Struct AgentConfig 

pub struct AgentConfig {
    pub id: String,
    pub model: String,
    pub system_prompt: String,
    pub max_rounds: usize,
    pub tool_executor: Arc<dyn ToolExecutor>,
    pub chat_options: Option<ChatOptions>,
    pub fallback_models: Vec<String>,
    pub llm_retry_policy: LlmRetryPolicy,
    pub plugins: Vec<Arc<dyn AgentPlugin>>,
    pub step_tool_provider: Option<Arc<dyn StepToolProvider>>,
    pub llm_executor: Option<Arc<dyn LlmExecutor>>,
}

Runtime configuration for the agent loop.

Fields

id: String

Unique identifier for this agent.

model: String

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

system_prompt: String

System prompt for the LLM.

max_rounds: usize

Optional loop-budget hint (core loop does not enforce this directly).

tool_executor: Arc<dyn ToolExecutor>

Tool execution strategy (parallel, sequential, or custom).

chat_options: Option<ChatOptions>

Chat options for the LLM.

fallback_models: Vec<String>

Fallback model ids used when the primary model fails.

Evaluated in order after model.

llm_retry_policy: LlmRetryPolicy

Retry policy for LLM inference failures.

plugins: Vec<Arc<dyn AgentPlugin>>

Plugins to run during the agent loop.

step_tool_provider: Option<Arc<dyn StepToolProvider>>

Optional per-step tool provider.

When not set, the loop uses a static provider derived from the tools map passed to run_step / run_loop / run_loop_stream.

llm_executor: Option<Arc<dyn LlmExecutor>>

Optional LLM executor override.

When not set, the loop uses GenaiLlmExecutor with Client::default().

Implementations

impl AgentConfig

pub fn new(model: impl Into<String>) -> Self

Create a new instance with the given model id.

pub fn with_id(id: impl Into<String>, model: impl Into<String>) -> Self

Create a new instance with explicit id and model.

pub fn with_system_prompt(self, prompt: impl Into<String>) -> Self

Set system prompt.

pub fn with_max_rounds(self, max_rounds: usize) -> Self

Set max rounds.

pub fn with_chat_options(self, options: ChatOptions) -> Self

Set chat options.

pub fn with_fallback_models(self, models: Vec<String>) -> Self

Set fallback model ids to try after the primary model.

pub fn with_fallback_model(self, model: impl Into<String>) -> Self

Add a single fallback model id.

pub fn with_llm_retry_policy(self, policy: LlmRetryPolicy) -> Self

Set LLM retry policy.

pub fn with_plugins(self, plugins: Vec<Arc<dyn AgentPlugin>>) -> Self

Set plugins.

pub fn with_plugin(self, plugin: Arc<dyn AgentPlugin>) -> Self

Add a single plugin.

pub fn with_tool_executor(self, executor: Arc<dyn ToolExecutor>) -> Self

Set tool executor strategy.

pub fn with_tools(self, tools: HashMap<String, Arc<dyn Tool>>) -> Self

Set static tool map (wraps in [StaticStepToolProvider]).

Prefer passing tools directly to [run_loop] / [run_loop_stream]; use this only when you need to set tools via step_tool_provider.

pub fn with_step_tool_provider( self, provider: Arc<dyn StepToolProvider>, ) -> Self

Set per-step tool provider.

pub fn with_llm_executor(self, executor: Arc<dyn LlmExecutor>) -> Self

Set LLM executor.

pub fn has_plugins(&self) -> bool

Check if any plugins are configured.

Trait Implementations

impl Clone for AgentConfig

fn clone(&self) -> AgentConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AgentConfig

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

Formats the value using the given formatter.

impl Default for AgentConfig

fn default() -> Self

Returns the “default value” for a type.