Crate aida

Expand description

§AIDA — AI-Native Requirements Management

Track what you build and why, with structured context for AI coding agents.

§Install the CLI

cargo install aida-cli

§Library Usage

This crate re-exports aida-core. For most users, install the CLI instead.

use aida::models::{Requirement, RequirementsStore};

§Links

Modules§

ai: AI Integration Module for AIDA
analytics: Analytics engine — deeper metrics for requirements management.
conflict: Conflict detection and resolution for distributed AIDA.
daemon: Daemon dispenser — Phase 3 sequence number server over Unix socket.
db: Database abstraction layer for AIDA requirements management
dispenser: Sequence Dispenser — generates monotonically increasing sequence numbers for object ID creation.
export: Export/Import functionality for requirements.
git_ops: Git operations for distributed AIDA.
hlc: Hybrid Logical Clock (HLC) implementation for distributed timestamp ordering.
import: Import functionality for legacy/incompatible requirements databases.
integrations: External integrations module.
meta: META Requirements Module
models
node: Node identity and workspace configuration for distributed AIDA.
object_store: Git-friendly object store for distributed AIDA.
oplog: Operation Log — append-only event log for conflict-free distributed sync.
project
registry
report: AI Integration Report Generation
scaffolding: AI Project Scaffolding Module
storage
telemetry: Telemetry — track AIDA usage for measuring tool effectiveness.
templates: Template loading system for scaffolding.
workspace: Multi-repo workspace support for distributed AIDA.

Structs§

AddResult: Result of adding a new requirement atomically
AgreedCounters: Per-type counters for agreed IDs assigned at merge-to-trunk.
AiActionPromptConfig: Configuration for a single AI prompt action (evaluation, duplicates, etc.)
AiClient: AI Client for interacting with Claude
AiIntegrationReport: AI Integration Report data
AiPromptConfig: Complete AI prompt configuration for a project
AiPromptsSection: AI Prompts configuration section
AiTypePromptConfig: Per-type AI prompt customization
BackgroundEvaluator: Background AI Evaluator
Baseline: Represents a baseline - a named snapshot of requirements at a point in time
BaselineComparison: Summary of changes between two baselines or baseline and current state
BaselineRequirementDiff: Represents changes to a single requirement between baselines
Comment: Represents a comment on a requirement with threading support
CommentReaction: Represents a reaction on a comment
ConflictInfo: Information about a conflict detected during save
CustomFieldDefinition: Definition of a custom field for a requirement type
CustomTypeDefinition: Definition of a custom requirement type with its specific statuses and fields
DatabaseConfig: Configuration for database backends
DispenserState: Current state of the dispenser — all counters for a given node.
EditLock: Information about a requirement being edited
EvaluationResponse: Response from evaluation action
EvaluationResult: Result of an evaluation to be applied to the store
EvaluatorConfig: Configuration for the background evaluator
FeatureDefinition: Configuration for a feature with its prefix
FieldChange: Represents a field change in a requirement’s history
FieldConflict: Describes a conflict in a specific field
FileDispenser: File-backed dispenser using a TOML state file with advisory locking. This is the Phase 1 implementation per the distributed architecture spec.
GitBackend: Git-backed storage backend.
GitLabIssueLink: Represents a link between an AIDA requirement and a GitLab issue Used to track traceability between specs and implementation work
GitLabSyncState: Tracks sync state between an AIDA requirement and a GitLab issue
HistoryEntry: Represents a history entry for a requirement update
Hlc: The HLC clock instance. Thread-safe via internal Mutex.
HlcTimestamp: A Hybrid Logical Clock timestamp.
IdConfigValidation: Result of validating ID configuration changes
IdConfiguration: ID system configuration
ImplementationInfo: Tracks implementation metadata for a requirement Stores information about how and when a requirement was implemented
ImportConfig: Configuration for an import operation
ImportIssue: Represents an issue found during import validation
ImportSummary: Summary of an import operation
ImportValidation: Result of validating an import file
IssueReport: Issue found in a requirement
LockFileInfo: Lock file contents tracking all active sessions
MemoryDispenser: A simple in-memory dispenser for testing and single-session use. Not persistent — loses state on drop.
NodeConfig: Information about a registered node (persisted locally, gitignored).
NodeRegistry: The shared node registry (committed to git, append-only).
NodeRegistryEntry: A node registration entry in the shared registry (committed to git).
PromptCustomization: Individual prompt customization
QueueEntry: Represents an entry in a user’s personal work queue
RawImportStore: Raw store structure for initial parsing
ReactionDefinition: Represents a reaction emoji definition
Registry: Registry of all projects
Relationship: Represents a relationship between two requirements
RelationshipDefinition: Defines a relationship type and its constraints
RelationshipValidation: Result of validating a relationship
ReportGenerator: Report generator
Requirement: Represents a single requirement in the system
RequirementSnapshot: A snapshot of a requirement at a specific point in time (for baselines) This is a full copy of the requirement state, not a reference
RequirementTypeDefinition: Configuration for a requirement type with its prefix
RequirementsStore: Collection of all requirements
ScaffoldArtifact: Represents a scaffolding artifact to be generated
ScaffoldConfig: Configuration for what scaffolding artifacts to generate
ScaffoldPreview: Result of scaffolding preview
ScaffoldStatus: Result of comparing scaffold to actual project
Scaffolder: Scaffolding generator
SessionInfo: Information about a user session with an open requirements file
SqliteBackend: SQLite backend implementation
SqliteDispenser: SQLite-backed dispenser using atomic UPSERT for sequence generation. This is the Phase 2 implementation per the distributed architecture spec.
Storage: Handles saving and loading requirements from disk with file locking for rudimentary multi-user support
StoredAiEvaluation: Stored AI evaluation with metadata This struct wraps evaluation results with timestamps to track when evaluations need to be refreshed (when requirement changes).
SuggestedImprovement: Suggested improvement to a requirement
Team: Represents a team in the system Teams can contain users (members) and can be nested (parent/child teams)
TemplateInfo: Information about an embedded template
TemplateLoader: Template loader that checks external files first, then falls back to embedded.
TraceLink: Represents a trace link between a requirement and a code artifact This enables bidirectional traceability: requirement -> code and code -> requirement
TraceabilityStats: Traceability statistics
TypePromptCustomization: Type-specific prompt customization
UrlLink: Represents an external URL link attached to a requirement
User: Represents a user in the system
UserRegistry: The shared user registry (committed to git, append-only).
UserRegistryEntry: A user registration entry in the shared registry.
VersionConflict: Error type for version conflicts during optimistic locking
WorkspaceConfig: Workspace configuration — discovered by walking up the directory tree.
YamlBackend: YAML file backend implementation

Enums§

AiMode: AI operation mode
ArtifactType: Represents the type of artifact being traced
BackendType: Types of database backends available
Cardinality: Cardinality constraints for relationships
ConfidenceLevel: Confidence level for AI-generated implementation
ConflictResolution: Resolution strategy when a conflict is detected
CustomFieldType: Field type for custom fields
DeploymentMode: The deployment mode for an AIDA instance.
EvaluatorStatus: Status of the background evaluator
FileStatus: Status of a scaffolded file compared to disk
GitLabLinkType: Type of link between AIDA requirement and GitLab issue
IdFormat: ID format style for requirement identifiers
IdMode: The operating mode for ID generation.
ImportIssueType: Types of issues that can occur during import
ImportMergeMode: How to merge imported data with existing data
IssueResolution: How to handle a specific issue during import
LinkOrigin: How a GitLab link was originally created
MetaSubtype: Represents the subtype for Meta requirements
MigrationCheck: Result of checking for migration status
NumberingStrategy: Numbering strategy for requirement IDs
ProjectType: Project type for specialized scaffolding
RelationshipType: Represents a relationship type between requirements
ReportFormat: Report output format
RequirementPriority: Represents the priority of a requirement
RequirementStatus: Represents the status of a requirement
RequirementType: Represents the type of a requirement
SaveResult: Result of attempting a save with conflict detection
ScaffoldError: Errors that can occur during scaffolding
StorageError: Error type for storage operations
SyncStatus: Current sync status between AIDA and GitLab
TemplateSource: Source of a template
UpdateResult: Result type for optimistic locking operations
UrlOpenMode: How a URL should be opened when clicked

Constants§

DEFAULT_DUPLICATES_PROMPT: Default template for finding duplicates
DEFAULT_EVALUATION_PROMPT: Default template for requirement evaluation
DEFAULT_GENERATE_CHILDREN_PROMPT: Default template for generating child requirements
DEFAULT_IMPROVE_PROMPT: Default template for improving descriptions
DEFAULT_RELATIONSHIPS_PROMPT: Default template for suggesting relationships
META_PREFIX_FEATURE
META_PREFIX_TEAM
META_PREFIX_USER: Meta-type prefixes for special object types
META_PREFIX_VIEW

Traits§

DatabaseBackend: Core trait for database backends
Dispenser: The Dispenser trait — the stable interface for sequence number generation.

Functions§

check_migration_status: Check if a YAML file has been migrated to SQLite
check_scaffold_status: Check scaffold status for a project (standalone function for CLI)
create_backend: Creates a database backend based on the file extension or explicit type
create_backup: Create a backup of the current requirements file
default_reaction_definitions: Returns the default set of reaction definitions
default_type_definitions: Returns the default type definitions
determine_requirements_path: Determines the requirements file path to use based on the available information. This version does NOT check for migration - use determine_requirements_path_with_migration_check for migration-aware path resolution.
execute_import: Execute an import operation with the given configuration
export_to_json: Exports a RequirementsStore to a JSON file
get_config_dir: Gets the path to the AIDA config directory (~/.config/aida/)
get_embedded_templates: Get all embedded templates with their info
get_prompt_template: Get a prompt template, checking database first, then falling back to embedded
get_registry_path: Gets the path to the registry file
get_template_categories: Get template categories with descriptions
get_templates_by_category: Get templates by category
get_templates_dir: Gets the path to the templates directory (~/.config/aida/templates/)
import_from_json: Imports a RequirementsStore from a JSON file
migrate_sqlite_to_yaml: Migrates data from a SQLite database to a YAML file
migrate_yaml_to_sqlite: Migrates data from a YAML file to a SQLite database
needs_meta_seeding: Check if META requirements need seeding
open_or_create: Opens an existing database or creates a new one
seed_meta_requirements: Seed META requirements in a new database
validate_import_content: Validate import content (YAML string)
validate_import_file: Validate an import file and return validation results

Crate aida

Crate aida Copy item path

§AIDA — AI-Native Requirements Management

§Install the CLI

§Library Usage

§Links

Modules§

Structs§

Enums§

Constants§

Traits§

Functions§

Crate aida