# Decision Framer — Criteria, Ranking & Recommendation (`physealabs/decision-framer`) Actor Frame a choice into decision criteria, ranked candidates, a practical recommendation, and the evidence that would change it. - **URL**: https://apify.com/physealabs/decision-framer.md - **Developed by:** [jay casey](https://apify.com/physealabs) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage ## What's an Apify Actor? Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. - **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use). - **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`. - **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`. - **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`. - **Any other language** — the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Decision Framer — Criteria, Ranking & Recommendation Frame a choice into decision criteria, ranked candidates, a practical recommendation, and the evidence that would change it. **Powered by Physea's Liminality reasoning engine.** ### 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 One clean dataset row per input item, plus a downloadable `RESULTS.md` report. Failed or unavailable analysis is returned honestly as `status: "error"`; unsuccessful items are never charged. ```json { "index": 1, "status": "success", "recommendation": "Structured analysis appears here" } ``` ### Pricing **$0.03 per successful decision framed.** Errors are free. Batch any number of items in one run. # Actor input Schema ## `items` (type: `array`): Process one or more items in a single run. Each item produces exactly one dataset row. ## Actor input object example ```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" ] } ] } ``` # Actor output Schema ## `results` (type: `string`): One structured result row per input item. ## `markdown` (type: `string`): Readable report containing all item results. ## `summary` (type: `string`): Processed, succeeded, and error counts. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "items": [ { "decision": "Should our SaaS company build or buy customer support automation?", "context": "12-person company; $20k budget; launch in 8 weeks; two engineers; EU customers.", "candidates": [ "Build in-house", "Buy a managed platform" ], "priorities": [ "Launch speed", "Total cost", "EU data handling", "Maintenance burden" ] } ] }; // Run the Actor and wait for it to finish const run = await client.actor("physealabs/decision-framer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "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", ], }] } # Run the Actor and wait for it to finish run = client.actor("physealabs/decision-framer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "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" ] } ] }' | apify call physealabs/decision-framer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,physealabs/decision-framer" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/jt3FMPFRvjFEY69RU/builds/HV6HqCLOWSAdpz4N1/openapi.json