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import importlib
__all__ = ['AgentCreator']
class AgentCreator():
dependencies = ["ollama==0.4.7",
"pydantic==2.11.1",
"pydantic_core==2.33.0"]
inputSchema = {
"name": "AgentCreator",
"description": "Creates an AI agent for you. Please make sure to invoke the created agent using the AskAgent tool.",
"parameters": {
"type": "object",
"properties":{
"agent_name": {
"type": "string",
"description": "Name of the AI agent that is to be created. This name cannot have spaces or special characters. It should be a single word.",
},
"base_model": {
"type": "string",
"description": "A base model from which the new agent mode is to be created. Available models are: llama3.2, mistral"
},
"system_prompt": {
"type": "string",
"description": "This is the system prompt that will be used to create the agent. It should be a string that describes the role of the agent and its capabilities."
},
"description": {
"type": "string",
"description": "Description of the agent. This is a string that describes the agent and its capabilities. It should be a single line description.",
},
},
"required": ["agent_name", "base_model", "system_prompt", "description"],
},
"creates": {
"selector": "base_model",
"types": {
"llama3.2":{
"description": "3 Billion parameter model",
"create_cost": 10,
"invoke_cost": 20,
},
"mistral":{
"description": "7 Billion parameter model",
"create_cost": 20,
"invoke_cost": 50,
}
}
}
}
def does_agent_exist(self, agent_name):
ollama = importlib.import_module("ollama")
all_agents = [a.model for a in ollama.list().models]
if agent_name in all_agents or f'{agent_name}:latest' in all_agents:
return True
return False
def run(self, **kwargs):
print("Running Agent Creator")
agent_name = kwargs.get("agent_name")
base_model = kwargs.get("base_model")
system_prompt = kwargs.get("system_prompt")
ollama = importlib.import_module("ollama")
json = importlib.import_module("json")
if self.does_agent_exist(agent_name):
return {
"status": "error",
"message": "Agent already exists",
"output": None
}
ollama_response = ollama.create(
model = agent_name,
from_ = base_model,
system = system_prompt,
stream = False
)
if "success" in ollama_response["status"]:
try:
with open("./models/models.json", "r", encoding="utf8") as f:
models = f.read()
models = json.loads(models)
models[agent_name] = {
"base_model": base_model,
"description": kwargs.get("description"),
"create_cost": self.inputSchema["creates"]["types"][base_model]["create_cost"],
"invoke_cost": self.inputSchema["creates"]["types"][base_model]["invoke_cost"],
}
with open("./models/models.json", "w", encoding="utf8") as f:
f.write(json.dumps(models, indent=4))
return {
"status": "success",
"message": "Agent successfully created",
"cost": self.inputSchema["creates"]["types"][base_model]["create_cost"],
}
except Exception as e:
print("Error while writing to models.json", e)
return {
"status": "error",
"message": f"Agent creation failed: {e}",
}
else:
return {
"status": "error",
"message": "Agent creation failed",
} |