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4
src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json
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@ -1236,8 +1236,8 @@
"description": "# 📖 README\nThis flow demonstrates chaining three prompts and three language models.\nEach prompt is specifically designed to process previous output, with each LLM call building upon previous results\n\n\n## Prerequisites\n\n* [OpenAI API Key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. To run the flow, open the **Playground**. An example input is provided, with other suggestions listed below.\n\n \"The increasing need for secure and user-friendly decentralized finance (DeFi) platforms that make cryptocurrency investments accessible to non-tech-savvy users.\"\n\n \"The rising popularity of immersive, augmented reality (AR) experiences for remote collaboration and virtual team-building in distributed workforces.\"\n\n \"The expanding market for smart, IoT-enabled urban farming solutions that allow city dwellers to grow their own food efficiently in small spaces.\"\n\n \"The emerging demand for AI-powered personal styling and shopping assistants that consider sustainability, body positivity, and individual style preferences.\"\n\n",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.basic_prompt_chaining.b5fcc15e"
"i18n_key": "template_notes.basic_prompt_chaining.b5fcc15e",
"template": {}
},
"type": "note"
},

8
src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json
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@ -522,10 +522,10 @@
"description": "# 📖 README\nThis template demonstrates a standard chat flow with additional instructions provided by a prompt. Prompts provide instructions and inputs for a Large Language Model (LLM) beyond the standard user-provided chat input. In this example, the prompt describes the LLM's role and persona.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground** to start the chat and run the flow.\n\n## Next steps\nChange the prompt template, model, or model settings, such as **Temperature**, and then see how the responses change with these different inputs.\n💡 Some component settings are hidden by default; to view all settings click **Controls** in each component's header menu.\n💡 You can use curly braces to create variables in your template, such as `{variable}`. These can be populated from other components, with Langflow global variables, or at runtime.",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.basic_prompting.bd8ff52b",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.basic_prompting.bd8ff52b"
}
}
},
"dragging": false,
@ -559,10 +559,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.basic_prompting.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.basic_prompting.cd87cff6"
}
},
"type": "note"
},

4
src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json
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@ -778,8 +778,8 @@
"description": "# 📖 README\nCreate a blog post by using content fetched from URLs and user-provided instructions.\n\n## Prerequisites\n\n* An [OpenAI API key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. In the **URL** component, enter URLs you want to fetch content from. Ensure they start with `http://` or `https://`.\n3. Open the **Playground**. A blog post is written from the content fetched by the **URL** component.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.blog_writer.bf39194c"
"i18n_key": "template_notes.blog_writer.bf39194c",
"template": {}
},
"type": "note"
},

4
src/backend/base/langflow/initial_setup/starter_projects/Custom Component Generator.json
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@ -858,8 +858,8 @@
"description": "# 📖 README\nHi! I'm here to help you create custom components for Langflow. Think of me as your technical partner who can help turn your ideas into working components! \n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n\n2. 💭 Tell Me What You Want to Build.\nSimply describe what you want your component to do in plain English. For example:\n- \"I need a component that sends Slack messages\"\n- \"I want to create a tool that can process CSV files\"\n- \"I need something that can translate text\"\n\n\nReady to build something awesome? 🚀 Let's get started!",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.custom_component_generator.e1782063"
"i18n_key": "template_notes.custom_component_generator.e1782063",
"template": {}
},
"type": "note"
},

6
src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json
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@ -1586,10 +1586,10 @@
"description": "# 📖 README\n\nWelcome to the Instagram Copywriter! This flow helps you create compelling Instagram posts with AI-generated content and image prompts.\n\n## Quick start\n- Configure your **Model Provider** with your API credentials.\n- Add your **Tavily API Key** to the **Tavily AI Search** component.\n\n## Using the Flow\n**Enter Your Topic**\n - In the Chat Input, enter a brief description of the topic you want to post about.\n - Example: \"Create a post about meditation and its benefits\"",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.instagram_copywriter.f27f9c29",
"template": {
"backgroundColor": "amber"
},
"i18n_key": "template_notes.instagram_copywriter.f27f9c29"
}
},
"type": "note"
},
@ -3423,4 +3423,4 @@
"chatbots",
"agents"
]
}
}

16
src/backend/base/langflow/initial_setup/starter_projects/Invoice Summarizer.json
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@ -122,10 +122,10 @@
"description": "# 📖 README\nLeverage the **Needle Search API** and an **Agent** to gather and summarize your invoice data quickly and accurately.\n\n## Prerequisites\n\n* A **Collection** and an **API Key** from your [Needle.ai](https://needle-ai.com) deployment\n* An [OpenAI API key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n\n2. Load your invoices into your Needle Collection. \n\n3. In the **Needle Search** tool, add your **Needle Collection ID** and **Needle API Key**.\n\n4. Open the **Playground** and query your invoices. The **Agent** component determines the correct query and search size for data retrieval.\n",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.invoice_summarizer.b5ab0aab",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.invoice_summarizer.b5ab0aab"
}
},
"type": "note"
},
@ -601,10 +601,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.invoice_summarizer.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.invoice_summarizer.cd87cff6"
}
},
"type": "note"
},
@ -628,10 +628,10 @@
"description": "### Add your Needle Search API key here 👇",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.invoice_summarizer.c3786403",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.invoice_summarizer.c3786403"
}
},
"type": "note"
},
@ -715,7 +715,7 @@
}
],
"pinned": false,
"score": 7.568328950209746e-06,
"score": 7.568328950209746e-6,
"template": {
"_type": "Component",
"code": {
@ -1778,4 +1778,4 @@
"assistants",
"agents"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Market Research.json
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@ -703,10 +703,10 @@
"description": "# 📖 README\nThis flow helps you gather comprehensive information about companies for sales and business intelligence purposes.\n\n## Prerequisites\n\n- **[Tavily API Key](https://docs.tavily.com/welcome)**\n- **[OpenAI API Key](https://platform.openai.com/)**\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Add your **Tavily API key** to the **Tavily Search** component.\n3. In the **Chat Input**, enter a company name you want to research.\n4. Open the **Playground** and research the company. The **Structured Output** component transforms the raw LLM response into structured data, and the **Parser** component presents the data as text for the **Chat output** component to present.",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.market_research.a1643ecd",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.market_research.a1643ecd"
}
},
"type": "note"
},
@ -2353,4 +2353,4 @@
"assistants",
"agents"
]
}
}

20
src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json
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@ -2340,10 +2340,10 @@
"description": "### Add your Assembly AI API key and audio file here",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.meeting_summary.4868f127",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.meeting_summary.4868f127"
}
},
"type": "note"
},
@ -2370,10 +2370,10 @@
"description": "### Add your Assembly AI API key here",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.meeting_summary.14364ee4",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.meeting_summary.14364ee4"
}
},
"type": "note"
},
@ -2400,10 +2400,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.meeting_summary.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.meeting_summary.cd87cff6"
}
},
"type": "note"
},
@ -2430,10 +2430,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.meeting_summary.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.meeting_summary.cd87cff6"
}
},
"type": "note"
},
@ -2806,8 +2806,8 @@
"description": "# 📖 README\nThis flow automatically transcribes and summarizes meetings by converting audio recordings into concise summaries using **AssemblyAI** and **OpenAI GPT-4**. \n\n## Prerequisites\n\n- **[AssemblyAI API Key](https://www.assemblyai.com/)**\n- **[OpenAI API Key](https://platform.openai.com/)**\n\n## Quick start\n\n1. Upload an audio file. Most common audio file formats are [supported](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/components/assemblyai/assemblyai_start_transcript.py#L27).\n2. To run the summary generator flow, click **Playground**.\n\nThe flow transcribes the audio using **AssemblyAI**.\nThe transcript is formatted for AI processing.\nThe **GPT-4** model extracts key points and insights.\nThe summarized meeting details are displayed in a chat-friendly format.\n\n\n\n",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.meeting_summary.136e4d60"
"i18n_key": "template_notes.meeting_summary.136e4d60",
"template": {}
},
"type": "note"
},

4
src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json
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@ -676,8 +676,8 @@
"description": "# 📖 README\nThis flow extends the **Basic Prompting** template by adding a **Message History** component that can retrieve up to 100 previous chat messages as context for the current conversation.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Open the **Playground**, and then tell the LLM your name.\n3. Start a new chat session in the Playground, and then ask, `what is my name`. The LLM is able to retrieve your name from the stored chat history.\n\n## About the Message History component\nThe **Language Model** and **Agent** components have built-in chat memory that is enabled by default and functionally the same as the **Message History** component.\nOnly use the **Message History** component when you want to store or retrieve chat memory from an external chat memory database, or when you need to retrieve chat memory outside of the current session context, such as in a non-chat flow or by supplying memories from other chats to a different session. For more information, see [Store chat memory](https://docs.langflow.org/memory#store-chat-memory).",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.memory_chatbot.07ea6a8e"
"i18n_key": "template_notes.memory_chatbot.07ea6a8e",
"template": {}
},
"type": "note"
},

14
src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json
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@ -130,10 +130,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.news_aggregator.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.news_aggregator.cd87cff6"
}
},
"type": "note"
},
@ -157,10 +157,10 @@
"description": "### Add your AgentQL API key here",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.news_aggregator.c93afbb2",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.news_aggregator.c93afbb2"
}
},
"type": "note"
},
@ -835,10 +835,10 @@
"description": "# 📖 README\nThis flow extracts structured data from a URL and saves it into a JSON file.\n\n## Prerequisites\n\n* **[AgentQL API Key](https://dev.agentql.com/api-keys)**\n* **[OpenAI API Key](https://platform.openai.com/)**\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Add your [AgentQL API Key](https://dev.agentql.com/api-keys) to the **AgentQL** component.\n3. Click **Playground** and enter a question.\n\nThe **Agent** component populates the **AgentQL** component's **URL** and **Query** fields, and returns a structured response to your question. Then the extracted data is saved into a JSON file `news-aggregated.json`, which can be found in your current project directory.",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.news_aggregator.85a46aa0",
"template": {
"backgroundColor": "amber"
},
"i18n_key": "template_notes.news_aggregator.85a46aa0"
}
},
"type": "note"
},
@ -2223,4 +2223,4 @@
"web-scraping",
"agents"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json
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@ -2079,10 +2079,10 @@
"description": "# 📖 README\nThis Langflow project provides an integration for the NVIDIA RTX Remix Toolkit through its REST API.\n\n## Prerequisites\n\nBefore using this project, ensure you have completed the following steps:\n\n1. **Install RTX Remix Toolkit**\n You must have the RTX Remix Toolkit installed on your system. Follow the installation guide here:\n [Installing the RTX Remix Toolkit](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/docs/installation/install-toolkit.html)\n\n2. **Run RTX Remix Toolkit**\n Make sure the RTX Remix Toolkit application is running before using this Langflow project.\n\n3. **Create/Open a Project**\n You must have an RTX Remix project opened within the Toolkit. Learn how to set up a project here:\n [Setting Up a Project with the RTX Remix Toolkit](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/docs/gettingstarted/learning-toolkitsetup.html)\n\n### Quick Start Tutorial\n\nTo quickly get started with RTX Remix, follow the [Building Your First Mod for the RTX Remix Sample](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/docs/tutorials/tutorial-remixtool.html) tutorial.\n\nIt goes through the process of installing the various required parts, setting them up and getting a project up and running.\n\n## Getting Started\n\nOnce all prerequisites are met, the Langflow project should work without additional configuration.\n\n### Testing the Connection\n\nTo verify everything is working correctly:\n\n1. Open the Langflow project\n2. Locate the **RTX Remix MCP Connection** node\n3. Click the **refresh button** on the node\n4. Verify that the various REST API tools appear\n\nIf the REST API tools appear after refreshing, your connection to RTX Remix Toolkit is working properly and you can begin using the available tools.\n\n## Additional Resources\n\n- [RTX Remix Documentation](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/)\n- [RTX Remix MCP Documentation](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/docs/howto/learning-mcp.html)\n- [RTX Remix REST API Documentation](https://docs.omniverse.nvidia.com/kit/docs/rtx_remix/latest/docs/howto/learning-restapi.html)",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.nvidia_rtx_remix.7c2d3875",
"template": {
"backgroundColor": "lime"
},
"i18n_key": "template_notes.nvidia_rtx_remix.7c2d3875"
}
},
"type": "note"
},
@ -2802,4 +2802,4 @@
"tags": [
"agents"
]
}
}

14
src/backend/base/langflow/initial_setup/starter_projects/Pokédex Agent.json
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@ -654,10 +654,10 @@
"description": "## Open the playground and ask anything about a Pokémon! ⚡ 🐹",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.pok_dex_agent.5ee09434",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.pok_dex_agent.5ee09434"
}
},
"type": "note"
},
@ -684,8 +684,8 @@
"description": "# 📖 README\nCollect research on Pokémon with a specialized **Agent** and the Pokédex API.\n\n## Prerequisites\n\n* An [OpenAI API key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Click **Playground** and ask about your favorite Pokémon.\nThe **Agent** queries the Pokedex API and returns a formatted entry.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.pok_dex_agent.e977aa45"
"i18n_key": "template_notes.pok_dex_agent.e977aa45",
"template": {}
},
"type": "note"
},
@ -712,10 +712,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.pok_dex_agent.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.pok_dex_agent.cd87cff6"
}
},
"type": "note"
},
@ -1833,4 +1833,4 @@
"tags": [
"agents"
]
}
}

18
src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json
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@ -1467,10 +1467,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.price_deal_finder.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.price_deal_finder.cd87cff6"
}
},
"type": "note"
},
@ -1494,10 +1494,10 @@
"description": "### 💡 Add your AgentQL API key here",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.price_deal_finder.128b41fe",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.price_deal_finder.128b41fe"
}
},
"type": "note"
},
@ -1522,8 +1522,8 @@
"description": "# 📖 README\nThis flow searches and compares prices of a product on the web.\n## Prerequisites\n\n* **[AgentQL API Key](https://dev.agentql.com/api-keys)**\n* **[OpenAI API Key](https://platform.openai.com/)**\n* **[TavilyAI Search API Key](https://tavily.com/)**\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Add your [AgentQL API Key](https://dev.agentql.com/api-keys) to the **AgentQL** component.\n3. Add your [TavilyAI Search API Key](https://tavily.com/) to the **Tavily AI Search** component.\n4. Click **Playground** and enter a product in chat. For example, search \"iPhone 16 Pro 512 GB\")\n* The **Agent** component populates the **Tavily AI Search** component's **Search Query** field, and the **Agent QL** component's **URL** and **Query** fields. \n\n* The **Agent** returns a structured response to your searcn in the chat.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.price_deal_finder.8d99099b"
"i18n_key": "template_notes.price_deal_finder.8d99099b",
"template": {}
},
"type": "note"
},
@ -1550,10 +1550,10 @@
"description": "### 💡 Add your Tavily AI Search key here",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.price_deal_finder.71f0e5f4",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.price_deal_finder.71f0e5f4"
}
},
"type": "note"
},
@ -2204,4 +2204,4 @@
"web-scraping",
"agents"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json
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@ -961,10 +961,10 @@
"description": "# 📖 README\n\nWelcome to the Research Agent! This flow helps you conduct in-depth research on various topics using AI-powered tools and analysis.\n\n## Quick start\n- Configure your **Model Provider** with your API credentials.\n- Add your **Tavily API Key** to the Tavily AI Search component.\n \n## Using the Flow\n - Type your research question or topic into the Chat Input node.\n - Be specific and clear about what you want to investigate.",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.research_agent.f5853a2c",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.research_agent.f5853a2c"
}
},
"type": "note"
},
@ -3404,4 +3404,4 @@
"assistants",
"agents"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json
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@ -904,8 +904,8 @@
"description": "# 📖 README\nThis template translates research paper summaries on ArXiv into Portuguese and summarizes them. \n Using **Langflow’s looping mechanism**, the template iterates through multiple research papers, translates them with the **OpenAI** model component, and outputs an aggregated version of all translated papers. \n\n## Quick start\n1. Configure your **Model Provider** with your API credentials. \n2. In the **Playground**, enter a query related to a research topic (for example, “Quantum Computing Advancements”). \n\n The flow fetches a list of research papers from ArXiv matching the query. Each paper in the retrieved list is processed one-by-one using the Langflow **Loop component**. \n\n The abstract of each paper is translated into Portuguese by the **OpenAI** model component. \n\n Once all papers are translated, the system aggregates them into a **single structured output**.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.research_translation_loop.60bb882f"
"i18n_key": "template_notes.research_translation_loop.60bb882f",
"template": {}
},
"type": "note"
},
@ -1650,4 +1650,4 @@
"chatbots",
"content-generation"
]
}
}

8
src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json
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@ -402,8 +402,8 @@
"description": "# 📖 README\nThis template creates strategic keywords based on your product and audience profile.\n\n### Prerequisites\n\n* [OpenAI API Key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n\n2. In the **Prompt** component, complete the following fields. Optionally, just run the flow with the included example values.\n\n* Product Information\n* Pain Points\n* Goals\n* Target Audience\n* Expertise Level\n* Review Output \n\n3. Open the **Playground**, and then click **Run Flow**. The LLM generates keywords based on your inputs.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.seo_keyword_generator.7c12d397"
"i18n_key": "template_notes.seo_keyword_generator.7c12d397",
"template": {}
},
"type": "note"
},
@ -880,10 +880,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.seo_keyword_generator.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.seo_keyword_generator.cd87cff6"
}
},
"type": "note"
},

6
src/backend/base/langflow/initial_setup/starter_projects/SaaS Pricing.json
View File

@ -664,8 +664,8 @@
"description": "# 📖 README\nWelcome to the SaaS Pricing Calculator! This flow helps you determine the optimal monthly subscription price for your software service.\n\n## Instructions\n\n1. Prepare Your Data\n - Gather information on monthly infrastructure costs\n - Calculate customer support expenses\n - Estimate continuous development costs\n - Decide on your desired profit margin\n - Determine the estimated number of subscribers\n\n2. Input Values\n - Enter the gathered data into the respective fields in the Prompt node\n - Double-check the accuracy of your inputs\n\n3. Run the Flow\n - Click the \"Run\" button to start the calculation process\n - The flow will use Chain-of-Thought prompting to guide the AI through the steps\n\n4. Review the Results\n - Examine the output in the Chat Output node\n - The result will show a breakdown of costs and the final subscription price\n\n5. Adjust and Refine\n - If needed, modify your inputs to explore different pricing scenarios\n - Re-run the flow to see how changes affect the final price\n\nRemember: Regularly update your costs and subscriber estimates to keep your pricing model accurate and competitive! 💼📊",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.saas_pricing.77b328bb"
"i18n_key": "template_notes.saas_pricing.77b328bb",
"template": {}
},
"type": "note"
},
@ -1486,4 +1486,4 @@
"agents",
"assistants"
]
}
}

14
src/backend/base/langflow/initial_setup/starter_projects/Search agent.json
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@ -826,8 +826,8 @@
"description": "# <20> README\nThis template connects the **Search Point** endpoint from [ScrapeGraphAI](https://scrapegraphai.com) to an **Agent** component.\n\n## Prerequisites\n\n* [OpenAI API key](https://platform.openai.com/docs/overview)\n* [ScrapeGraphAI API key](https://dashboard.scrapegraphai.com)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n\n2. Add your **ScrapeGraphAI API key** to the **ScrapeGraphSearchApi** component.\n\n3. Open the **Playground** and ask your Agent a question. The Agent uses ScrapeGraph as a tool to answer you.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.search_agent.c93b5067"
"i18n_key": "template_notes.search_agent.c93b5067",
"template": {}
},
"type": "note"
},
@ -854,10 +854,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.search_agent.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.search_agent.cd87cff6"
}
},
"type": "note"
},
@ -881,10 +881,10 @@
"description": "### Add your ScrapeGraphAI API key here 👇",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.search_agent.dd9e880e",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.search_agent.dd9e880e"
}
},
"type": "note"
},
@ -1536,4 +1536,4 @@
"agents",
"assistants"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json
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@ -2386,8 +2386,8 @@
"description": "# 📖 README\nThis flow demonstrates how to chain multiple AI agents for comprehensive research and analysis. Each agent specializes in different aspects of the research process, building upon the previous agent's work. \n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Add your **Tavily API Key** to the **Tavily AI Search** component.\n3. Open the **Playground** and enter a query to run the flow. Be specific, clear, and include key aspects that you want the agents to analyze in a financial perspective.\nBecause this flow includes a financial analysis agent, useful queries should include a financial aspect, such as \"Should I invest in Tesla (TSLA)? Focus on AI development impact\". In contrast, asking the agent, \"Tell me about Tesla\" isn't as useful because it doesn't trigger the financial research agent or provide specific talking points for the other agents to research.\n\n## Next steps\nThis template uses financial analysis as an example. Try adapting it for other research-intensive tasks that require multiple perspectives and data sources.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.sequential_tasks_agents.1b5d73aa"
"i18n_key": "template_notes.sequential_tasks_agents.1b5d73aa",
"template": {}
},
"type": "note"
},
@ -4155,4 +4155,4 @@
"agents",
"web-scraping"
]
}
}

10
src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
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@ -126,10 +126,10 @@
"description": "# 📖 README\nRun an Agent with URL and Calculator tools available for its use. \nThe Agent decides which tool to use to solve a problem.\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Open the Playground and chat with the Agent. Request some information about a recipe, and then ask to add two numbers together. In the responses, the Agent will use different tools to solve different problems.\n\n## Next steps\nConnect more tools to the Agent to create your perfect assistant.\n\nFor more, see the [Langflow docs](https://docs.langflow.org/agents-tool-calling-agent-component).",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.simple_agent.89c2ae6b",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.simple_agent.89c2ae6b"
}
},
"type": "note"
},
@ -153,10 +153,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.simple_agent.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.simple_agent.cd87cff6"
}
},
"type": "note"
},
@ -1970,4 +1970,4 @@
"assistants",
"agents"
]
}
}

18
src/backend/base/langflow/initial_setup/starter_projects/Social Media Agent.json
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@ -590,10 +590,10 @@
"description": "### 💡 Add your Apify API key here ",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.social_media_agent.15789e48",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.social_media_agent.15789e48"
}
},
"type": "note"
},
@ -617,10 +617,10 @@
"description": "### 💡 Add your Apify API key here ",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.social_media_agent.15789e48",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.social_media_agent.15789e48"
}
},
"type": "note"
},
@ -647,10 +647,10 @@
"description": "# 📖 README\nExtract data with **Apify Actors** and analyze the data with an **Agent**.\n\n## Prerequisites\n\n* An [Apify API token](https://docs.apify.com/platform/integrations/api#api-token)\n* An [OpenAI API key](https://platform.openai.com/)\n\n## Quick start\n\n1. Configure your **Model Provider** with your API credentials.\n2. Enter your **Apify** API token in the **Apify Token** fields of the **Apify Actors** components.\n3. Open the **Playground** and chat with the agent. For example, task it with retrieving a profile bio and the latest video by using this prompt: \n ```\n Find the TikTok profile of the company OpenAI using Google search, then show me the profile bio and their latest video.\n ```",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.social_media_agent.501213ba",
"template": {
"backgroundColor": "amber"
},
"i18n_key": "template_notes.social_media_agent.501213ba"
}
},
"type": "note"
},
@ -1234,10 +1234,10 @@
"description": "### Configure your Model Provider",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.social_media_agent.cd87cff6",
"template": {
"backgroundColor": "transparent"
},
"i18n_key": "template_notes.social_media_agent.cd87cff6"
}
},
"type": "note"
},
@ -1886,4 +1886,4 @@
"agent",
"assistants"
]
}
}

6
src/backend/base/langflow/initial_setup/starter_projects/Travel Planning Agents.json
View File

@ -754,8 +754,8 @@
"description": "# 📖 README\n\nThe travel planning system is a smart setup that uses several specialized Agent components to help plan incredible trips. Imagine each agent as a travel expert focusing on a part of your journey. Here's how it works:\nEach agent has a role defined in the **Agent Instructions** and relevant **Tools** attached. The user submits a query through the **Chat Input**, and the three agents create a complete travel plan based on the user's query.\n\n## Quick start\n1. Configure your **Model Provider** with your API credentials.\n2. Add your **Search API** key to the Search API component.\n2. Run the flow in the **Playground**.",
"display_name": "",
"documentation": "",
"template": {},
"i18n_key": "template_notes.travel_planning_agents.d6f6704d"
"i18n_key": "template_notes.travel_planning_agents.d6f6704d",
"template": {}
},
"type": "note"
},
@ -3529,4 +3529,4 @@
"agents",
"openai"
]
}
}

4
src/backend/base/langflow/initial_setup/starter_projects/Twitter Thread Generator.json
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@ -1651,10 +1651,10 @@
"description": "# 📖 README\nWelcome to the Twitter Thread Generator! This flow helps you create compelling Twitter threads by transforming your structured inputs into engaging content.\n\n## Instructions\n\n1. Prepare Your Inputs\n - Fill in the \"Context\" with your main message or story\n - Define \"Content Guidelines\" for thread structure and style\n - Specify \"Profile Type\" and \"Profile Details\" to reflect your brand identity\n - Set \"Tone and Style\" to guide the communication approach\n - Choose \"Output Format\" (thread) and desired language\n\n2. Configure the Prompt\n - The flow uses a specialized prompt template to generate content\n - Ensure all input fields are connected to the prompt node\n\n3. Run the Generation\n - Execute the flow to process your inputs\n - The OpenAI model will create the thread based on your specifications\n\n4. Review and Refine\n - Examine the output in the Chat Output node\n - If needed, adjust your inputs and re-run for better results\n\n5. Finalize and Post\n - Once satisfied, copy the generated thread\n - Post to Twitter, maintaining the structure and flow\n\nRemember: Be specific in your context and guidelines for the best results! 🚀\n",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.twitter_thread_generator.3ee42d8b",
"template": {
"backgroundColor": "amber"
},
"i18n_key": "template_notes.twitter_thread_generator.3ee42d8b"
}
},
"type": "note"
},

6
src/backend/base/langflow/initial_setup/starter_projects/Youtube Analysis.json
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@ -1848,10 +1848,10 @@
"description": "# 📖 README\nThis flow performs comprehensive analysis of YouTube videos.\n1. Extract video comments and transcripts.\n2. Run sentiment analysis on comments using LLM.\n3. Combine transcript content and comment sentiment for comprehensive video analysis.\n## Quick start\n- Configure your **Model Provider** with your API credentials.\n- Add your **YouTube Data API v3 key**\n- If you don't have a YoutTube API key, create one in the [Google Cloud Console](https://console.cloud.google.com).\n- Ensure the chat input is a valid YouTube video URL. A sample URL is provided in the chat input component.\n",
"display_name": "",
"documentation": "",
"i18n_key": "template_notes.youtube_analysis.8d068d5f",
"template": {
"backgroundColor": "neutral"
},
"i18n_key": "template_notes.youtube_analysis.8d068d5f"
}
},
"type": "note"
},
@ -2697,4 +2697,4 @@
"agents",
"assistants"
]
}
}