Configuring Agents

Define and customize agents using JSON: set up personality traits, emotional parameters, memory files, LLM preferences, and voice profiles — all without writing Rust.

📄 Agent Config Overview

Agents are fully defined in a single config.json file.

Example:

{
  "agents": [
    {
      "id": "velma",
      "personality": {
        "curiosity": 0.7,
        "trust": 0.4,
        "anger": 0.1
      },
      "goals": [
        {
          "id": "explore_ruins",
          "priority": 0.8
        }
      ],
      "memories": [
        {
          "content": "Player helped me escape the cave.",
          "tags": ["trust"],
          "importance": 0.9
        }
      ],
      "llm_profile": {
        "preferred_provider": "groq"
      }
    }
  ]
}

🧠 Personality

Set the emotional baseline of your agent:

"personality": {
  "curiosity": 0.6,
  "anger": 0.2,
  "happiness": 0.8
}

  • Values range 0.0 to 1.0

  • These affect tone, memory bias, and behavioral tendencies

🎯 Goals

Each goal is:

{
  "id": "explore_cave",
  "priority": 0.9
}

  • Goals can be reprioritized during runtime

  • The agent’s actions aim to resolve the top goal

🗃️ Memory"memories": [

  {
    "content": "Marcus gave me the map.",
    "tags": ["trust", "plot_critical"],
    "importance": 0.7
  }
]

  • Importance influences recall likelihood

  • Tags link to emotions and behavior trees

🔁 Inference Settings

LLM routing is configured per-agent or globally:

"llm_profile": {
  "preferred_provider": "openai",
  "fallbacks": ["groq", "local"]
}

  • You can set provider priority or override at runtime

  • Local models supported via llm_service.rs

💡 Tips

  • Use separate JSON files per agent and merge at build time

  • Goals and emotions can be modified by gameplay triggers

  • For testing, you can simulate config overrides via CLI

🔗 Related Pages

  • 5. Inference System

  • 8. API Reference → config.rs

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