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External LLM Guide

This guide explains how to configure and use the External LLM feature in Home Agent, which enables a dual-LLM strategy where a fast primary LLM can delegate complex queries to a more powerful external LLM.

Table of Contents

• Overview
• Dual-LLM Strategy
• Use Cases
• Configuration
• How It Works
• Best Practices
• Examples
• Cost Management
• Troubleshooting

Overview

The External LLM feature provides a powerful dual-LLM architecture that combines the efficiency of a fast local model with the capabilities of a powerful cloud model. The primary LLM handles tool execution and most conversations, but can delegate complex analysis or reasoning tasks to an external LLM when needed.

Key Benefits

• Cost Efficiency: Use inexpensive local models for most tasks, only calling expensive models when necessary
• Performance: Fast response times for simple queries while still having access to advanced capabilities
• Flexibility: Choose different models for different strengths (speed vs. capability)
• Transparency: Primary LLM explicitly decides when to use external LLM, making the decision process clear

Dual-LLM Strategy

Architecture

User Query
    ↓
Primary LLM (e.g., llama2, gpt-4o-mini)
    ↓
    ├─→ Simple query → Direct response
    │
    ├─→ Tool execution → ha_control, ha_query, custom tools
    │
    └─→ Complex analysis → query_external_llm
                               ↓
                        External LLM (e.g., gpt-4o, claude-3-5-sonnet)
                               ↓
                        Detailed response
                               ↓
                        Primary LLM → Formatted response to user

Design Philosophy

1. Primary LLM: Fast, efficient, handles tool calling and direct queries
2. External LLM: Powerful, used only when needed for complex reasoning
3. Explicit Delegation: Primary LLM chooses when to use external LLM
4. Context Isolation: External LLM only receives what primary LLM sends (not full conversation history)

Use Cases

When to Use External LLM

The external LLM is ideal for:

1. Complex Analysis

Example: Analyzing energy usage patterns

User: "Analyze my energy consumption over the past week and suggest optimizations"

Primary LLM:
  1. Calls ha_query to get energy data
  2. Calls query_external_llm with data and analysis request
  3. External LLM performs detailed analysis
  4. Primary LLM formats and presents recommendations

2. Detailed Explanations

Example: Understanding home automation concepts

User: "Explain how I can optimize my heating schedule for maximum efficiency"

Primary LLM:
  1. Recognizes this needs detailed explanation
  2. Calls query_external_llm for comprehensive explanation
  3. External LLM provides detailed breakdown with examples
  4. Primary LLM presents to user

3. Multi-Step Reasoning

Example: Planning automations

User: "Help me create an automation that adjusts lights based on time of day and occupancy"

Primary LLM:
  1. Calls query_external_llm for automation design advice
  2. External LLM suggests logic and structure
  3. Primary LLM uses response to guide user through implementation

4. Creative Tasks

Example: Naming and organizing

User: "Suggest creative names for my smart home scenes and explain when to use each"

Primary LLM:
  1. Calls query_external_llm for creative suggestions
  2. External LLM generates creative, descriptive names
  3. Primary LLM presents organized list

When NOT to Use External LLM

The primary LLM should handle directly:

• Simple queries: "Turn on the lights", "What's the temperature?"
• Tool execution: Controlling devices, querying sensors
• Quick lookups: Current states, recent history
• Direct commands: Clear, single-step actions

Configuration

Enabling External LLM

Configure via Home Assistant UI or configuration.yaml:

UI Configuration

1. Go to Settings > Devices & Services > Home Agent
2. Click Configure
3. Select External LLM from the menu
4. Fill in the configuration:
   • Enable External LLM: Toggle on
   • Base URL: External LLM API endpoint
   • API Key: Your API key
   • Model: Model name
   • Tool Description: When primary LLM should use it (optional)
   • Temperature: Creativity level (optional, default: 0.8)
   • Max Tokens: Maximum response length (optional, default: 1000)

YAML Configuration (Alternative)

home_agent:
  external_llm_enabled: true
  external_llm_base_url: "https://api.openai.com/v1"
  external_llm_api_key: !secret openai_api_key
  external_llm_model: "gpt-4o"
  external_llm_temperature: 0.8
  external_llm_max_tokens: 1000
  external_llm_tool_description: |
    Use this tool for complex analysis, detailed explanations, or tasks
    requiring advanced reasoning. Provide the specific question as the
    prompt and include relevant context data.

secrets.yaml:

openai_api_key: sk-your-api-key-here

Configuration Options

Choosing an External LLM

Popular options:

OpenAI GPT-4

external_llm_base_url: "https://api.openai.com/v1"
external_llm_model: "gpt-4o"

Pros: Excellent reasoning, good at analysis, reliable Cons: Higher cost, requires internet

Anthropic Claude

external_llm_base_url: "https://api.anthropic.com/v1"  # Via OpenAI-compatible proxy
external_llm_model: "claude-3-5-sonnet-20241022"

Pros: Excellent at analysis and explanation, large context window Cons: Requires compatible proxy, higher cost

OpenRouter (Multiple Models)

external_llm_base_url: "https://openrouter.ai/api/v1"
external_llm_model: "anthropic/claude-3.5-sonnet"  # or other models

Pros: Access to many models, flexible pricing Cons: Requires OpenRouter account

Self-Hosted Models

external_llm_base_url: "http://localhost:11434/v1"  # Ollama
external_llm_model: "llama2:70b"  # Larger model than primary

Pros: Free, private, no internet required Cons: Requires powerful hardware, may be slower

How It Works

The query_external_llm Tool

When you enable External LLM, the primary LLM gains access to a new tool:

{
  "name": "query_external_llm",
  "description": "Use for complex analysis, detailed explanations, or advanced reasoning",
  "parameters": {
    "prompt": "The question or task for the external LLM (required)",
    "context": "Additional context data (optional)"
  }
}

Conversation Flow

1. User sends query to Home Agent
2. Primary LLM processes the query
3. Primary LLM decides if external LLM is needed
4. If needed:
   • Primary LLM constructs prompt parameter
   • Primary LLM includes relevant context (entity data, tool results, etc.)
   • Primary LLM calls query_external_llm
5. External LLM receives:
   • The prompt (question/task)
   • The context (if provided)
   • NOT the full conversation history (for efficiency)
6. External LLM responds with analysis/explanation
7. Primary LLM formats the response for the user

Context Handling

Important: The external LLM does NOT automatically receive:

• Full conversation history
• Previous tool calls
• Original user query

The primary LLM must explicitly include all necessary information in the prompt and context parameters.

Example Context Passing

# What primary LLM sends to external LLM
{
  "prompt": "Analyze this energy usage data and suggest optimizations",
  "context": {
    "energy_data": {
      "sensor.energy_usage": [
        {"time": "2024-01-01T00:00:00", "value": 150},
        {"time": "2024-01-01T01:00:00", "value": 160},
        ...
      ]
    },
    "current_rate": "$0.12 per kWh",
    "peak_hours": "4pm-9pm"
  }
}

Best Practices

1. Customize the Tool Description

Help the primary LLM understand when to use the external LLM:

external_llm_tool_description: |
  Delegate to this tool when:
  - User asks for detailed analysis or recommendations
  - Task requires multi-step reasoning or planning
  - Creative suggestions or explanations are needed
  - Complex data interpretation is required

  Do NOT use for:
  - Simple device control
  - Quick status queries
  - Direct tool execution

2. Choose Complementary Models

Select models with different strengths:

Primary LLM: Fast, efficient, good at tool calling

• gpt-4o-mini
• llama2:13b
• mistral

External LLM: Powerful, good at reasoning

• gpt-4o
• claude-3-5-sonnet
• llama2:70b

3. Optimize Prompts

The primary LLM should provide clear, focused prompts:

❌ Too vague:

{
  "prompt": "Help with energy"
}

✅ Clear and specific:

{
  "prompt": "Analyze the energy consumption data and identify: 1) Peak usage times, 2) Unusual spikes, 3) Optimization opportunities",
  "context": { ...energy data... }
}

4. Monitor Costs

Track external LLM usage to manage costs:

automation:
  - alias: "Log External LLM Usage"
    trigger:
      - platform: event
        event_type: home_agent.tool.executed
        event_data:
          tool_name: query_external_llm
    action:
      - service: counter.increment
        target:
          entity_id: counter.external_llm_calls
      - service: notify.admin
        data:
          message: "External LLM called: {{ trigger.event.data.parameters.prompt[:50] }}..."

5. Set Appropriate Limits

Configure tool call limits to prevent excessive costs:

home_agent:
  tools_max_calls_per_turn: 3  # Limit tool calls per conversation turn
  tools_timeout: 30  # Timeout for external LLM calls

6. Test with Different Temperatures

Adjust temperature based on task:

• Analysis (0.3-0.5): More focused, deterministic
• Explanations (0.7-0.8): Balanced creativity and accuracy
• Creative tasks (0.9-1.2): More creative and varied

Examples

Example 1: Energy Analysis

User: "Analyze my energy usage this week and suggest ways to save money"

Primary LLM:

1. Calls ha_query to get energy sensor data
2. Receives data for past week
3. Calls query_external_llm:

   {
     "prompt": "Analyze this energy consumption data and provide specific recommendations to reduce costs",
     "context": {
       "energy_data": [...],
       "electricity_rate": "$0.12/kWh",
       "peak_rate_hours": "4pm-9pm at $0.18/kWh"
     }
   }
   

4. External LLM responds with detailed analysis
5. Primary LLM formats response: "Based on your energy usage, here are three ways to save money: ..."

Example 2: Automation Design

User: "Help me create a smart lighting automation for my home office"

Primary LLM:

1. Calls query_external_llm:

   {
     "prompt": "Design a smart lighting automation for a home office that considers: time of day, occupancy, natural light, and work/focus modes. Provide specific logic and triggers.",
     "context": {
       "available_lights": ["light.desk", "light.overhead", "light.ambient"],
       "sensors": ["binary_sensor.office_occupied", "sensor.office_light_level"],
       "user_preferences": "Prefers warm lighting for video calls, bright light for detailed work"
     }
   }
   

2. External LLM provides detailed automation design
3. Primary LLM guides user through implementation

Example 3: Climate Optimization

User: "Optimize my heating schedule for maximum comfort and efficiency"

Primary LLM:

1. Calls ha_query to get climate history and preferences
2. Calls query_external_llm:

   {
     "prompt": "Create an optimized heating schedule that balances comfort and efficiency, considering occupancy patterns and outdoor temperature variations",
     "context": {
       "current_schedule": [...],
       "occupancy_pattern": "Home 6pm-8am weekdays, all day weekends",
       "temperature_preferences": "68°F when home, 62°F when away",
       "historical_usage": [...],
       "outdoor_temps": [...]
     }
   }
   

3. External LLM creates optimized schedule
4. Primary LLM presents schedule and helps implement it

Cost Management

Estimating Costs

External LLM costs depend on:

• Model pricing (per 1K tokens)
• Frequency of use
• Response length (max_tokens)
• Context size

Example calculation (GPT-4o):

• Input: $2.50 per 1M tokens
• Output: $10.00 per 1M tokens
• Average query: ~500 input tokens, ~300 output tokens
• Cost per query: ~$0.004

Cost Optimization Tips

1. Use external LLM selectively

   • Let primary LLM handle simple tasks
   • Only delegate truly complex queries

2. Limit max_tokens

   external_llm_max_tokens: 500  # Shorter, more focused responses
   

3. Reduce context size

   • Only include essential data in context
   • Summarize large datasets before passing

4. Choose cost-effective models

   • OpenAI GPT-4o-mini instead of GPT-4o
   • Self-hosted models for no per-query cost

5. Set usage limits

   tools_max_calls_per_turn: 2  # Limit tools per conversation
   

6. Monitor and alert

   automation:
     - alias: "External LLM Usage Alert"
       trigger:
         - platform: numeric_state
           entity_id: counter.external_llm_calls_today
           above: 50
       action:
         - service: notify.admin
           data:
             message: "High external LLM usage today: {{ states('counter.external_llm_calls_today') }} calls"
   

Troubleshooting

External LLM Tool Not Available

Symptoms: Primary LLM doesn't call query_external_llm

Solutions:

1. Verify external_llm_enabled: true in configuration
2. Restart Home Assistant after configuration changes
3. Check logs for registration errors
4. Verify tool appears in tool list (Developer Tools > Services)

Authentication Errors

Symptoms: "External LLM authentication failed" errors

Solutions:

1. Verify API key is correct and not expired
2. Check API key has sufficient permissions/credits
3. Ensure API key is properly set in secrets.yaml
4. Test API key directly with curl:

   curl https://api.openai.com/v1/models \
     -H "Authorization: Bearer YOUR_API_KEY"
   

Timeout Errors

Symptoms: "External LLM request timed out" errors

Solutions:

1. Increase timeout:

   tools_timeout: 60  # Increase to 60 seconds
   

2. Reduce max_tokens for faster responses
3. Check network connectivity
4. Verify external LLM service is operational

High Costs

Symptoms: Unexpected API charges

Solutions:

1. Check usage metrics in API provider dashboard
2. Review logs for excessive calls
3. Reduce max_tokens
4. Adjust tool description to be more selective
5. Implement usage limits and monitoring

Poor Response Quality

Symptoms: External LLM responses are not helpful

Solutions:

1. Improve prompts: Primary LLM may not be providing enough context
2. Adjust temperature: Lower for more focused, higher for creative
3. Increase max_tokens: May be cutting off responses
4. Try different model: Some models excel at different tasks
5. Customize tool description: Help primary LLM understand when/how to use it

Context Not Being Passed

Symptoms: External LLM seems unaware of relevant information

Remember: External LLM only receives what's in prompt and context parameters

Solutions:

1. Check primary LLM is including context in tool call
2. Verify context data is being retrieved correctly
3. Review tool execution logs to see what's being sent
4. Adjust primary LLM's system prompt to encourage context passing

Debug Logging

Enable detailed logging for troubleshooting:

logger:
  logs:
    custom_components.home_agent: debug
    custom_components.home_agent.tools.external_llm: debug

Check logs for:

• Tool registration
• API calls and responses
• Error messages
• Token usage
• Response times

Further Reading

• Custom Tools Guide - Learn about custom tool creation
• Project Specification - Technical details and architecture
• Home Agent README - General setup and usage
• OpenAI API Documentation
• Anthropic API Documentation