Struct AgentLoopConfig 

pub struct AgentLoopConfig {

    pub max_rounds: usize,
    pub system_prompt: Option<String>,
    pub disabled_skill_ids: BTreeSet<String>,
    pub selected_skill_ids: Option<Vec<String>>,
    pub selected_skill_mode: Option<String>,
    pub additional_tool_schemas: Vec<ToolSchema>,
    pub tool_registry: Arc<ToolRegistry>,
    pub composition_executor: Option<Arc<CompositionExecutor>>,
    pub skill_manager: Option<Arc<SkillManager>>,
    pub skip_initial_user_message: bool,
    pub storage: Option<Arc<dyn Storage>>,
    pub attachment_reader: Option<Arc<dyn AttachmentReader>>,
    pub metrics_collector: Option<MetricsCollector>,
    pub model_name: Option<String>,
    pub fast_model_name: Option<String>,
    pub background_model_name: Option<String>,
    pub planning_model_name: Option<String>,
    pub search_model_name: Option<String>,
    pub compression_instructions: Option<String>,
    pub summarization_model_name: Option<String>,
    pub background_model_provider: Option<Arc<dyn LLMProvider>>,
    pub provider_name: Option<String>,
    pub reasoning_effort: Option<ReasoningEffort>,
    pub disabled_tools: BTreeSet<String>,
    pub token_budget: Option<TokenBudget>,
    pub image_fallback: Option<ImageFallbackConfig>,
    pub prompt_memory_flags: PromptMemoryFlags,
    pub max_tool_calls_per_round: usize,
    pub max_consecutive_failures_per_tool: usize,
    pub strict_argument_tool_names: Vec<String>,
    pub per_tool_timeout_secs: u64,
    pub parallel_batch_timeout_secs: u64,
    pub permission_mode: Option<PermissionMode>,
    pub features_dynamic_model_routing: bool,
}

Configuration for the agent loop.

Fields

max_rounds: usize

system_prompt: Option<String>

disabled_skill_ids: BTreeSet<String>

Skill IDs that are disabled globally for this execution.

selected_skill_ids: Option<Vec<String>>

Optional explicit skill selection for this execution. When set, only these skill IDs are considered for skill context and allowlists.

selected_skill_mode: Option<String>

Optional active skill mode for this execution.

When set, skill discovery prefers skills-<mode> directories over generic directories for the same skill id.

additional_tool_schemas: Vec<ToolSchema>

tool_registry: Arc<ToolRegistry>

composition_executor: Option<Arc<CompositionExecutor>>

skill_manager: Option<Arc<SkillManager>>

skip_initial_user_message: bool

If true, skip appending the initial user message (already present in session).

storage: Option<Arc<dyn Storage>>

Optional storage for persisting session changes

attachment_reader: Option<Arc<dyn AttachmentReader>>

Optional attachment reader for resolving bamboo-attachment://... references into data: URLs for upstream providers. This must not mutate session storage.

metrics_collector: Option<MetricsCollector>

Optional asynchronous metrics collector

model_name: Option<String>

Model name used for metrics attribution

fast_model_name: Option<String>

Optional explicit fast/cheap model name for lightweight foreground tasks such as task evaluation.

Call sites may fall back to model_name when this is unset.

background_model_name: Option<String>

Dedicated background summarization model for host-side context compression and other non-interactive maintenance work.

Unlike fast_model_name, this must not silently fall back to the main interaction model.

planning_model_name: Option<String>

Model for planning/coordination tasks (task decomposition, architecture). Falls back to model_name when unset.

search_model_name: Option<String>

Model for search/navigation tasks (grep, file listing, symbol resolution). Falls back to fast_model_name when unset.

compression_instructions: Option<String>

Custom instructions for conversation summarization, injected into the LLM summary prompt. Lets users control what the summary focuses on.

Resolution order: session-level > config-level > built-in defaults.

summarization_model_name: Option<String>

Dedicated model for summarization. Falls back to background_model_name.

background_model_provider: Option<Arc<dyn LLMProvider>>

Optional provider override for background/fast model LLM calls.

When set, context compression, summarization, and other background model calls use this provider instead of the shared agent loop provider.

provider_name: Option<String>

Provider name used for provider-specific request behavior.

reasoning_effort: Option<ReasoningEffort>

Optional request-time reasoning effort override.

disabled_tools: BTreeSet<String>

Tool names that should be excluded from schemas sent to the LLM.

token_budget: Option<TokenBudget>

Token budget for context management (optional, defaults to model’s limits)

image_fallback: Option<ImageFallbackConfig>

Optional image fallback behavior applied to LLM requests only (never persisted).

This is intended for text-only provider paths where image parts must be degraded (placeholder / OCR / error) without leaking into stored session history or UI.

prompt_memory_flags: PromptMemoryFlags

Feature flags controlling prompt-time memory injection behavior.

max_tool_calls_per_round: usize

Maximum tool calls allowed per round (default: 80).

max_consecutive_failures_per_tool: usize

Maximum consecutive failures per tool before circuit breaker (default: 3).

strict_argument_tool_names: Vec<String>

Tool names that require strict argument validation.

per_tool_timeout_secs: u64

Per-tool execution timeout in seconds (default: 120).

parallel_batch_timeout_secs: u64

Parallel batch execution timeout in seconds (default: 300).

permission_mode: Option<PermissionMode>

Permission mode for this execution (default: None = use PermissionConfig’s mode).

features_dynamic_model_routing: bool

Enable dynamic per-round model routing based on task complexity. When true, the pipeline classifies complexity at each round end and stores the result in session metadata.

Trait Implementations

impl Default for AgentLoopConfig

fn default() -> Self

Returns the “default value” for a type.