Struct AgentConfig 

pub struct AgentConfig {

    pub provider: String,
    pub api_key_env: String,
    pub default_model: String,
    pub theme: String,
    pub todo_planning_mode: bool,
    pub ui_surface: UiSurfacePreference,
    pub max_conversation_turns: usize,
    pub reasoning_effort: ReasoningEffortLevel,
    pub verbosity: VerbosityLevel,
    pub temperature: f32,
    pub max_tokens: u32,
    pub refine_temperature: f32,
    pub refine_max_tokens: u32,
    pub enable_self_review: bool,
    pub max_review_passes: usize,
    pub refine_prompts_enabled: bool,
    pub refine_prompts_max_passes: usize,
    pub refine_prompts_model: String,
    pub small_model: AgentSmallModelConfig,
    pub onboarding: AgentOnboardingConfig,
    pub project_doc_max_bytes: usize,
    pub instruction_max_bytes: usize,
    pub instruction_files: Vec<String>,
    pub custom_prompts: AgentCustomPromptsConfig,
    pub custom_api_keys: BTreeMap<String, String>,
    pub checkpointing: AgentCheckpointingConfig,
}

Agent-wide configuration

Fields

provider: String

AI provider for single agent mode (gemini, openai, anthropic, openrouter, xai, zai)

api_key_env: String

Environment variable that stores the API key for the active provider

default_model: String

Default model to use

theme: String

UI theme identifier controlling ANSI styling

todo_planning_mode: bool

Enable TODO planning workflow integrations (update_plan tool, onboarding hints)

ui_surface: UiSurfacePreference

Preferred rendering surface for the interactive chat UI (auto, alternate, inline)

max_conversation_turns: usize

Maximum number of conversation turns before auto-termination

reasoning_effort: ReasoningEffortLevel

Reasoning effort level for models that support it (none, low, medium, high) Applies to: Claude, GPT-5, GPT-5.1, Gemini, Qwen3, DeepSeek with reasoning capability

verbosity: VerbosityLevel

Verbosity level for output text (low, medium, high) Applies to: GPT-5.1 and other models that support verbosity control

temperature: f32

Temperature for main LLM responses (0.0-1.0) Lower values = more deterministic, higher values = more creative Recommended: 0.7 for balanced creativity and consistency Range: 0.0 (deterministic) to 1.0 (maximum randomness)

max_tokens: u32

Maximum tokens for main LLM generation responses (default: 2000) Adjust based on model context window size:

• 2000 for standard tasks
• 16384 for models with 128k context
• 32768 for models with 256k context

refine_temperature: f32

Temperature for prompt refinement (0.0-1.0, default: 0.3) Lower values ensure prompt refinement is more deterministic/consistent Keep lower than main temperature for stable prompt improvement

refine_max_tokens: u32

Maximum tokens for prompt refinement (default: 800) Prompts are typically shorter, so 800 tokens is usually sufficient

enable_self_review: bool

Enable an extra self-review pass to refine final responses

max_review_passes: usize

Maximum number of self-review passes

refine_prompts_enabled: bool

Enable prompt refinement pass before sending to LLM

refine_prompts_max_passes: usize

Max refinement passes for prompt writing

refine_prompts_model: String

Optional model override for the refiner (empty = auto pick efficient sibling)

small_model: AgentSmallModelConfig

Small/lightweight model configuration for efficient operations Used for tasks like large file reads, parsing, git history, conversation summarization Typically 70-80% cheaper than main model; ~50% of Claude Code’s calls use this tier

onboarding: AgentOnboardingConfig

Session onboarding and welcome message configuration

project_doc_max_bytes: usize

Maximum bytes of AGENTS.md content to load from project hierarchy

instruction_max_bytes: usize

Maximum bytes of instruction content to load from AGENTS.md hierarchy

instruction_files: Vec<String>

Additional instruction files or globs to merge into the hierarchy

custom_prompts: AgentCustomPromptsConfig

Custom prompt configuration for slash command shortcuts

custom_api_keys: BTreeMap<String, String>

Provider-specific API keys captured from interactive configuration flows

checkpointing: AgentCheckpointingConfig

Checkpointing configuration for automatic turn snapshots

Implementations

impl AgentConfig

pub fn validate_llm_params(&self) -> Result<(), String>

Validate LLM generation parameters

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.

impl<'de> Deserialize<'de> for AgentConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl JsonSchema for AgentConfig

fn schema_name() -> String

The name of the generated JSON Schema.

fn schema_id() -> Cow<'static, str>

Returns a string that uniquely identifies the schema produced by this type.

fn json_schema(generator: &mut SchemaGenerator) -> Schema

Generates a JSON Schema for this type.

fn is_referenceable() -> bool

Whether JSON Schemas generated for this type should be re-used where possible using the $ref keyword.

impl Serialize for AgentConfig

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

where __S: Serializer,

Serialize this value into the given Serde serializer.