LLM Integration

Use AI for text classification and generation within your workflows.

Overview

SideButton integrates with OpenAI (and Anthropic) APIs to enable:

• Classification — Categorize text into predefined buckets
• Generation — Create text based on prompts

Setup

API Key

Set your OpenAI API key:

Option 1: Environment Variable

export OPENAI_API_KEY=sk-your-key-here
npx @sidebutton/server serve

Option 2: Settings (Dashboard)

1. Open localhost:9876
2. Go to Settings
3. Add an env context:
   • Name: OPENAI_API_KEY
   • Value: sk-your-key-here

Supported Providers

Provider	Environment Variable	Status
OpenAI	OPENAI_API_KEY	✅ Supported
Anthropic	ANTHROPIC_API_KEY	✅ Supported

LLM Steps

llm.classify

Categorize text into one of several categories:

- type: llm.classify
  input: "{{email_content}}"
  categories:
    - urgent
    - important
    - normal
    - spam
  as: priority

Parameters:

Parameter	Type	Required	Description
input	string	Yes	Text to classify
categories	array	Yes	List of possible categories
as	string	Yes	Variable for result

Output: One of the category strings (e.g., "urgent")

Use cases:

• Email triage
• Support ticket routing
• Content moderation
• Lead qualification

llm.generate

Generate text based on a prompt:

- type: llm.generate
  prompt: |
    Summarize this article in 3 bullet points:

    {{article_content}}
  as: summary

Parameters:

Parameter	Type	Required	Description
prompt	string	Yes	Prompt for the LLM
as	string	Yes	Variable for result

Output: Generated text string

Use cases:

• Summarization
• Reply drafting
• Content creation
• Code explanation

User Contexts

Customize AI behavior for specific workflows using LLM Contexts in Settings.

Creating an LLM Context

1. Go to Settings → User Contexts
2. Click Add Context
3. Choose type: LLM
4. Fill in:
   • Industry: Match by category domain (e.g., Sales)
   • Domain: Match by allowed_domains (e.g., linkedin.com)
   • Context: Instructions for the AI

Example Context

Industry: Sales
Domain: linkedin.com
Context: |
  You are a professional sales assistant.
  Keep responses concise and business-appropriate.
  Focus on building relationships, not hard selling.
  Never use emojis or overly casual language.

How Contexts Are Applied

When an LLM step runs:

1. The system checks workflow's category.domain and policies.allowed_domains
2. Matching contexts are prepended to the prompt
3. The combined prompt is sent to the API

Examples

Email Priority Classification

id: classify_email
title: "Classify Email Priority"

steps:
  - type: browser.extract
    selector: ".email-body"
    as: email

  - type: llm.classify
    input: "{{email}}"
    categories:
      - urgent_action_needed
      - important_but_not_urgent
      - informational
      - spam_or_marketing
    as: priority

  - type: control.if
    condition: "{{priority}} == 'urgent_action_needed'"
    then:
      - type: browser.click
        selector: ".flag-important"

Article Summarization

id: summarize_article
title: "Summarize Article"

steps:
  - type: browser.extract
    selector: "article"
    as: content

  - type: llm.generate
    prompt: |
      Summarize this article:

      {{content}}

      Format your response as:
      - HEADLINE: (one line summary)
      - KEY POINTS: (3 bullet points)
      - TAKEAWAY: (one sentence)
    as: summary

  - type: control.stop
    message: "{{summary}}"

Draft Reply with Context

id: draft_linkedin_reply
title: "Draft LinkedIn Reply"

category:
  domain: sales

policies:
  allowed_domains:
    - "*.linkedin.com"

steps:
  - type: browser.extractAll
    selector: ".msg-s-message-group"
    as: conversation

  - type: llm.generate
    prompt: |
      Draft a reply to this LinkedIn conversation:

      {{conversation}}

      The reply should be professional and move the conversation forward.
    as: reply

  - type: browser.type
    selector: ".msg-form__contenteditable"
    text: "{{reply}}"

Sentiment Analysis

id: analyze_sentiment
title: "Analyze Sentiment"

steps:
  - type: browser.extractAll
    selector: ".review-text"
    as: reviews

  - type: llm.classify
    input: "{{reviews}}"
    categories:
      - very_positive
      - positive
      - neutral
      - negative
      - very_negative
    as: sentiment

  - type: control.stop
    message: "Overall sentiment: {{sentiment}}"

Best Practices

1. Be Specific in Prompts

# Bad - vague
prompt: "Write something about {{topic}}"

# Good - specific
prompt: |
  Write a 100-word summary of {{topic}}.
  Include:
  - Main definition
  - Key applications
  - One interesting fact

2. Use Categories Thoughtfully

# Bad - overlapping categories
categories:
  - good
  - positive
  - great

# Good - distinct categories
categories:
  - positive
  - neutral
  - negative

3. Provide Context

prompt: |
  You are analyzing customer support tickets.

  Ticket: {{ticket_content}}

  Classify the urgency and suggest a response.

4. Handle Failures Gracefully

- type: control.retry
  max_attempts: 2
  delay_ms: 1000
  steps:
    - type: llm.generate
      prompt: "{{input}}"
      as: result

Troubleshooting

"OpenAI API key not configured"

Set the OPENAI_API_KEY environment variable before starting the server.

Rate limiting errors

• Add delays between LLM calls
• Use control.retry with exponential backoff
• Consider batching requests

Unexpected output

• Check your prompt is clear
• Use few-shot examples
• Add output format instructions

High latency

• Keep prompts concise
• Extract only needed content
• Cache results if appropriate

API Usage & Costs

LLM steps make API calls that may incur costs:

Step Type	Typical Usage
llm.classify	~100-500 tokens
llm.generate	~500-2000 tokens

Monitor your API usage at OpenAI Dashboard.

Next Steps

• Workflow Examples — More LLM workflow examples
• Step Types — All available steps
• User Contexts — Customize AI behavior