This article provides a complete, practical walkthrough of the Wait Splitout Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating Batch Queries with Anthropic Claude Using n8n: A Low-Code Workflow
   
   Meta Description:  
   Explore how to automate and streamline large-scale prompt submission to Claude (by Anthropic) using an advanced n8n workflow. This guide walks through batch processing, real-time polling, and intelligent parsing, enabling scalable AI request handling.
   
   Keywords:  
   n8n workflow, Anthropic API, Claude AI, automation, prompt batching, low code, batch processing, AI assistant, Claude batch API, Langchain memory, AI workflow automation, Anthropic Claude integration
   
   Third-Party APIs Used:
   
   1. Anthropic API (Claude)
      - Endpoint(s) used:
        - POST /v1/messages/batches
        - GET /v1/messages/batches/{batch_id}
      - Documentation: https://docs.anthropic.com/en/docs
   
   Article:
   
   Automating Parallel Prompt Processing with Anthropic Claude and n8n
   
   Artificial Intelligence (AI) models like Claude by Anthropic are reshaping how we generate content, assist users, and automate tasks. But what happens when you want not just one, but dozens—or even hundreds—of responses simultaneously?
   
   Enter n8n, a powerful, extensible workflow automation tool. By leveraging Claude’s batch messages endpoint and combining it with n8n’s modular logic and Langchain memory integrations, you can fully automate large-scale prompt submission and result retrieval with minimal code.
   
   In this article, we’ll explore a sophisticated n8n workflow designed specifically to process multiple prompts in parallel using Claude’s batch API. This setup ensures reliability, scalability, and integrates support for context-aware AI interactions via memory.
   
   🚀 The Goal  
   Automate the lifecycle of a batched prompt submission to Anthropic’s Claude model—from submission to polling, and eventually parsing final responses for downstream use.
   
   🧠 Key Features of the Workflow
   
   1. Parallel Prompt Handling  
   The workflow accepts an array of prompts—each customized with its own `custom_id`, `model`, token limits, and message history. These are bundled and sent to Anthropic’s batch endpoint in one HTTP call. This makes bulk processing efficient and manageable.
   
   2. Polling for Completion  
   Once submitted, processing status is not immediately available. To handle this, the workflow uses a combination of polling and conditional logic. It keeps checking the batch status until it transitions to "ended", ensuring responses are pulled only when they’re ready.
   
   3. JSONL Response Parsing  
   Claude returns batch responses in JSONL (JSON Lines) format, which consists of individual prompt responses separated by newlines. The workflow includes a custom code node to split, parse, and convert this raw response into a structured array of results—making it easier to filter or reuse responses.
   
   4. Claude’s Memory-Enabled Prompts  
   By integrating with Langchain’s AI memory (via the @n8n/n8n-nodes-langchain plugin), this workflow can store chat history between user and assistant messages, enabling more natural interactions.
   
   5. Example Usage Variants  
   The n8n workflow supports two example input methods:
      - A simple single query: “Hey Claude, tell me a fun fact about bees!”
      - A contextual prompt using prior chat memory (e.g., from earlier user and assistant interactions).
   
   These examples are joined, formatted, and pushed through the same batching logic—showing flexibility in how input is structured and processed.
   
   🧪 Usage Flow at a Glance  
   
   1. Triggered Manually or Remotely  
   The "When Executed by Another Workflow" or "Manual Trigger" nodes initiate the workflow. Inputs like `anthropic-version` and a list of `requests` are required.
   
   2. Submit to Claude’s Batch API  
   The structured requests are POSTed to https://api.anthropic.com/v1/messages/batches, each containing message content, the model (e.g., `claude-3-5-haiku-20241022`), and a `custom_id`.
   
   3. Evaluate and Wait  
   Workflow uses an IF node to detect when the batch finishes processing (`processing_status == "ended"`). If not done, it waits 10 seconds and checks again.
   
   4. Fetch and Parse  
   Once complete, the batch’s result URL is accessed. The returned JSONL is then split and parsed into usable JSON objects.
   
   5. Filter and Store Results  
   Using `custom_id`, specific results (e.g., first-prompt-in-my-batch or second-prompt-in-my-batch) are isolated. Final AI responses are cleanly saved for follow-up processing or display.
   
   🔧 Developer Notes  
   
   - The workflow supports any number of requests in a batch—limited only by Anthropic’s API constraints.  
   - Full support for Claude versioning by including a configurable `anthropic-version` header.  
   - Each prompt is independently tracked via `custom_id` to aid mapping requests to responses.  
   - It uses memoryManager and memoryBufferWindow nodes from Langchain for handling ongoing conversations.  
   
   🧩 Why Use This Approach?
   
   - Scalability: Instead of making a separate call per prompt, batching reduces overhead and increases throughput.  
   - Reliability: Polling ensures you only proceed once processing is truly complete.  
   - Flexibility: Can adapt to both stateless and stateful AI prompt use cases.  
   - Low Code Friendly: All of this is achieved in n8n—with just a touch of JavaScript for parsing and formatting.
   
   🎯 Conclusion
   
   This powerful n8n workflow unlocks scalable, asynchronous prompt processing with Anthropic Claude. Whether you’re building AI assistants, query engines, or educational bots, automating batch submissions can save time and reduce cost. The integration of Claude’s batch API with n8n’s modular automation universe offers a potent blueprint for anyone seeking advanced AI capabilities—without having to write and maintain their own backend infrastructure.
   
   Ready to scale your AI prompt processing? Claude and n8n are just one workflow away.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.