Spaces:
Sleeping
Sleeping
File size: 10,039 Bytes
5fc6c27 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
# coding: utf-8
# Copyright (c) 2025 inclusionAI.
import copy
import json
import traceback
from typing import Dict, Any, List, Union
from examples.tools.common import Agents
from aworld.core.agent.base import AgentResult
from aworld.agents.llm_agent import Agent
from aworld.models.llm import call_llm_model
from aworld.config.conf import AgentConfig, ConfigDict
from aworld.core.common import Observation, ActionModel
from aworld.logs.util import logger
from examples.plan_execute.prompts import *
from examples.plan_execute.utils import extract_pattern
class ExecuteAgent(Agent):
def __init__(self, conf: Union[Dict[str, Any], ConfigDict, AgentConfig], **kwargs):
super(ExecuteAgent, self).__init__(conf, **kwargs)
def id(self) -> str:
return Agents.EXECUTE.value
def reset(self, options: Dict[str, Any]):
"""Execute agent reset need query task as input."""
super().reset(options)
self.system_prompt = execute_system_prompt.format(task=self.task)
self.step_reset = False
async def async_policy(self, observation: Observation, info: Dict[str, Any] = {}, **kwargs) -> Union[
List[ActionModel], None]:
await self.async_desc_transform()
return self._common(observation, info)
def policy(self,
observation: Observation,
info: Dict[str, Any] = None,
**kwargs) -> List[ActionModel] | None:
self.desc_transform()
return self._common(observation, info)
def _common(self, observation, info):
self._finished = False
content = observation.content
llm_result = None
## build input of llm
input_content = [
{'role': 'system', 'content': self.system_prompt},
]
for traj in self.trajectory:
# Handle multiple messages in content
if isinstance(traj[0].content, list):
input_content.extend(traj[0].content)
else:
input_content.append(traj[0].content)
if traj[-1].tool_calls is not None:
input_content.append(
{'role': 'assistant', 'content': '', 'tool_calls': traj[-1].tool_calls})
else:
input_content.append({'role': 'assistant', 'content': traj[-1].content})
if content is None:
content = observation.action_result[0].error
if not self.trajectory:
new_messages = [{"role": "user", "content": content}]
input_content.extend(new_messages)
else:
# Collect existing tool_call_ids from input_content
existing_tool_call_ids = {
msg.get("tool_call_id") for msg in input_content
if msg.get("role") == "tool" and msg.get("tool_call_id")
}
new_messages = []
for traj in self.trajectory:
if traj[-1].tool_calls is not None:
# Handle multiple tool calls
for tool_call in traj[-1].tool_calls:
# Only add if this tool_call_id doesn't exist in input_content
if tool_call.id not in existing_tool_call_ids:
new_messages.append({
"role": "tool",
"content": content,
"tool_call_id": tool_call.id
})
if new_messages:
input_content.extend(new_messages)
else:
input_content.append({"role": "user", "content": content})
# Validate tool_calls and tool messages pairing
assistant_tool_calls = []
tool_responses = []
for msg in input_content:
if msg.get("role") == "assistant" and msg.get("tool_calls"):
assistant_tool_calls.extend(msg["tool_calls"])
elif msg.get("role") == "tool":
tool_responses.append(msg.get("tool_call_id"))
# Check if all tool_calls have corresponding responses
tool_call_ids = {call.id for call in assistant_tool_calls}
tool_response_ids = set(tool_responses)
if tool_call_ids != tool_response_ids:
missing_calls = tool_call_ids - tool_response_ids
extra_responses = tool_response_ids - tool_call_ids
error_msg = f"Tool calls and responses mismatch. Missing responses for tool_calls: {missing_calls}, Extra responses: {extra_responses}"
logger.error(error_msg)
raise ValueError(error_msg)
tool_calls = []
try:
llm_result = call_llm_model(self.llm, input_content, model=self.model_name,
tools=self.tools, temperature=0)
logger.info(f"Execute response: {llm_result.message}")
res = self.response_parse(llm_result)
content = res.actions[0].policy_info
tool_calls = llm_result.tool_calls
except Exception as e:
logger.warning(traceback.format_exc())
finally:
if llm_result:
ob = copy.deepcopy(observation)
ob.content = new_messages
self.trajectory.append((ob, info, llm_result))
else:
logger.warning("no result to record!")
res = []
if tool_calls:
for tool_call in tool_calls:
tool_action_name: str = tool_call.function.name
if not tool_action_name:
continue
names = tool_action_name.split("__")
tool_name = names[0]
action_name = '__'.join(names[1:]) if len(names) > 1 else ''
params = json.loads(tool_call.function.arguments)
res.append(ActionModel(agent_name=Agents.EXECUTE.value,
tool_name=tool_name,
action_name=action_name,
params=params))
if res:
res[0].policy_info = content
self._finished = False
elif content:
policy_info = extract_pattern(content, "final_answer")
if policy_info:
res.append(ActionModel(agent_name=Agents.EXECUTE.value,
policy_info=policy_info))
self._finished = True
else:
res.append(ActionModel(agent_name=Agents.EXECUTE.value,
policy_info=content))
logger.info(f">>> execute result: {res}")
result = AgentResult(actions=res,
current_state=None)
return result.actions
class PlanAgent(Agent):
def __init__(self, conf: Union[Dict[str, Any], ConfigDict, AgentConfig], **kwargs):
super(PlanAgent, self).__init__(conf, **kwargs)
def id(self) -> str:
return Agents.PLAN.value
def reset(self, options: Dict[str, Any]):
"""Execute agent reset need query task as input."""
super().reset(options)
self.system_prompt = plan_system_prompt.format(task=self.task)
self.done_prompt = plan_done_prompt.format(task=self.task)
self.postfix_prompt = plan_postfix_prompt.format(task=self.task)
self.first_prompt = init_prompt
self.first = True
self.step_reset = False
async def async_policy(self, observation: Observation, info: Dict[str, Any] = {}, **kwargs) -> Union[
List[ActionModel], None]:
await self.async_desc_transform()
return self._common(observation, info)
def policy(self,
observation: Observation,
info: Dict[str, Any] = None,
**kwargs) -> List[ActionModel] | None:
self._finished = False
self.desc_transform()
return self._common(observation, info)
def _common(self, observation, info):
llm_result = None
input_content = [
{'role': 'system', 'content': self.system_prompt},
]
# build input of llm based history
for traj in self.trajectory:
input_content.append({'role': 'user', 'content': traj[0].content})
# plan agent no tool to call, use content
input_content.append({'role': 'assistant', 'content': traj[-1].content})
message = observation.content
if self.first_prompt:
message = self.first_prompt
self.first_prompt = None
input_content.append({"role": "user", "content": message})
try:
llm_result = call_llm_model(self.llm, messages=input_content, model=self.model_name)
logger.info(f"Plan response: {llm_result.message}")
except Exception as e:
logger.warning(traceback.format_exc())
raise e
finally:
if llm_result:
ob = copy.deepcopy(observation)
ob.content = message
self.trajectory.append((ob, info, llm_result))
else:
logger.warning("no result to record!")
res = self.response_parse(llm_result)
content = res.actions[0].policy_info
if "TASK_DONE" not in content:
content += self.done_prompt
else:
# The task is done, and the assistant agent need to give the final answer about the original task
content += self.postfix_prompt
if not self.first:
self._finished = True
self.first = False
logger.info(f">>> plan result: {content}")
result = AgentResult(actions=[ActionModel(agent_name=Agents.PLAN.value,
tool_name=Agents.EXECUTE.value,
policy_info=content)],
current_state=None)
return result.actions
|