agentloop

type Catalog struct {
	// contains filtered or unexported fields
}

type Decision struct {
	Kind       DecisionKind
	ToolCallID string
	ToolName   string
	// Step is the declared step to run, set when Kind is DecideRunStep.
	Step string
	// Args are the child DAG parameters, set when Kind is DecideRunStep.
	Args map[string]any
	// Task, TaskStatus and Reason are set when Kind is DecideSetTaskStatus.
	Task       string
	TaskStatus TaskStatus
	Reason     string
	// Question is set when Kind is DecideAskUser.
	Question string
	// Content is the model's prose, set when Kind is DecideStop.
	Content string
	// Problem describes why the decision was rejected, set when Kind is DecideInvalid.
	Problem string
}

type DecisionKind int

type Event struct {
	Turn int    `json:"turn"`
	Kind string `json:"kind"`
	// ToolCallID ties this event to its observation when a turn contains
	// multiple action calls.
	ToolCallID string `json:"toolCallId,omitempty"`
	// Name is the step or task the event concerns.
	Name string `json:"name,omitempty"`
	// Status is the resulting node status, for action events.
	Status string `json:"status,omitempty"`
	// Attempt counts this run of the step, starting at 1.
	Attempt int `json:"attempt,omitempty"`
	// Reason carries the agent's justification, or why a call was rejected.
	Reason     string `json:"reason,omitempty"`
	StartedAt  string `json:"startedAt,omitempty"`
	FinishedAt string `json:"finishedAt,omitempty"`
	// ChildDAGRunID identifies the child run this action produced, so the
	// timeline can link straight to it. Empty for steps that run no child DAG.
	ChildDAGRunID string `json:"childDagRunId,omitempty"`
	// ChildDAGName is the child DAG that ran.
	ChildDAGName string `json:"childDagName,omitempty"`
}

type MaskFunc func([]ir.LLMMessage) []ir.LLMMessage

type PendingAction struct {
	ToolCallID string `json:"toolCallId"`
	ToolName   string `json:"toolName"`
	Step       string `json:"step"`
	// Question is the text put to a person, set when the pending action is a
	// question the agent asked.
	Question string `json:"question,omitempty"`
}

type Planner struct {
	// contains filtered or unexported fields
}

type State struct {
	Tasks []TaskState `json:"tasks"`
	// Events is the decision timeline, in the order decisions were made.
	Events []Event `json:"events,omitempty"`
	// StepRuns counts how many times each step has been started.
	StepRuns map[string]int `json:"stepRuns,omitempty"`
	// Turns counts LLM decisions made so far.
	Turns int `json:"turns,omitempty"`
	// Pending is the legacy single action awaiting an observation. It is retained
	// for runs persisted before action batches were supported.
	Pending *PendingAction `json:"pending,omitempty"`
	// PendingActions is the ordered action batch whose observations have not
	// been reported to the model yet.
	PendingActions []PendingAction `json:"pendingActions,omitempty"`
	// Nudges counts consecutive turns where the LLM declined to act while tasks
	// were still open.
	Nudges int `json:"nudges,omitempty"`
	// Answers records what a person replied to each question, so the agent
	// is held to an answer it already has.
	Answers map[string]string `json:"answers,omitempty"`
	// ObservationAging records that old tool results must remain compacted for
	// the rest of this run.
	ObservationAging bool `json:"observationAging,omitempty"`
	// contains filtered or unexported fields
}

type TaskState struct {
	Name        string     `json:"name"`
	Description string     `json:"description,omitempty"`
	Status      TaskStatus `json:"status,omitempty"`
	// Reason is the justification the agent gave for the current status.
	Reason string `json:"reason,omitempty"`
}

type TaskStatus string