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import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import yaml
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from pydantic import (
BaseModel,
Field,
field_validator,
)
from swarms.utils.loguru_logger import initialize_logger
from swarms.structs.agent import Agent
from swarms.structs.swarm_router import SwarmRouter
from swarms.utils.litellm_wrapper import LiteLLM
logger = initialize_logger(log_folder="create_agents_from_yaml")
class AgentConfig(BaseModel):
agent_name: str
system_prompt: str
model_name: Optional[str] = None
max_loops: int = Field(default=1, ge=1)
autosave: bool = True
dashboard: bool = False
verbose: bool = False
dynamic_temperature_enabled: bool = False
saved_state_path: Optional[str] = None
user_name: str = "default_user"
retry_attempts: int = Field(default=3, ge=1)
context_length: int = Field(default=100000, ge=1000)
return_step_meta: bool = False
output_type: str = "str"
auto_generate_prompt: bool = False
artifacts_on: bool = False
artifacts_file_extension: str = ".md"
artifacts_output_path: str = ""
@field_validator("system_prompt")
@classmethod
def validate_system_prompt(cls, v):
if not v or not isinstance(v, str) or len(v.strip()) == 0:
raise ValueError(
"System prompt must be a non-empty string"
)
return v
class SwarmConfig(BaseModel):
name: str
description: str
max_loops: int = Field(default=1, ge=1)
swarm_type: str
task: Optional[str] = None
flow: Optional[Dict] = None
autosave: bool = True
return_json: bool = False
rules: str = ""
@field_validator("swarm_type")
@classmethod
def validate_swarm_type(cls, v):
valid_types = {
"SequentialWorkflow",
"ConcurrentWorkflow",
"AgentRearrange",
"MixtureOfAgents",
"auto",
}
if v not in valid_types:
raise ValueError(
f"Swarm type must be one of: {valid_types}"
)
return v
class YAMLConfig(BaseModel):
agents: List[AgentConfig] = Field(..., min_length=1)
swarm_architecture: Optional[SwarmConfig] = None
model_config = {
"extra": "forbid" # Prevent additional fields not in the model
}
def load_yaml_safely(
yaml_file: str = None, yaml_string: str = None
) -> Dict:
"""Safely load and validate YAML configuration using Pydantic."""
try:
if yaml_string:
config_dict = yaml.safe_load(yaml_string)
elif yaml_file:
if not os.path.exists(yaml_file):
raise FileNotFoundError(
f"YAML file {yaml_file} not found."
)
with open(yaml_file, "r") as file:
config_dict = yaml.safe_load(file)
else:
raise ValueError(
"Either yaml_file or yaml_string must be provided"
)
# Validate using Pydantic
YAMLConfig(**config_dict)
return config_dict
except yaml.YAMLError as e:
raise ValueError(f"Error parsing YAML: {str(e)}")
except Exception as e:
raise ValueError(f"Error validating configuration: {str(e)}")
@retry(
stop=stop_after_attempt(3),
wait=wait_exponential(multiplier=1, min=4, max=10),
retry=retry_if_exception_type((ConnectionError, TimeoutError)),
before_sleep=lambda retry_state: logger.info(
f"Retrying after error: {retry_state.outcome.exception()}"
),
)
def create_agent_with_retry(
agent_config: Dict, model: LiteLLM
) -> Agent:
"""Create an agent with retry logic for handling transient failures."""
try:
validated_config = AgentConfig(**agent_config)
agent = Agent(
agent_name=validated_config.agent_name,
system_prompt=validated_config.system_prompt,
llm=model,
max_loops=validated_config.max_loops,
autosave=validated_config.autosave,
dashboard=validated_config.dashboard,
verbose=validated_config.verbose,
dynamic_temperature_enabled=validated_config.dynamic_temperature_enabled,
saved_state_path=validated_config.saved_state_path,
user_name=validated_config.user_name,
retry_attempts=validated_config.retry_attempts,
context_length=validated_config.context_length,
return_step_meta=validated_config.return_step_meta,
output_type=validated_config.output_type,
auto_generate_prompt=validated_config.auto_generate_prompt,
artifacts_on=validated_config.artifacts_on,
artifacts_file_extension=validated_config.artifacts_file_extension,
artifacts_output_path=validated_config.artifacts_output_path,
)
return agent
except Exception as e:
logger.error(
f"Error creating agent {agent_config.get('agent_name', 'unknown')}: {str(e)}"
)
raise
def create_agents_from_yaml(
model: Callable = None,
yaml_file: str = "agents.yaml",
yaml_string: str = None,
return_type: str = "auto",
) -> Union[
SwarmRouter,
Agent,
List[Agent],
Tuple[Union[SwarmRouter, Agent], List[Agent]],
List[Dict[str, Any]],
]:
"""
Create agents and/or SwarmRouter based on configurations defined in a YAML file or string.
"""
agents = []
task_results = []
swarm_router = None
try:
# Load and validate configuration
config = load_yaml_safely(yaml_file, yaml_string)
# Create agents with retry logic
for agent_config in config["agents"]:
logger.info(
f"Creating agent: {agent_config['agent_name']}"
)
if "model_name" in agent_config:
model_instance = LiteLLM(
model_name=agent_config["model_name"]
)
else:
model_name = "gpt-4o"
model_instance = LiteLLM(model_name=model_name)
agent = create_agent_with_retry(
agent_config, model_instance
)
logger.info(
f"Agent {agent_config['agent_name']} created successfully."
)
agents.append(agent)
# Create SwarmRouter if specified
if "swarm_architecture" in config:
try:
swarm_config = SwarmConfig(
**config["swarm_architecture"]
)
swarm_router = SwarmRouter(
name=swarm_config.name,
description=swarm_config.description,
max_loops=swarm_config.max_loops,
agents=agents,
swarm_type=swarm_config.swarm_type,
task=swarm_config.task,
flow=swarm_config.flow,
autosave=swarm_config.autosave,
return_json=swarm_config.return_json,
rules=swarm_config.rules,
)
logger.info(
f"SwarmRouter '{swarm_config.name}' created successfully."
)
except Exception as e:
logger.error(f"Error creating SwarmRouter: {str(e)}")
raise ValueError(
f"Failed to create SwarmRouter: {str(e)}"
)
# Handle return types with improved error checking
valid_return_types = {
"auto",
"swarm",
"agents",
"both",
"tasks",
"run_swarm",
}
if return_type not in valid_return_types:
raise ValueError(
f"Invalid return_type. Must be one of: {valid_return_types}"
)
if return_type == "run_swarm" or "swarm":
if not swarm_router:
raise ValueError(
"Cannot run swarm: SwarmRouter not created."
)
try:
return swarm_router.run(
config["swarm_architecture"]["task"]
)
except Exception as e:
logger.error(f"Error running SwarmRouter: {str(e)}")
raise
# Return appropriate type based on configuration
if return_type == "auto":
return (
swarm_router
if swarm_router
else (agents[0] if len(agents) == 1 else agents)
)
elif return_type == "swarm":
return (
swarm_router
if swarm_router
else (agents[0] if len(agents) == 1 else agents)
)
elif return_type == "agents":
return agents[0] if len(agents) == 1 else agents
elif return_type == "both":
return (
swarm_router
if swarm_router
else agents[0] if len(agents) == 1 else agents
), agents
elif return_type == "tasks":
return task_results
except Exception as e:
logger.error(
f"Critical error in create_agents_from_yaml: {str(e)}"
)
raise
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