Connect Slido to AI Agents: Automate Engagement and Meeting Exports

You want to connect Slido to an AI agent so your system can independently export event data, extract Q&A sessions, parse survey results, and analyze participant engagement. Here is exactly how to do it using Truto's /tools endpoint and SDK, bypassing the need to maintain a custom API wrapper for Slido's complex asynchronous export architecture.

Giving a Large Language Model (LLM) read access to your Slido instance is an engineering headache. You either spend weeks building a system that can handle background jobs and polling logic, or you use a managed infrastructure layer that handles the boilerplate for you. If your team uses ChatGPT, check out our guide on connecting Slido to ChatGPT, or if you are building on Anthropic's models, read our guide on connecting Slido to Claude. For developers building custom autonomous workflows, you need a programmatic way to fetch these tools and bind them directly to your agent framework.

This guide breaks down exactly how to fetch AI-ready tools for Slido, bind them natively to an LLM using LangChain (or any framework like LangGraph, CrewAI, or Vercel AI SDK), and execute complex meeting intelligence workflows. For a deeper look at the architecture behind this approach, refer to our research on architecting AI agents and the SaaS integration bottleneck.

The Engineering Reality of Custom Slido Connectors

Building AI agents is easy. Connecting them to external SaaS APIs is hard. Giving an LLM access to external data sounds simple in a prototype. You write a Node.js function that makes a fetch request and wrap it in an @tool decorator. In production, this approach collapses entirely - especially with an ecosystem designed for high-volume event data like Slido.

If you decide to integrate Slido yourself, you own the entire API lifecycle. Slido's API introduces several highly specific integration challenges that break standard LLM assumptions.

The Asynchronous Export Trap

Most AI agents are built to execute a function and wait for a synchronous JSON response. Slido does not work this way for bulk data. When your agent needs to read all the Q&A questions from a large all-hands meeting, it cannot simply call GET /questions.

Instead, the agent must trigger an asynchronous export job. Slido returns an HTTP 202 Accepted response containing an export_id. The agent must then take that ID and repeatedly poll a separate endpoint until the job finishes processing and provides a download URL. Standard LLM agents struggle immensely with this pattern. Without strict tool definitions and an orchestration loop, the LLM will hallucinate the contents of the HTTP 202 response instead of waiting for the actual data.

Highly Nested Relational Hierarchies

Slido data is not flat. Events contain Rooms, Rooms contain Sessions, Sessions contain Polls, and Polls contain Votes. When an agent needs to extract the results of a specific multiple-choice question, it needs to understand this exact hierarchy. Hand-coding individual endpoints for every layer of this hierarchy results in massive context bloat. You are forcing the LLM to learn the entire Slido entity-relationship model from scratch, leading to incorrect parameter generation and failed API calls.

Strict Rate Limiting Without Upstream Buffering

Slido enforces strict rate limits, especially when rapidly polling export endpoints. If your agent framework gets stuck in a tight while-loop trying to check the status of an export, it will quickly hit a wall.

It is critical to note how Truto handles this: Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream Slido API returns an HTTP 429 Too Many Requests error, Truto passes that error directly to the caller. Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification.

The caller (your AI agent framework) is solely responsible for reading these headers and initiating a retry or backoff sequence. If you build a custom connector, you have to write the parsing logic for Slido's specific rate limit headers. By using Truto, your agent relies on a single, standardized set of headers regardless of which SaaS tool it is querying.

Connecting Slido to AI Agents via Truto's Tools Endpoint

To bypass these architectural hurdles, you can use Truto's /tools endpoint. Truto maps Slido's underlying endpoints into normalized REST Proxy APIs. It then exposes descriptions and JSON schemas for all these methods directly to your LLM framework.

Instead of writing custom asynchronous polling logic for 15 different Slido endpoints, you initialize your agent like this using the Truto SDK:

import { ChatOpenAI } from "@langchain/openai";
import { TrutoToolManager } from "truto-langchainjs-toolset";
 
// 1. Initialize the LLM
const llm = new ChatOpenAI({
  modelName: "gpt-4o",
  temperature: 0,
});
 
// 2. Fetch AI-ready tools for the specific Slido account
const truto = new TrutoToolManager({
  apiKey: process.env.TRUTO_API_KEY
});
 
// 3. Get all Slido tools for a specific integrated account ID
const slidoTools = await truto.getTools("integrated-account-id-for-slido");
 
// 4. Bind the tools directly to the LLM
const agentWithSlido = llm.bindTools(slidoTools);

The agent now natively understands how to start exports and check their status, governed by strict JSON schemas that prevent hallucinated arguments.

Hero Tools for Slido AI Agents

To execute complex meeting and event workflows, your agent does not need to know the entire Slido API surface. It only needs high-leverage primitives. Here are the core Slido tools exposed via Truto that enable autonomous event data extraction.

create_a_slido_exports_event

This tool starts an asynchronous export of events in Slido. It returns an export job object (HTTP 202) with an export_id for polling status. The exported event records include event_id, owner_user_id, start_date, end_date, and timezone configurations. The request body accepts an optional filter object to target specific dates or owners.

"Export all events in Slido that took place last week, and tell me the export ID so we can track the job status."

create_a_slido_exports_question

This tool starts an asynchronous export of Q&A questions submitted during a Slido event. It returns the 202 export job object. Once polled successfully, the exported data includes question_id, participant_id, status, is_anonymous, is_starred, upvotes, and downvotes. This is the primary mechanism for extracting audience sentiment.

"Trigger an export of all Q&A questions for the Q3 All-Hands event. Filter for questions that have more than 10 upvotes."

create_a_slido_exports_poll

This tool initiates the extraction of high-level poll data. It returns basic poll metadata including the poll type, status, and creation date. This is critical for agents needing to map out which polls were run during a specific room or session before digging into the specific vote values.

"Start a data export for all polls associated with the Engineering Townhall event and give me the tracking ID."

create_a_slido_exports_participant

This tool handles the asynchronous export of participant data. Once the job completes, it provides event_id, participant_id, name, email, and first_joined_date. Agents use this tool to sync attendee lists against external CRM or marketing automation platforms.

"Export the participant list for the recent product launch webinar so we can cross-reference the emails against our Salesforce contacts."

create_a_slido_exports_surveys_quizz

This tool exports complete surveys and quizzes, grouping the underlying polls together. The exported records include the survey_quiz_id, room_id, and polls_count. Agents use this to understand the structure of complex multi-question assessments.

"Trigger an export of all quizzes administered during the new employee onboarding session yesterday."

get_single_slido_export_by_id

This is the crucial polling tool. It takes the export_id generated by any of the create_a_slido_exports_* tools and returns the current details of the specific export job. The agent must loop this tool until the job status indicates completion and a result is available.

"Check the status of export ID 'exp_123456'. If it is completed, parse the resulting URL for the data."

To view the complete schema definitions and the full list of available operations, visit the Slido integration page.

Building Multi-Step Workflows

AI agents excel when they chain multiple operations together to achieve a high-level goal. Because Slido relies heavily on the HTTP 202 asynchronous pattern, your agent loop must be explicitly designed to handle job submission, polling delays, and rate limit backoffs.

The Asynchronous Polling Loop

When a user asks for poll results, the agent must perform a multi-step sequence. Frameworks like LangGraph are ideal for this because they can maintain state across multiple execution loops.

sequenceDiagram
    participant User
    participant Agent as Agent Framework
    participant Truto as Truto API
    participant Slido as Slido API

    User->>Agent: "Get Q&A results for Event 123"
    Agent->>Truto: call create_a_slido_exports_question(event_id=123)
    Truto->>Slido: POST /exports/questions
    Slido-->>Truto: HTTP 202 (export_id: 999)
    Truto-->>Agent: return export_id: 999
    
    loop Polling
        Agent->>Truto: call get_single_slido_export_by_id(id=999)
        Truto->>Slido: GET /exports/999
        Slido-->>Truto: HTTP 200 (status: pending)
        Truto-->>Agent: status: pending
        Note over Agent: Agent waits / sleeps
    end

    Agent->>Truto: call get_single_slido_export_by_id(id=999)
    Truto->>Slido: GET /exports/999
    Slido-->>Truto: HTTP 200 (status: completed, url: data_link)
    Truto-->>Agent: data payload
    Agent-->>User: "Here are the top upvoted questions..."

Handling Rate Limits (HTTP 429)

Because your agent is polling, it will eventually hit a rate limit. As stated earlier, Truto does not absorb these errors. Your agent must intercept the failure, read the standardized ratelimit-reset header, pause execution, and try again.

Here is a conceptual example of how you handle this in a custom tool-calling loop:

async function executeToolWithBackoff(tool, args, maxRetries = 3) {
  let attempts = 0;
  
  while (attempts < maxRetries) {
    try {
      const result = await tool.invoke(args);
      return result;
    } catch (error) {
      if (error.response && error.response.status === 429) {
        // Truto normalizes these headers for all upstream APIs
        const resetTime = error.response.headers['ratelimit-reset'];
        const delayMs = resetTime ? (parseInt(resetTime) * 1000) - Date.now() : 2000;
        
        console.log(`Rate limited by Slido. Waiting ${delayMs}ms before retry...`);
        await new Promise(resolve => setTimeout(resolve, Math.max(delayMs, 1000)));
        attempts++;
      } else {
        throw error;
      }
    }
  }
  throw new Error("Max retries exceeded for Slido tool execution");
}

By building this resilience into your framework, the agent can gracefully navigate Slido's API constraints without crashing the run.

Workflows in Action

Here are concrete examples of how an AI agent uses these tools in production to automate meeting intelligence and RevOps tasks.

Scenario 1: Post-All-Hands Sentiment and Q&A Triage

Internal communications teams spend hours manually exporting Q&A data, reading through anonymous submissions, and categorizing feedback after large corporate events.

"Analyze the Q&A from yesterday's Global All-Hands event. Export the questions, filter out anything with less than 5 upvotes, and categorize the remaining questions into 'HR/Benefits', 'Product Roadmap', or 'General Leadership'. Provide a summary of the top concerns."

Step-by-step execution:

1. The agent calls create_a_slido_exports_event with a date filter to locate the exact event_id for yesterday's Global All-Hands.
2. The agent extracts the ID and calls get_single_slido_export_by_id until the event list is returned.
3. Using the specific event ID, it calls create_a_slido_exports_question.
4. It loops get_single_slido_export_by_id until the question data is ready.
5. The agent ingests the payload, executes its own internal filtering logic (dropping items with < 5 upvotes), applies LLM classification to categorize the remaining text, and generates the final markdown summary for the user.

Result: The user gets a clean, structured intelligence brief categorizing employee sentiment based on real, upvoted data - without logging into the Slido dashboard or fighting with CSV files.

Scenario 2: Engagement Scoring and Participant Sync

Marketing teams use webinars to generate leads, but figuring out which attendees were actually engaged (e.g., answering polls, submitting questions) requires manual cross-referencing between Slido and a CRM.

"Export the participant list and the poll vote values for the 'Q4 Partner Enablement' webinar. Cross-reference the participants with their vote data, and give me a list of the top 10 most engaged email addresses based on how many polls they answered."

Step-by-step execution:

1. The agent searches for the event to grab the event_id.
2. It fires off two parallel asynchronous jobs: create_a_slido_exports_participant and a separate tool for poll vote values (if available, or create_a_slido_exports_poll).
3. The agent tracks both export_id values, calling get_single_slido_export_by_id until both datasets are fully compiled.
4. The LLM acts as a data analyst, joining the participant array (which contains emails) with the poll vote array (using participant_id as the foreign key).
5. It counts the interactions per participant, sorts them, and returns the top 10 emails.

Result: RevOps gets a highly qualified list of engaged prospects ready for immediate follow-up in HubSpot or Salesforce, completely bypassing manual VLOOKUPs in Excel.

The Strategic Value of a Unified Tool Layer

Building an AI agent is a test of your infrastructure's resilience. If you push the complexity of Slido's asynchronous export architecture and strict rate limiting directly into your LLM prompts, the model will hallucinate, execution will time out, and your workflows will fail silently.

By leveraging a unified tool layer, your agent sees a stable, predictable set of functions. It requests an export, receives a tracking ID, and checks the status until the data arrives. Truto handles the underlying authentication, normalization, and structural translation, allowing your engineering team to focus on building better agent logic rather than maintaining bespoke API wrappers.