Struct AgentProfile 

pub struct AgentProfile {

    pub prompt: Option<String>,
    pub style: Option<String>,
    pub temperature: Option<f32>,
    pub model_provider: Option<String>,
    pub model_name: Option<String>,
    pub allowed_tools: Option<Vec<String>>,
    pub denied_tools: Option<Vec<String>>,
    pub memory_k: usize,
    pub top_p: f32,
    pub max_context_tokens: Option<usize>,
    pub enable_graph: bool,
    pub graph_memory: bool,
    pub graph_depth: usize,
    pub graph_weight: f32,
    pub auto_graph: bool,
    pub graph_threshold: f32,
    pub graph_steering: bool,
    pub fast_reasoning: bool,
    pub fast_model_provider: Option<String>,
    pub fast_model_name: Option<String>,
    pub fast_model_temperature: f32,
    pub fast_model_tasks: Vec<String>,
    pub escalation_threshold: f32,
    pub show_reasoning: bool,
    pub enable_audio_transcription: bool,
    pub audio_response_mode: String,
    pub audio_scenario: Option<String>,
}

Configuration for a specific agent profile

Fields

prompt: Option<String>

System prompt for this agent

style: Option<String>

Conversational style or personality

temperature: Option<f32>

Temperature override for this agent (0.0 to 2.0)

model_provider: Option<String>

Model provider override (e.g., “openai”, “anthropic”, “lmstudio”)

model_name: Option<String>

Model name override (e.g., “gpt-4”, “claude-3-opus”)

allowed_tools: Option<Vec<String>>

List of tools this agent is allowed to use

denied_tools: Option<Vec<String>>

List of tools this agent is forbidden from using

memory_k: usize

Memory parameters: number of messages to recall (k for top-k)

top_p: f32

Top-p sampling parameter for memory recall

max_context_tokens: Option<usize>

Maximum context window size for this agent

enable_graph: bool

Enable knowledge graph features for this agent

graph_memory: bool

Use graph-based memory recall (combines with embeddings)

graph_depth: usize

Maximum graph traversal depth for context building

graph_weight: f32

Weight for graph-based relevance vs semantic similarity (0.0 to 1.0)

auto_graph: bool

Automatically build graph from conversations

graph_threshold: f32

Graph-based tool recommendation threshold (0.0 to 1.0)

graph_steering: bool

Use graph for decision steering

fast_reasoning: bool

Enable fast reasoning with a smaller model

fast_model_provider: Option<String>

Model provider for fast reasoning (e.g., “mlx”, “ollama”, “lmstudio”)

fast_model_name: Option<String>

Model name for fast reasoning (e.g., “lmstudio-community/Llama-3.2-3B-Instruct”)

fast_model_temperature: f32

Temperature for fast model (typically lower for consistency)

fast_model_tasks: Vec<String>

Tasks to delegate to fast model

escalation_threshold: f32

Confidence threshold to escalate to main model

show_reasoning: bool

Display reasoning summary to user (requires fast model for summarization)

enable_audio_transcription: bool

Enable audio transcription for this agent

audio_response_mode: String

Audio response mode: “immediate” or “batch”

audio_scenario: Option<String>

Preferred audio transcription scenario for testing

Implementations

impl AgentProfile

pub fn validate(&self) -> Result<(), Error>

Validate the agent profile configuration

pub fn is_tool_allowed(&self, tool_name: &str) -> bool

Check if a tool is allowed for this agent

pub fn effective_temperature(&self, default: f32) -> f32

Get the effective temperature (override or default)

pub fn effective_provider<'a>(&'a self, default: &'a str) -> &'a str

Get the effective model provider (override or default)

pub fn effective_model_name<'a>( &'a self, default: Option<&'a str>, ) -> Option<&'a str>

Get the effective model name (override or default)

Trait Implementations

impl Clone for AgentProfile

fn clone(&self) -> AgentProfile

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AgentProfile

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl Default for AgentProfile

fn default() -> AgentProfile

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for AgentProfile

fn deserialize<__D>( __deserializer: __D, ) -> Result<AgentProfile, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for AgentProfile

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.