# Example: Decision Framer: Compare Options and Evidence Gaps

**Use case:** 

Run Decision Framer: Compare Options and Evidence Gaps with a small, ready-to-copy input. Supply a decision, candidate options, and priorities. Get comparison criteria, a ranking, an analysis, and the evidence that could change it. Unverified candidates remain clearly labeled.

## Input

```json
{
  "items": [
    {
      "decision": "Should our SaaS company build or buy customer support automation?",
      "context": "12-person company; $20k budget; launch in 8 weeks; two engineers; EU customers.",
      "candidates": [
        "Build in-house",
        "Buy a managed platform"
      ],
      "priorities": [
        "Launch speed",
        "Total cost",
        "EU data handling",
        "Maintenance burden"
      ]
    }
  ]
}
```

## Output

```json
{
  "index": {
    "label": "Index",
    "format": "text"
  },
  "status": {
    "label": "Status",
    "format": "text"
  },
  "decision": {
    "label": "Decision",
    "format": "text"
  },
  "criteria": {
    "label": "Criteria",
    "format": "array"
  },
  "rankedCandidates": {
    "label": "Rankedcandidates",
    "format": "array"
  },
  "recommendation": {
    "label": "Recommendation",
    "format": "text"
  },
  "whatWouldFlipIt": {
    "label": "Whatwouldflipit",
    "format": "array"
  },
  "analysis": {
    "label": "Analysis",
    "format": "text"
  },
  "confidence": {
    "label": "Confidence",
    "format": "text"
  },
  "error": {
    "label": "Error",
    "format": "text"
  }
}
```

## About this Actor

This example demonstrates how to use [AI Decision Matrix & Option Comparison](https://apify.com/physealabs/decision-framer.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/physealabs/decision-framer.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/physealabs/decision-framer.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).