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from cerebrum.llm.apis import llm_chat, llm_call_tool, llm_chat_with_json_output
from cerebrum.interface import AutoTool
import json
import os
def get_config():
from cerebrum.config.config_manager import config
return config
config = get_config()
import traceback
aios_kernel_url = config.get_kernel_url()
class ChatSupportAgent:
def __init__(self, agent_name):
self.agent_name = agent_name
self.messages = []
self.rounds = 0
self.log_to_board = False
self.config = self.load_config()
self.tools = [
tool.get_tool_call_format()
for tool in AutoTool.from_batch_preloaded(self.config["tools"])
]
def load_config(self):
script_path = os.path.abspath(__file__)
script_dir = os.path.dirname(script_path)
config_file = os.path.join(script_dir, "config.json")
with open(config_file, "r") as f:
config = json.load(f)
return config
def pre_select_tools(self, tool_names):
pre_selected_tools = []
for tool_name in tool_names:
for tool in self.tools:
if tool["function"]["name"] == tool_name:
pre_selected_tools.append(tool)
break
return pre_selected_tools
def build_system_instruction(self):
prefix = "".join(["".join(self.config["description"])])
plan_instruction = "".join(
[
f"You are given the available tools from the tool list: {json.dumps(self.tools)} to help you solve problems. ",
"Generate a plan with comprehensive yet minimal steps to fulfill the task. ",
"The plan must follow the json format as below: ",
"[",
'{"action_type": "action_type_value", "action": "action_value","tool_use": [tool_name1, tool_name2,...]}',
'{"action_type": "action_type_value", "action": "action_value", "tool_use": [tool_name1, tool_name2,...]}',
"...",
"]",
"In each step of the planned plan, identify tools to use and recognize no tool is necessary. ",
"Followings are some plan examples. ",
"[" "[",
'{"action_type": "tool_use", "action": "gather information from arxiv. ", "tool_use": ["arxiv"]},',
'{"action_type": "chat", "action": "write a summarization based on the gathered information. ", "tool_use": []}',
"];",
"[",
'{"action_type": "tool_use", "action": "gather information from arxiv. ", "tool_use": ["arxiv"]},',
'{"action_type": "chat", "action": "understand the current methods and propose ideas that can improve ", "tool_use": []}',
"]",
"]",
]
)
# if self.workflow_mode == "manual":
# self.messages.append({"role": "system", "content": prefix})
# else:
# assert self.workflow_mode == "automatic"
# self.messages.append({"role": "system", "content": prefix})
# self.messages.append({"role": "user", "content": plan_instruction})
def automatic_workflow(self):
for i in range(self.plan_max_fail_times):
response = llm_chat_with_json_output(llms=[{"name": "gpt-4o-mini", "backend": "openai"}],
messages=self.messages,
message_return_type="json"
)["response"]["response_message"]
try:
workflow = json.loads(response)
except:
workflow = None
self.rounds += 1
if workflow:
return workflow
else:
self.messages.append(
{
"role": "assistant",
"content": f"Fail {i+1} times to generate a valid plan. I need to regenerate a plan",
}
)
return None
def manual_workflow(self):
workflow = [
{
"action_type": "chat",
"action": "identify the user's technical issue or requirement",
"tool_use": []
},
{
"action_type": "chat",
"action": "search for troubleshooting steps for the identified issue",
"tool_use": []
},
{
"action_type": "chat",
"action": "organize the above information and summarize the solution",
"tool_use": []
}
]
return workflow
def run(self, task_input):
try:
# 1. System instruction / prompt setup
self.build_system_instruction()
self.messages.append({"role": "user", "content": task_input})
# 2. Decide workflow
workflow = None
if getattr(self, "workflow_mode", "manual") == "automatic":
workflow = self.automatic_workflow()
self.messages = self.messages[:1] # Reset context to avoid token bloat
else:
workflow = self.manual_workflow()
# 3. Append the workflow plan (optional thinking step)
self.messages.append({
"role": "user",
"content": f"[Thinking]: The workflow generated is {json.dumps(workflow)}. Follow it step by step."
})
# 4. Execute the workflow
if workflow:
final_result = ""
for i, step in enumerate(workflow):
action_type = step["action_type"]
action = step["action"]
tool_use = step["tool_use"]
# Append step prompt
step_prompt = f"Step {i+1}: {action}"
self.messages.append({"role": "user", "content": step_prompt})
# Prepare tools (if any)
selected_tools = self.pre_select_tools(tool_use) if tool_use else None
# Call appropriate LLM method
if action_type == "call_tool":
response = llm_call_tool(
agent_name=self.agent_name,
messages=self.messages,
llms=[{"name": "gpt-4o-mini", "backend": "openai"}],
tools=selected_tools,
base_url=aios_kernel_url
)["response"]
else:
response = llm_chat(
agent_name=self.agent_name,
messages=self.messages,
llms=[{"name": "gpt-4o-mini", "backend": "openai"}],
base_url=aios_kernel_url
)["response"]
self.rounds += 1
self.messages.append({"role": "assistant", "content": response.get("response_message", "")})
final_result = self.messages[-1]["content"]
return {
"agent_name": self.agent_name,
"result": final_result.strip(),
"rounds": self.rounds,
}
else:
return {
"agent_name": self.agent_name,
"result": "⚠️ No valid workflow was generated.",
"rounds": self.rounds,
}
except Exception as e:
print(f"[Agent Run Error]: {e}")
traceback.print_exc()
return {
"agent_name": self.agent_name,
"result": f"⚠️ Exception: {e}",
"rounds": self.rounds,
}
# def run(self, task_input):
# try:
# self.messages.append({"role": "user", "content": task_input})
# response = llm_chat(
# agent_name=self.agent_name,
# messages=self.messages,
# llms=[{"name": "gpt-4o-mini", "backend": "openai"}],
# base_url=aios_kernel_url
# )["response"]
# self.rounds += 1
# response_message = response.get("response_message", "⚠️ No response.")
# return {
# "agent_name": self.agent_name,
# "result": response_message.strip(),
# "rounds": self.rounds
# }
# except Exception as e:
# print(f"Error in run(): {e}")
# traceback.print_exc()
# return {
# "agent_name": self.agent_name,
# "result": f"⚠️ Error: {str(e)}",
# "rounds": self.rounds
# }
# def run(self, task_input):
# self.messages.append({"role": "user", "content": task_input})
# # Optional: auto-load tools from config
# tool_names = config.get("tools", []) # or just hardcode a small list
# tools = [
# tool.get_tool_call_format()
# for tool in AutoTool.from_batch_preloaded(tool_names)
# ] if tool_names else []
# # Run LLM with optional tool calls
# result = llm_chat(llms=[{"name": "gpt-4o-mini", "backend": "openai"}],
# agent_name=self.agent_name,
# messages=self.messages,
# base_url=aios_kernel_url
# )
# self.rounds += 1
# # # 🧠 Default to plain response if no tools triggered
# # if "response" not in result:
# # return {"error": "No response from LLM"}
# response_message = result.get("result", None)
# if not response_message:
# print("error", "No result in result")
# tool_calls = result["response"].get("tool_calls") or []
# tool_responses = []
# for call in tool_calls:
# tool_name = call["name"]
# params = call["parameters"]
# tool = AutoTool.from_preloaded(tool_name)
# response = tool.run(params)
# tool_responses.append((tool_name, response))
# response_message = result["response"].get("response_message", "")
# full_result = response_message + "\n\n".join(f"[{name}]: {resp}" for name, resp in tool_responses)
# return {
# "agent_name": self.agent_name,
# "result": full_result.strip(),
# "rounds": self.rounds,
# } |