GT AI OS Community Edition v2.0.33
Security hardening release addressing CodeQL and Dependabot alerts: - Fix stack trace exposure in error responses - Add SSRF protection with DNS resolution checking - Implement proper URL hostname validation (replaces substring matching) - Add centralized path sanitization to prevent path traversal - Fix ReDoS vulnerability in email validation regex - Improve HTML sanitization in validation utilities - Fix capability wildcard matching in auth utilities - Update glob dependency to address CVE - Add CodeQL suppression comments for verified false positives 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
HackWeasel
439
apps/tenant-backend/app/models/assistant_template.py
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439
apps/tenant-backend/app/models/assistant_template.py
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"""
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Agent Template Models for GT 2.0
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Defines agent templates, custom builders, and MCP integration models.
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Follows the simplified hierarchy with file-based storage.
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"""
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from typing import List, Optional, Dict, Any, Union
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from datetime import datetime
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from enum import Enum
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from pydantic import BaseModel, Field
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import json
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from pathlib import Path
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from app.models.access_group import AccessGroup, Resource
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class AssistantType(str, Enum):
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"""Pre-defined agent types from architecture"""
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RESEARCH = "research_assistant"
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CODING = "coding_assistant"
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CYBER_ANALYST = "cyber_analyst"
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EDUCATIONAL = "educational_tutor"
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CUSTOM = "custom"
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class PersonalityConfig(BaseModel):
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"""Agent personality configuration"""
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tone: str = Field(default="balanced", description="formal | balanced | casual")
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explanation_depth: str = Field(default="intermediate", description="beginner | intermediate | expert")
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interaction_style: str = Field(default="collaborative", description="teaching | collaborative | direct")
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class ResourcePreferences(BaseModel):
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"""Agent resource preferences"""
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primary_llm: str = Field(default="gpt-4", description="Primary LLM model")
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fallback_models: List[str] = Field(default_factory=list, description="Fallback model list")
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context_length: int = Field(default=4000, description="Maximum context length")
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temperature: float = Field(default=0.7, description="Response temperature")
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streaming_enabled: bool = Field(default=True, description="Enable streaming responses")
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class MemorySettings(BaseModel):
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"""Agent memory configuration"""
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conversation_retention: str = Field(default="session", description="session | temporary | permanent")
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context_window_size: int = Field(default=10, description="Number of messages to retain")
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learning_from_interactions: bool = Field(default=False, description="Learn from user interactions")
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max_memory_size_mb: int = Field(default=50, description="Maximum memory size in MB")
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class AssistantTemplate(BaseModel):
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"""
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Pre-configured agent template
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Stored in Resource Cluster library
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"""
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template_id: str
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name: str
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description: str
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category: AssistantType
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# Core configuration
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system_prompt: str = Field(description="System prompt with variable substitution")
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default_capabilities: List[str] = Field(default_factory=list, description="Default capability requirements")
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# Configurations
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personality_config: PersonalityConfig = Field(default_factory=PersonalityConfig)
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resource_preferences: ResourcePreferences = Field(default_factory=ResourcePreferences)
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memory_settings: MemorySettings = Field(default_factory=MemorySettings)
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# Metadata
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icon_path: Optional[str] = None
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version: str = Field(default="1.0.0")
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created_at: datetime = Field(default_factory=datetime.utcnow)
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updated_at: datetime = Field(default_factory=datetime.utcnow)
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# Access control
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required_access_groups: List[str] = Field(default_factory=list)
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minimum_role: Optional[str] = None
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def to_instance(self, user_id: str, instance_name: str, tenant_domain: str) -> "AssistantInstance":
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"""Create an instance from this template"""
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return AssistantInstance(
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id=f"{user_id}-{instance_name}-{datetime.utcnow().timestamp()}",
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template_id=self.template_id,
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name=instance_name,
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description=f"Instance of {self.name}",
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owner_id=user_id,
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tenant_domain=tenant_domain,
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# Copy configurations
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system_prompt=self.system_prompt,
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capabilities=self.default_capabilities.copy(),
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personality_config=self.personality_config.model_copy(),
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resource_preferences=self.resource_preferences.model_copy(),
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memory_settings=self.memory_settings.model_copy(),
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# Instance specific
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access_group=AccessGroup.INDIVIDUAL,
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team_members=[],
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created_at=datetime.utcnow(),
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updated_at=datetime.utcnow()
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)
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class AssistantInstance(Resource):
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"""
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User's instance of an agent
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Inherits from Resource for access control
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"""
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template_id: Optional[str] = Field(default=None, description="Source template if from template")
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# Agent configuration
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system_prompt: str
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capabilities: List[str] = Field(default_factory=list)
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personality_config: PersonalityConfig = Field(default_factory=PersonalityConfig)
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resource_preferences: ResourcePreferences = Field(default_factory=ResourcePreferences)
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memory_settings: MemorySettings = Field(default_factory=MemorySettings)
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# Resource bindings
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linked_datasets: List[str] = Field(default_factory=list, description="Linked RAG dataset IDs")
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linked_tools: List[str] = Field(default_factory=list, description="Linked tool/integration IDs")
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linked_models: List[str] = Field(default_factory=list, description="Specific model overrides")
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# Usage tracking
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conversation_count: int = Field(default=0)
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total_messages: int = Field(default=0)
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total_tokens_used: int = Field(default=0)
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last_used: Optional[datetime] = None
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# File storage paths (created by controller)
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config_file_path: Optional[str] = None
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memory_file_path: Optional[str] = None
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def get_file_structure(self) -> Dict[str, str]:
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"""Get expected file structure for agent storage"""
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base_path = f"/data/{self.tenant_domain}/users/{self.owner_id}/agents/{self.id}"
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return {
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"config": f"{base_path}/config.json",
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"prompt": f"{base_path}/prompt.md",
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"capabilities": f"{base_path}/capabilities.json",
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"memory": f"{base_path}/memory/",
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"resources": f"{base_path}/resources/"
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}
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def update_from_template(self, template: AssistantTemplate):
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"""Update instance from template (for version updates)"""
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self.system_prompt = template.system_prompt
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self.personality_config = template.personality_config.model_copy()
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self.resource_preferences = template.resource_preferences.model_copy()
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self.updated_at = datetime.utcnow()
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def add_linked_dataset(self, dataset_id: str):
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"""Link a RAG dataset to this agent"""
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if dataset_id not in self.linked_datasets:
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self.linked_datasets.append(dataset_id)
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self.updated_at = datetime.utcnow()
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def remove_linked_dataset(self, dataset_id: str):
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"""Unlink a RAG dataset"""
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if dataset_id in self.linked_datasets:
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self.linked_datasets.remove(dataset_id)
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self.updated_at = datetime.utcnow()
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class AssistantBuilder(BaseModel):
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"""Configuration for building custom agents"""
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name: str
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description: Optional[str] = None
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base_template: Optional[AssistantType] = None
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# Custom configuration
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system_prompt: str
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personality_config: PersonalityConfig = Field(default_factory=PersonalityConfig)
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resource_preferences: ResourcePreferences = Field(default_factory=ResourcePreferences)
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memory_settings: MemorySettings = Field(default_factory=MemorySettings)
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# Capabilities
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requested_capabilities: List[str] = Field(default_factory=list)
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required_models: List[str] = Field(default_factory=list)
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required_tools: List[str] = Field(default_factory=list)
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def build_instance(self, user_id: str, tenant_domain: str) -> AssistantInstance:
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"""Build agent instance from configuration"""
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return AssistantInstance(
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id=f"custom-{user_id}-{datetime.utcnow().timestamp()}",
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template_id=None, # Custom build
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name=self.name,
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description=self.description or f"Custom agent by {user_id}",
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owner_id=user_id,
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tenant_domain=tenant_domain,
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resource_type="agent",
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# Apply configurations
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system_prompt=self.system_prompt,
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capabilities=self.requested_capabilities,
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personality_config=self.personality_config,
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resource_preferences=self.resource_preferences,
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memory_settings=self.memory_settings,
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# Default access
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access_group=AccessGroup.INDIVIDUAL,
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team_members=[],
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created_at=datetime.utcnow(),
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updated_at=datetime.utcnow()
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)
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# Pre-defined templates from architecture
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BUILTIN_TEMPLATES = {
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AssistantType.RESEARCH: AssistantTemplate(
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template_id="research_assistant_v1",
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name="Research & Analysis Agent",
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description="Specialized in information synthesis and analysis with citations",
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category=AssistantType.RESEARCH,
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system_prompt="""You are a research agent specialized in information synthesis and analysis.
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Focus on providing well-sourced, analytical responses with clear reasoning.
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Always cite your sources and provide evidence for your claims.
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When uncertain, clearly state the limitations of your knowledge.""",
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default_capabilities=[
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"llm:gpt-4",
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"rag:semantic_search",
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"tools:web_search",
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"export:citations"
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],
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personality_config=PersonalityConfig(
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tone="formal",
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explanation_depth="expert",
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interaction_style="collaborative"
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),
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resource_preferences=ResourcePreferences(
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primary_llm="gpt-4",
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fallback_models=["claude-sonnet", "gpt-3.5-turbo"],
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context_length=8000,
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temperature=0.7
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),
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required_access_groups=["research_tools"]
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),
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AssistantType.CODING: AssistantTemplate(
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template_id="coding_assistant_v1",
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name="Software Development Agent",
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description="Code quality, debugging, and development best practices",
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category=AssistantType.CODING,
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system_prompt="""You are a software development agent focused on code quality and best practices.
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Provide clear explanations, suggest improvements, and help debug issues.
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Follow the principle of clean, maintainable code.
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Always consider security implications in your suggestions.""",
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default_capabilities=[
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"llm:claude-sonnet",
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"tools:github_integration",
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"resources:documentation",
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"export:code_snippets"
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],
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personality_config=PersonalityConfig(
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tone="balanced",
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explanation_depth="intermediate",
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interaction_style="direct"
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),
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resource_preferences=ResourcePreferences(
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primary_llm="claude-sonnet",
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fallback_models=["gpt-4", "codellama"],
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context_length=16000,
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temperature=0.5
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),
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required_access_groups=["development_tools"]
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),
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AssistantType.CYBER_ANALYST: AssistantTemplate(
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template_id="cyber_analyst_v1",
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name="Cybersecurity Analysis Agent",
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description="Threat detection, incident response, and security best practices",
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category=AssistantType.CYBER_ANALYST,
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system_prompt="""You are a cybersecurity analyst agent for threat detection and response.
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Prioritize security best practices and provide actionable recommendations.
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Consider defense-in-depth strategies and zero-trust principles.
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Always emphasize the importance of continuous monitoring and improvement.""",
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default_capabilities=[
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"llm:gpt-4",
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"tools:security_scanning",
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"resources:threat_intelligence",
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"export:security_reports"
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],
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personality_config=PersonalityConfig(
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tone="formal",
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explanation_depth="expert",
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interaction_style="direct"
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),
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resource_preferences=ResourcePreferences(
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primary_llm="gpt-4",
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fallback_models=["claude-sonnet"],
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context_length=8000,
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temperature=0.3
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),
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required_access_groups=["cybersecurity_advanced"]
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),
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AssistantType.EDUCATIONAL: AssistantTemplate(
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template_id="educational_tutor_v1",
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name="AI Literacy Educational Agent",
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description="Critical thinking development and AI collaboration skills",
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category=AssistantType.EDUCATIONAL,
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system_prompt="""You are an educational agent focused on developing critical thinking and AI literacy.
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Use socratic questioning and encourage deep analysis of problems.
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Help students understand both the capabilities and limitations of AI.
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Foster independent thinking while teaching effective AI collaboration.""",
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default_capabilities=[
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"llm:claude-sonnet",
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"games:strategic_thinking",
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"puzzles:logic_reasoning",
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"analytics:learning_progress"
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],
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personality_config=PersonalityConfig(
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tone="casual",
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explanation_depth="beginner",
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interaction_style="teaching"
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),
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resource_preferences=ResourcePreferences(
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primary_llm="claude-sonnet",
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fallback_models=["gpt-4"],
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context_length=4000,
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temperature=0.8
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),
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required_access_groups=["ai_literacy"]
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)
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}
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class AssistantTemplateLibrary:
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"""
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Manages the agent template library
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Templates stored in Resource Cluster, cached locally
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"""
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def __init__(self, resource_cluster_url: str):
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self.resource_cluster_url = resource_cluster_url
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self.cache_path = Path("/tmp/agent_templates_cache")
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self.cache_path.mkdir(exist_ok=True)
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self._templates_cache: Dict[str, AssistantTemplate] = {}
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async def get_template(self, template_id: str) -> Optional[AssistantTemplate]:
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"""Get template by ID, using cache if available"""
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if template_id in self._templates_cache:
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return self._templates_cache[template_id]
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# Check built-in templates
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for template_type, template in BUILTIN_TEMPLATES.items():
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if template.template_id == template_id:
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self._templates_cache[template_id] = template
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return template
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# Would fetch from Resource Cluster in production
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return None
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async def list_templates(
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self,
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category: Optional[AssistantType] = None,
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access_groups: Optional[List[str]] = None
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) -> List[AssistantTemplate]:
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"""List available templates with filtering"""
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templates = list(BUILTIN_TEMPLATES.values())
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if category:
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templates = [t for t in templates if t.category == category]
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if access_groups:
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templates = [
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t for t in templates
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if any(g in access_groups for g in t.required_access_groups)
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]
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return templates
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async def deploy_template(
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self,
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template_id: str,
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user_id: str,
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instance_name: str,
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tenant_domain: str,
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customizations: Optional[Dict[str, Any]] = None
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) -> AssistantInstance:
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"""Deploy template as user instance"""
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template = await self.get_template(template_id)
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if not template:
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raise ValueError(f"Template not found: {template_id}")
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# Create instance
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instance = template.to_instance(user_id, instance_name, tenant_domain)
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# Apply customizations
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if customizations:
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if "personality" in customizations:
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instance.personality_config = PersonalityConfig(**customizations["personality"])
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if "resources" in customizations:
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instance.resource_preferences = ResourcePreferences(**customizations["resources"])
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if "memory" in customizations:
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instance.memory_settings = MemorySettings(**customizations["memory"])
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return instance
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# API Models
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class AssistantTemplateResponse(BaseModel):
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"""API response for agent template"""
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template_id: str
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name: str
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description: str
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category: str
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required_access_groups: List[str]
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version: str
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created_at: datetime
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class AssistantInstanceResponse(BaseModel):
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"""API response for agent instance"""
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id: str
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name: str
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description: str
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template_id: Optional[str]
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owner_id: str
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access_group: AccessGroup
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team_members: List[str]
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conversation_count: int
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last_used: Optional[datetime]
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created_at: datetime
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updated_at: datetime
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class CreateAssistantRequest(BaseModel):
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"""Request to create agent from template or custom"""
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template_id: Optional[str] = None
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name: str
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description: Optional[str] = None
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customizations: Optional[Dict[str, Any]] = None
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# For custom agents
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system_prompt: Optional[str] = None
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personality_config: Optional[PersonalityConfig] = None
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resource_preferences: Optional[ResourcePreferences] = None
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memory_settings: Optional[MemorySettings] = None
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