If you need to call a guest list one by one, confirm attendance, collect notes, and compile a summary, How to Configure an Event Guest Confirmation Agent with OpenClaw is a practical way to automate the whole workflow with AI voice calls. The setup below shows how to connect OpenClaw to WisGate, define the conversation, run outbound calls sequentially, and turn the responses into something your team can act on quickly. Start automating your event guest confirmations today with OpenClaw and WisGate’s AI voice calls to save time and streamline RSVP collection automatically.
Introduction: The Challenge of Manual Guest Confirmation Calls
Anyone who has handled event RSVP tracking knows the routine: you get a guest list, then spend hours calling people, leaving voicemails, sending follow-up texts, and updating a spreadsheet. It is repetitive work, and the details get messy fast. One guest says they are coming but might be late, another asks to bring a plus-one, and someone else wants to share a dietary restriction. If you are doing that manually, every call adds more room for missed notes.
An AI voice agent can handle those calls for you. In this workflow, OpenClaw acts as the orchestration layer for outbound calling, while WisGate provides access to the model behind the conversation. That means you can define a call script, have the system speak with each guest, capture their response, and store the result in a structured way. The goal is not to replace event planning. The goal is to remove the repetitive calling.
This guide focuses on a B2C-adjacent use case: confirming guests for an event. You will see the configuration steps, the API connection details, the conversation design, and the post-call summary flow. By the end, you should have a clear path from guest list to attendance report.
Preparing Your Environment: Installing and Configuring OpenClaw
Before you connect anything to WisGate, prepare OpenClaw locally and make sure you can edit its configuration file. Clawdbot stores its configuration in a JSON file in your home directory, so the setup starts there. You will also need a WisGate API key and access to the model you want to use for the voice interaction and summary generation.
The core idea is simple: OpenClaw needs to know where to send model requests, which provider to use, and which model ID to call. For this use case, the custom provider is named moonshot, and it points to WisGate’s API endpoint. The model is claude-opus-4-6, which is specified with a 256000-token context window and a maxTokens value of 8192. Those values matter because call transcripts, guest notes, and summary prompts can add up quickly.
Follow the setup in order. If you skip the restart step, OpenClaw will keep using the old configuration and your changes will not take effect.
- Open your terminal.
- Edit the configuration file: nano ~/.openclaw/openclaw.json
- Paste the updated model configuration into the models section.
- Save the file with Ctrl + O, then press Enter.
- Exit nano with Ctrl + X.
- Stop OpenClaw with Ctrl + C.
- Restart it with openclaw tui.
Editing the OpenClaw Configuration File
Here is the configuration snippet to place in ~/.openclaw/openclaw.json. It defines a custom provider called moonshot that points to WisGate, uses the openai-completions API type, and selects claude-opus-4-6 for the model.
"models": {
"mode": "merge",
"providers": {
"moonshot": {
"baseUrl": "https://api.wisgate.ai/v1",
"apiKey": "WISGATE-API-KEY",
"api": "openai-completions",
"models": [
{
"id": "claude-opus-4-6",
"name": "Claude Opus 4.6",
"reasoning": false,
"input": [
"text"
],
"cost": {
"input": 0,
"output": 0,
"cacheRead": 0,
"cacheWrite": 0
},
"contextWindow": 256000,
"maxTokens": 8192
}
]
}
}
}
A few details are especially useful here. The baseUrl is https://api.wisgate.ai/v1, the api type is openai-completions, and the apiKey field should contain your own key rather than the placeholder value. The model is set to reasoning false, which keeps the configuration focused on conversational processing and response handling. The large contextWindow of 256000 tokens gives you room for long transcripts, follow-up notes, and structured output. After editing, save with Ctrl + O, hit Enter, then Ctrl + X to exit. Finally, stop the running instance with Ctrl + C and relaunch it with openclaw tui so the new provider settings are loaded.
Integrating WisGate API for AI Voice Calls
WisGate gives OpenClaw a single API endpoint for model access: https://api.wisgate.ai/v1. In this setup, OpenClaw sends the prompt, guest context, and voice-call instructions to that endpoint using the openai-completions API type. That is useful because it keeps the integration close to a familiar OpenAI-style request pattern while still routing traffic through WisGate.
For authentication, use your WisGate API key in the config. Treat it like any other secret: do not hard-code it into public scripts or shared documents. If you are testing locally, keep it in the configuration file only on your machine and rotate it if you think it has been exposed.
The recommended model in this article is claude-opus-4-6. It has a 256000-token context window and supports up to 8192 max tokens per call. For RSVP calls, that gives you enough room to include event details, the guest’s previous response history, and a structured instruction set for the call agent. It is also a practical choice when you want a single model to handle both the live interaction and the post-call write-up.
If you want to explore response formatting and model testing patterns, WisGate AI Studio is available at https://wisgate.ai/studio/image. Even though that page is focused on image models, it is still a useful reference for how WisGate structures requests and outputs across the platform. For developers, that makes it easier to reason about request shape, output formatting, and how a unified API layer behaves across different workloads.
Designing the Conversation Flow for RSVP Collection
A good call script keeps the interaction short, clear, and predictable. The guest should immediately understand who is calling, why the call is happening, and what the AI needs from them. For RSVP collection, the conversation flow usually has four parts: greeting, confirmation, optional detail collection, and closing.
Start by telling the agent exactly what to ask. A simple prompt might tell it to confirm attendance, ask whether the guest is coming, collect any notes, and end politely. If the guest says yes, the agent should record that response. If the guest says no, the agent should ask whether the response should be marked as declined. If the guest mentions special requests, the agent should capture them in a separate note field.
This is where AI event RSVP automation works well. You are not trying to hold a long conversation. You are guiding the model through a narrow task with predictable output fields. Keep the wording consistent so the summaries are easy to compile later.
Practical conversation rules help a lot:
- Ask only one question at a time.
- Confirm attendance before asking for extra notes.
- Repeat names, dates, and event times if the guest seems unsure.
- End with a short thank-you message.
Automating the Outbound Call Process Using OpenClaw
Once the conversation logic is ready, wire it into the outbound call process. The key pattern is to ingest the guest list, loop through each entry, and place calls sequentially rather than all at once. Sequential calling is easier to monitor, and for an event guest confirmation agent, it also matches how a human assistant would work.
A typical workflow looks like this:
- Load the guest list from a CSV file, spreadsheet export, or API feed.
- Normalize the fields you need, such as name, phone number, RSVP status, and notes.
- Queue the entries in order.
- Call one guest at a time.
- Capture the result after each call.
- Move to the next guest only after the previous call is complete.
That pattern keeps your data tidy and reduces confusion if a call fails or a guest asks for a callback. In OpenClaw, you can build this as an orchestration loop that sends one contact at a time to the voice agent. If the guest is unavailable, mark the status and retry later. If the guest answers, let the agent collect the response and then persist the result.
For the use case here, the output you want is simple: attending, not attending, maybe attending, plus notes. Keep the fields stable so they can be summarized later without extra cleanup.
Post-Call Processing: Summarizing and Compiling Guest Responses
After each call ends, the raw transcript is usually too noisy for an event team to read line by line. This is where WisGate’s conversation synthesis capabilities become useful. Feed the transcript, call metadata, and any structured notes back through the model to generate a concise summary for each guest.
The summary should include the guest’s RSVP status, any special notes, and any follow-up action. For example, if the guest is attending but asked for vegetarian food, the summary should call that out explicitly. If the guest requested a callback, that should appear as a separate action item. When you process all calls this way, you end up with a clean attendance report instead of a folder full of transcripts.
Use a consistent summary template. For example:
- Guest name
- RSVP status
- Notes
- Follow-up action
That structure is especially useful when you compile responses for a planner, coordinator, or client. It also makes it easier to export the results into a spreadsheet or CRM later. Because claude-opus-4-6 supports a 256000-token context window, you can include a lot of call detail without forcing the summary prompt to be chopped down too aggressively.
Pricing Considerations and Cost Efficiency with WisGate
Cost matters when you run repeated model calls. In the provided pricing comparison, the official rate is 0.068 USD per image, while WisGate provides the same stable quality at 0.058 USD per image. That is about 15% cost savings. The platform also reports consistent 20-second processing for base64 outputs sized 0.5k to 4k, which helps keep output timing predictable.
Even though this article focuses on voice automation, the pricing data is useful because it shows how WisGate approaches routing efficiency across model categories. For teams building AI voice and conversation workflows, that kind of predictable cost structure makes it easier to plan usage. If you are testing model behavior or generating companion assets in WisGate AI Studio, the same endpoint discipline and output consistency can help keep development practical.
Conclusion: Benefits of Automated AI Voice RSVP Collection
An event guest confirmation agent built with OpenClaw and WisGate turns a repetitive calling task into a structured workflow. You can ingest the guest list, place outbound calls one at a time, collect attendance details, and generate post-call summaries without manually typing every response. That gives your team a cleaner RSVP process and a clearer view of who is coming, who needs follow-up, and what notes matter.
The main takeaway is simple: define the provider correctly, use the WisGate API endpoint at https://api.wisgate.ai/v1, configure claude-opus-4-6 with the right context limits, and keep the conversation flow short and specific. From there, OpenClaw handles the calling sequence while WisGate handles the model side.
Try WisGate’s API now by visiting https://wisgate.ai/ and explore the “claude-opus-4-6” model for your next AI voice automation project. If you want to review model resources before you build, start with https://wisgate.ai/models.