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Auto-Dev Database Models
SQLAlchemy models for the self-evolving agent system:
- ToolMutation: Tracks code mutations and lineage for AlphaEvolver
- WorkflowVariant: Tracks workflow variations with fitness scores
- SkillCandidate: Memento-generated skill proposals awaiting validation
"""
import uuid
from datetime import datetime, timezone
from sqlalchemy import Column, DateTime, Float, Index, JSON, String, Text
from core.database import Base
class ToolMutation(Base):
"""
AlphaEvolve: Tracks tool code mutations, lineage, and sandbox test results.
Each mutation has a parent_tool_id for lineage tracing, allowing the system
to track evolutionary chains of code improvements.
"""
__tablename__ = "tool_mutations"
id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
tenant_id = Column(String(36), nullable=False, index=True)
parent_tool_id = Column(String(36), nullable=True, index=True) # Lineage tracing
tool_name = Column(String(255), nullable=False)
mutated_code = Column(Text, nullable=False)
sandbox_status = Column(String(50), default="pending") # pending, passed, failed
execution_error = Column(Text, nullable=True)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
class WorkflowVariant(Base):
"""
AlphaEvolve: Tracks variations of workflows/prompts alongside their
automated fitness scores.
Fitness is evaluated in two stages:
1. Immediate proxy signals (compilation, execution success)
2. Deferred async signals (webhook events, conversion data)
"""
__tablename__ = "workflow_variants"
id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
tenant_id = Column(String(36), nullable=False, index=True)
parent_variant_id = Column(String(36), nullable=True, index=True)
agent_id = Column(String(36), nullable=True, index=True)
workflow_definition = Column(JSON, nullable=False)
fitness_score = Column(Float, nullable=True) # 0 to 1.0
fitness_signals = Column(JSON, nullable=True) # Raw proxy or external signals
evaluation_status = Column(String(50), default="pending") # pending, evaluated, pruned
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
last_evaluated_at = Column(DateTime(timezone=True), nullable=True)
class SkillCandidate(Base):
"""
Memento-Skills: Skill proposals generated from failed episodes.
When an agent fails a task repeatedly, the MementoEngine analyzes
the failure pattern and generates a new skill candidate. The candidate
must pass sandbox validation before it can be promoted to the active
skill registry.
Lifecycle: pending → validated/failed → promoted
"""
__tablename__ = "skill_candidates"
id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
tenant_id = Column(String(36), nullable=False, index=True)
agent_id = Column(String(36), nullable=True, index=True)
source_episode_id = Column(String(36), nullable=True, index=True)
skill_name = Column(String(255), nullable=False)
skill_description = Column(Text, nullable=True)
generated_code = Column(Text, nullable=False)
failure_pattern = Column(JSON, nullable=True) # Extracted from episode analysis
validation_status = Column(
String(50), default="pending"
) # pending, validated, failed, promoted
validation_result = Column(JSON, nullable=True) # Sandbox execution results
fitness_score = Column(Float, nullable=True)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
validated_at = Column(DateTime(timezone=True), nullable=True)
promoted_at = Column(DateTime(timezone=True), nullable=True)
__table_args__ = (
Index("ix_skill_candidates_tenant_status", "tenant_id", "validation_status"),
)
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