to automate the podcast creation process. By combining the power of the Gemini API with the flexibility of LangGraph, you built an intelligent agent capable of:\n",
"\n",
"- **Generating Podcast Outlines:** Structuring the flow and content of the podcast.\n",
"- **Conducting Research:** Gathering information from various sources like arXiv, PubMed, and Wikipedia.\n",
"- **Writing Engaging Scripts:** Crafting podcast scripts with natural-sounding dialogue, citations, and a conversational style.\n",
"- **Critiquing and Revising:** Providing feedback on the script and iteratively refining it.\n",
"- **Generating Audio:** Using text-to-speech technology to create the final podcast audio.\n",
"\n",
"This is just a starting point! You can customize this workflow further by:\n",
"\n",
"- **Adding New Research Tools:** Integrate additional sources of information relevant to your podcast topics.\n",
"- **Experimenting with Prompts:** Refine the prompts to guide the AI agent towards your desired style and content.\n",
"- **Exploring Different Voices:** Use a wider range of voices for the podcast hosts to create unique and engaging listening experiences.\n",
"\n",
"The possibilities are endless! As AI technology continues to advance, you can expect even more creative and innovative applications in podcasting and other content creation domains.\n",
"\n",
"You can learn more about [LangGraph](https://langchain-ai.github.io/langgraph/), the [Gemini API in Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models), or the [chat model provider for Google Gen AI in LangChain](https://docs.langchain.com/oss/python/integrations/chat/google_generative_ai) in their respective documentation pages."
]
}
],
"metadata": {
"colab": {
"name": "langgraph_gemini_podcast.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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