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Raycast AI: Mac Productivity with AI
PROGRAMMING LANGUAGES Jan. 25, 2026, 11:30 p.m.

Raycast AI: Mac Productivity with AI

Raycast has become the go‑to launcher for many Mac power users, and the newest addition—Raycast AI—takes productivity to a whole new level. By weaving large‑language‑model capabilities directly into the Raycast workflow, you can automate repetitive tasks, generate code snippets on the fly, and even draft emails without leaving your keyboard. In this article we’ll explore how Raycast AI works, set it up, and dive into practical examples that showcase its real‑world impact.

Getting Started with Raycast AI

First, make sure you’re running Raycast version 1.70 or newer; the AI features are hidden behind the “Beta” toggle in Settings → Extensions. Enable the toggle, and you’ll see a brand‑new “AI” section in the command palette.

Raycast AI relies on an external LLM provider (OpenAI, Anthropic, or a self‑hosted model). To get started, open Preferences → AI Settings and paste your API key. The UI will validate the key and let you choose a default model—GPT‑4o is a solid default for most users.

Once the key is saved, you can invoke AI directly by typing ai in the Raycast launcher. A tiny prompt box appears, ready for you to ask questions, generate text, or run a custom script.

Core Features at a Glance

  • Instant text generation: Summarize articles, draft replies, or brainstorm ideas.
  • Code assistance: Generate snippets, refactor code, or explain complex logic.
  • Custom AI commands: Combine Raycast’s script commands with AI prompts for bespoke workflows.
  • Context awareness: Raycast can feed the contents of the frontmost window or selected text to the model.

These features are not isolated; they blend seamlessly with Raycast’s existing extensions. For example, you can pipe the output of a git status script into AI and ask it to create a concise commit message.

Why Raycast AI Beats Standalone Chat Apps

Traditional chat‑based AI tools force you to switch contexts—copying text, opening a browser, then pasting the result back. Raycast AI lives in the same keyboard‑first environment you already use for launching apps, searching files, and running scripts. The result is a frictionless loop where you can stay in the flow state without ever reaching for the mouse.

Building Your First Custom AI Command

Raycast’s extensibility shines when you combine script commands with AI. Let’s create a simple “Summarize Clipboard” command that reads whatever you’ve copied, sends it to the LLM, and returns a concise summary.

#!/usr/bin/env python3
import os
import subprocess
import json
import sys
import requests

# 1️⃣ Grab clipboard contents
clipboard = subprocess.check_output(
    ["pbpaste"], universal_newlines=True
).strip()

if not clipboard:
    print("❌ Clipboard is empty.")
    sys.exit(1)

# 2️⃣ Prepare the request payload
api_key = os.getenv("OPENAI_API_KEY")
if not api_key:
    print("❌ Set OPENAI_API_KEY in your environment.")
    sys.exit(1)

payload = {
    "model": "gpt-4o-mini",
    "messages": [
        {"role": "system", "content": "You are a concise summarizer."},
        {"role": "user", "content": f"Summarize the following text in 3 sentences:\n\n{clipboard}"}
    ],
    "temperature": 0.3,
}

# 3️⃣ Call the OpenAI API
response = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers={"Authorization": f"Bearer {api_key}"},
    json=payload,
)

if response.status_code != 200:
    print(f"❌ API error: {response.text}")
    sys.exit(1)

summary = response.json()["choices"][0]["message"]["content"]
print(summary)

Save this script as summarize_clipboard.py, make it executable (chmod +x), and add a new Raycast Script Command pointing to it. Now, whenever you copy a long article or a meeting transcript, hit ⌘+Space, type “Summarize Clipboard,” and receive a crisp three‑sentence recap instantly.

Pro tip: Store your API key in ~/.zshrc (or the shell you use) as export OPENAI_API_KEY="your_key". Raycast inherits the environment, keeping your scripts clean and secure.

Real‑World Use Cases

1️⃣ Email Drafting on the Fly

Imagine you need to reply to a client asking for a project update. With Raycast AI, you can highlight the email thread, invoke the AI command “Draft Reply,” and let the model generate a polite, professional response. The AI can even insert placeholders for dates or figures you’ll fill in later.

2️⃣ Code Refactoring Assistant

Developers can save countless minutes by asking Raycast AI to refactor a function. Highlight the code, run the “Refactor with AI” command, and specify the desired style (e.g., “use list comprehensions” or “convert to async”). The model returns the updated snippet, which you can paste directly back into your editor.

def fetch_data(urls):
    results = []
    for u in urls:
        r = requests.get(u)
        if r.status_code == 200:
            results.append(r.json())
    return results

Prompt: “Rewrite this function using async/await and aiohttp.”

import aiohttp
import asyncio

async def fetch_one(session, url):
    async with session.get(url) as resp:
        return await resp.json() if resp.status == 200 else None

async def fetch_data(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [fetch_one(session, u) for u in urls]
        return [r for r in await asyncio.gather(*tasks) if r]

3️⃣ Meeting Notes Summarizer

After a Zoom call, you often have a raw transcript that’s hard to skim. Use a Raycast AI command that reads a .txt file, extracts action items, and formats them as a markdown checklist. The following script demonstrates the pattern:

#!/usr/bin/env python3
import sys, json, os, requests

if len(sys.argv) != 2:
    print("Usage: notes_summary.py path/to/transcript.txt")
    sys.exit(1)

path = sys.argv[1]
if not os.path.isfile(path):
    print("File not found.")
    sys.exit(1)

with open(path, "r") as f:
    transcript = f.read()

payload = {
    "model": "gpt-4o-mini",
    "messages": [
        {"role": "system", "content": "Extract action items from meeting notes and format them as markdown checkboxes."},
        {"role": "user", "content": transcript}
    ],
    "temperature": 0.2,
}

resp = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers={"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"},
    json=payload,
)

if resp.status_code != 200:
    print("API error")
    sys.exit(1)

print(resp.json()["choices"][0]["message"]["content"])

Bind this script to a Raycast command that accepts a file argument, and you’ll have a one‑click workflow for turning noisy transcripts into clear, actionable lists.

Pro Tips for Power Users

  • Cache frequent prompts. Wrap your AI calls in a simple JSON cache keyed by the prompt hash. This avoids redundant API calls for identical requests.
  • Leverage Raycast’s “Context” feature. When you invoke an AI command from a specific app, Raycast can automatically pass the app’s title or selected text, eliminating manual copy‑paste steps.
  • Fine‑tune temperature. Low temperature (< 0.3) yields deterministic outputs—ideal for code generation. Higher temperature (0.7‑0.9) produces creative prose, perfect for brainstorming.
  • Batch multiple prompts. Use Raycast’s Run in Background option to fire off several AI requests concurrently, then aggregate the results.

Remember: AI is a collaborator, not a replacement. Always review generated code or text before committing it to production.

Performance & Reliability Considerations

Because Raycast AI communicates over the internet, network latency can affect response times. Most users notice sub‑second replies with OpenAI’s hosted endpoints, but you can mitigate delays by:

  1. Choosing a model hosted in a region close to your location.
  2. Enabling “Streaming” mode in the AI Settings, which streams partial responses as they arrive.
  3. Setting a reasonable max_tokens limit to keep payloads small.

For mission‑critical workflows (e.g., CI/CD pipelines), consider a self‑hosted LLM behind your corporate firewall. Raycast AI supports custom endpoints, so you can point the extension to an internal server that respects your security policies.

Security & Privacy Best Practices

When you send data to an external LLM, you’re trusting the provider with potentially sensitive information. Here are steps to keep your data safe:

  • Use organization‑level API keys that have usage limits and audit logs.
  • Enable “Data Retention Off” in the provider’s dashboard if you don’t want your prompts stored.
  • Never embed raw credentials in scripts; rely on environment variables or macOS Keychain integration (Raycast can fetch secrets securely).

Raycast also offers a “Local‑Only” mode that disables network calls and falls back to a stub response. This is useful for testing UI flows without exposing data.

Extending Raycast AI with Community Extensions

The Raycast Store hosts a growing collection of community‑built AI extensions. Some noteworthy ones include:

  • AI‑Git‑Commit – Generates conventional commit messages from git diff output.
  • Snippet‑Generator – Turns natural‑language descriptions into ready‑to‑paste code blocks for multiple languages.
  • Idea‑Board – Captures brainstorming prompts and stores them in Notion via AI‑enhanced formatting.

Installing these extensions is as simple as clicking “Install” in the Raycast Store, then configuring your API key once. They serve as excellent starting points if you’re new to building AI‑powered scripts.

Putting It All Together: A Sample Daily Workflow

Let’s walk through a typical morning routine that showcases Raycast AI’s versatility:

  1. Check inbox. Highlight a new client email, press ⌘+Space, run “Draft Reply.” Review and send.
  2. Review pull requests. Select the PR description, invoke “Summarize Changes,” and get a quick bullet‑point overview.
  3. Plan the day. Open your calendar, copy the agenda, and ask “Create a prioritized to‑do list.” Raycast AI returns a markdown checklist you paste into Todoist.
  4. Write code. Describe a function you need (“Fetch JSON from a list of URLs concurrently”), run “Generate Code,” and copy the result into your editor.
  5. Wrap up meeting notes. After a call, save the transcript, run “Meeting Summary,” and drop the markdown into Confluence.

This loop keeps you in the same keyboard‑first environment, reducing context switches and boosting overall throughput.

Conclusion

Raycast AI transforms the Mac launcher from a simple app switcher into a powerful AI‑augmented assistant. By integrating large‑language‑model capabilities directly into your workflow, you can automate mundane tasks, generate high‑quality code, and stay focused without ever leaving the keyboard. Whether you’re a developer seeking instant refactoring help or a knowledge worker looking to summarize endless documents, Raycast AI provides a flexible, secure, and lightning‑fast solution. Dive in, experiment with custom commands, and let the AI do the heavy lifting while you stay in the flow.

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