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  "body_markdown": "📘 Introduction\nMany AI apps need to send more than one request to a model. You might classify several support tickets, summarize a list of notes, rewrite product descriptions, or generate short explanations for many topics.\nYou could call\ninvoke()\nagain and again in a loop, but LangChain also gives you batching methods for running multiple inputs through the same model or chain. In this tutorial, you will learn how to use\nbatch()\nand\nabatch()\nwith\nChatOpenAI\nin Python.\n💡\nLangChain batching is useful when you already have multiple independent inputs. It is different from streaming, chat memory, tools, agents, and the OpenAI Batch API.\n💡 Why batching matters\nBatching helps when each input can be processed independently. Instead of manually writing a loop and collecting each response yourself, you can pass a list of inputs to the same runnable and receive a list of outputs back.\nThis is useful for AI engineering workflows such as labeling examples, preparing synthetic data, evaluating prompts, summarizing multiple text snippets, or running the same chain over many user questions.\n✅\nBatching does not mean one giant prompt. It means several separate inputs are sent through the same runnable interface.\n✅ Prerequisites\nBefore getting started, make sure you have:\n☑️ Python installed\n☑️ Basic Python knowledge\n☑️ An OpenAI API key\n☑️ A terminal or command prompt\n⚙️1️⃣ Create a project folder\nCreate a new local project folder:\nmkdir langchain-batch-requests\ncd langchain-batch-requests\n🐍2️⃣ Create a virtual environment\nCreate and activate a virtual environment:\npython -m venv .venv\nsource .venv/bin/activate\nOn Windows, activate it with:\n.venv\\Scripts\\activate\n📦3️⃣ Install libraries\nInstall the packages for LangChain and OpenAI:\npip install langchain-core langchain-openai\n🔐4️⃣ Set your API key\nSet your OpenAI API key as an environment variable. Replace the placeholder with your own key and never put real secrets into code files.\nexport OPENAI_API_KEY=\"your_api_key_here\"\nOn Windows PowerShell, use:\n$env:OPENAI_API_KEY=\"your_api_key_here\"\n🧪5️⃣ Start with repeated invoke calls\nBefore using batching, let us start with a normal loop. This shows the problem we are improving.\nCreate a file named\nloop_invoke.py\nand add this code:\nfrom langchain_openai import ChatOpenAI\n\nmodel = ChatOpenAI(model=\"gpt-4.1-mini\", temperature=0)\n\ntopics = [\n    \"LangChain batching\",\n    \"AI prompt evaluation\",\n    \"model latency\",\n]\n\nfor topic in topics:\n    response = model.invoke(f\"Explain {topic} in one short sentence.\")\n    print(f\"{topic}: {response.content}\")\nloop_invoke.py\nRun the script:\npython loop_invoke.py\nThis works, but you are manually managing the loop. For three inputs that is fine. For dozens or hundreds of independent inputs, LangChain's batch methods make the workflow cleaner.\n🎓\nWant the full Academy version? Next we replace the manual loop with\nbatch()\n, add concurrency control, and finish with an async\nabatch()\nexample.\nYou can view this post with the tier:\n      Academy Membership\nJoin academy now to read the post and get access to the full library of premium posts for academy members only.\nJoin Academy\nAlready have an account?\nSign In",
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