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# coding: utf-8
# Copyright (c) 2025 inclusionAI.
import json
import time
import traceback
import aworld.trace as trace
from typing import List, Dict, Any, Tuple
from aworld.config.conf import ToolConfig
from aworld.core.agent.base import is_agent
from aworld.agents.llm_agent import Agent
from aworld.core.common import Observation, ActionModel, ActionResult
from aworld.core.context.base import Context
from aworld.core.event.base import Message
from aworld.core.tool.base import ToolFactory, Tool, AsyncTool
from aworld.core.tool.tool_desc import is_tool_by_name
from aworld.core.task import Task, TaskResponse
from aworld.logs.util import logger, color_log, Color, trace_logger
from aworld.models.model_response import ToolCall
from aworld.output.base import StepOutput, ToolResultOutput
from aworld.runners.task_runner import TaskRunner
from aworld.runners.utils import endless_detect
from aworld.sandbox import Sandbox
from aworld.tools.utils import build_observation
from aworld.utils.common import override_in_subclass
from aworld.utils.json_encoder import NumpyEncoder
def action_result_transform(message: Message, sandbox: Sandbox) -> Tuple[Observation, float, bool, bool, dict]:
action_results = message.payload
result: ActionResult = action_results[-1]
# ignore image, dom_tree attribute, need to process them from action_results in the agent.
return build_observation(container_id=sandbox.sandbox_id,
observer=result.tool_name,
ability=result.action_name,
content=result.content,
action_result=action_results), 1.0, result.is_done, result.is_done, {}
class WorkflowRunner(TaskRunner):
def __init__(self, task: Task, *args, **kwargs):
super().__init__(task=task, *args, **kwargs)
async def do_run(self, context: Context = None) -> TaskResponse:
self.max_steps = self.conf.get("max_steps", 100)
resp = await self._do_run(context)
self._task_response = resp
return resp
async def _do_run(self, context: Context = None) -> TaskResponse:
"""Multi-agent sequence general process workflow.
NOTE: Use the agent's finished state(no tool calls) to control the inner loop.
Args:
observation: Observation based on env
info: Extend info by env
"""
observation = self.observation
if not observation:
raise RuntimeError("no observation, check run process")
start = time.time()
msg = None
response = None
# Use trace.span to record the entire task execution process
with trace.span(f"task_execution_{self.task.id}", attributes={
"task_id": self.task.id,
"task_name": self.task.name,
"start_time": start
}) as task_span:
try:
response = await self._common_process(task_span)
except Exception as err:
logger.error(f"Runner run failed, err is {traceback.format_exc()}")
finally:
await self.outputs.mark_completed()
color_log(f"task token usage: {self.context.token_usage}",
color=Color.pink,
logger_=trace_logger)
for _, tool in self.tools.items():
if isinstance(tool, AsyncTool):
await tool.close()
else:
tool.close()
task_span.set_attributes({
"end_time": time.time(),
"duration": time.time() - start,
"error": msg
})
# todo sandbox cleanup
if self.swarm and hasattr(self.swarm, 'agents') and self.swarm.agents:
for agent_name, agent in self.swarm.agents.items():
try:
if hasattr(agent, 'sandbox') and agent.sandbox:
await agent.sandbox.cleanup()
except Exception as e:
logger.warning(f"call_driven_runner Failed to cleanup sandbox for agent {agent_name}: {e}")
return response
async def _common_process(self, task_span):
start = time.time()
step = 1
pre_agent_name = None
observation = self.observation
for idx, agent in enumerate(self.swarm.ordered_agents):
observation.from_agent_name = agent.id()
observations = [observation]
policy = None
cur_agent = agent
while step <= self.max_steps:
await self.outputs.add_output(
StepOutput.build_start_output(name=f"Step{step}", step_num=step))
terminated = False
observation = self.swarm.action_to_observation(policy, observations)
observation.from_agent_name = observation.from_agent_name or cur_agent.id()
if observation.to_agent_name and observation.to_agent_name != cur_agent.id():
cur_agent = self.swarm.agents.get(observation.to_agent_name)
exp_id = self._get_step_span_id(step, cur_agent.id())
with trace.span(f"step_execution_{exp_id}") as step_span:
try:
step_span.set_attributes({
"exp_id": exp_id,
"task_id": self.task.id,
"task_name": self.task.name,
"trace_id": trace.get_current_span().get_trace_id(),
"step": step,
"agent_id": cur_agent.id(),
"pre_agent": pre_agent_name,
"observation": json.dumps(observation.model_dump(exclude_none=True),
ensure_ascii=False,
cls=NumpyEncoder)
})
except:
pass
pre_agent_name = cur_agent.id()
if not override_in_subclass('async_policy', cur_agent.__class__, Agent):
message = cur_agent.run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False),
exp_id=exp_id)
else:
message = await cur_agent.async_run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream",
False),
exp_id=exp_id)
policy = message.payload
step_span.set_attribute("actions",
json.dumps([action.model_dump() for action in policy],
ensure_ascii=False))
observation.content = None
color_log(f"{cur_agent.id()} policy: {policy}")
if not policy:
logger.warning(f"current agent {cur_agent.id()} no policy to use.")
await self.outputs.add_output(
StepOutput.build_failed_output(name=f"Step{step}",
step_num=step,
data=f"current agent {cur_agent.id()} no policy to use.")
)
await self.outputs.mark_completed()
task_span.set_attributes({
"end_time": time.time(),
"duration": time.time() - start,
"status": "failed",
"error": f"current agent {cur_agent.id()} no policy to use."
})
return TaskResponse(msg=f"current agent {cur_agent.id()} no policy to use.",
answer="",
success=False,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage)
if is_agent(policy[0]):
status, info = await self._agent(agent, observation, policy, step)
if status == 'normal':
if info:
observations.append(observation)
elif status == 'break':
observation = self.swarm.action_to_observation(policy, observations)
if idx == len(self.swarm.ordered_agents) - 1:
return TaskResponse(
answer=observation.content,
success=True,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage
)
break
elif status == 'return':
await self.outputs.add_output(
StepOutput.build_finished_output(name=f"Step{step}", step_num=step)
)
info.time_cost = (time.time() - start)
task_span.set_attributes({
"end_time": time.time(),
"duration": info.time_cost,
"status": "success"
})
return info
elif is_tool_by_name(policy[0].tool_name):
# todo sandbox
msg, reward, terminated = await self._tool_call(policy, observations, step,
cur_agent)
step_span.set_attribute("reward", reward)
else:
logger.warning(f"Unrecognized policy: {policy[0]}")
await self.outputs.add_output(
StepOutput.build_failed_output(name=f"Step{step}",
step_num=step,
data=f"Unrecognized policy: {policy[0]}, need to check prompt or agent / tool.")
)
await self.outputs.mark_completed()
task_span.set_attributes({
"end_time": time.time(),
"duration": time.time() - start,
"status": "failed",
"error": f"Unrecognized policy: {policy[0]}, need to check prompt or agent / tool."
})
return TaskResponse(
msg=f"Unrecognized policy: {policy[0]}, need to check prompt or agent / tool.",
answer="",
success=False,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage
)
await self.outputs.add_output(
StepOutput.build_finished_output(name=f"Step{step}",
step_num=step, )
)
step += 1
if terminated and agent.finished:
logger.info(f"{agent.id()} finished")
if idx == len(self.swarm.ordered_agents) - 1:
return TaskResponse(
answer=observations[-1].content,
success=True,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage
)
break
async def _agent(self, agent: Agent, observation: Observation, policy: List[ActionModel], step: int):
# only one agent, and get agent from policy
policy_for_agent = policy[0]
agent_name = policy_for_agent.tool_name
if not agent_name:
agent_name = policy_for_agent.agent_name
cur_agent: Agent = self.swarm.agents.get(agent_name)
if not cur_agent:
raise RuntimeError(f"Can not find {agent_name} agent in swarm.")
status = "normal"
if cur_agent.id() == agent.id():
# Current agent is entrance agent, means need to exit to the outer loop
logger.info(f"{cur_agent.id()} exit the loop")
status = "break"
return status, None
if agent.handoffs and agent_name not in agent.handoffs:
# Unable to hand off, exit to the outer loop
status = "return"
return status, TaskResponse(msg=f"Can not handoffs {agent_name} agent ",
answer=observation.content,
success=False,
id=self.task.id,
usage=self.context.token_usage)
# Check if current agent done
if cur_agent.finished:
cur_agent._finished = False
logger.info(f"{cur_agent.id()} agent be be handed off, so finished state reset to False.")
con = policy_for_agent.policy_info
if policy_for_agent.params and 'content' in policy_for_agent.params:
con = policy_for_agent.params['content']
if observation:
observation.content = con
else:
observation = Observation(content=con)
return status, observation
return status, None
# todo sandbox
async def _tool_call(self, policy: List[ActionModel], observations: List[Observation], step: int, agent: Agent):
msg = None
terminated = False
# group action by tool name
tool_mapping = dict()
reward = 0.0
# Directly use or use tools after creation.
for act in policy:
if not self.tools or (self.tools and act.tool_name not in self.tools):
# dynamic only use default config in module.
conf = self.tools_conf.get(act.tool_name)
tool = ToolFactory(act.tool_name, conf=conf, asyn=conf.use_async if conf else False)
if isinstance(tool, Tool):
tool.reset()
elif isinstance(tool, AsyncTool):
await tool.reset()
tool_mapping[act.tool_name] = []
self.tools[act.tool_name] = tool
if act.tool_name not in tool_mapping:
tool_mapping[act.tool_name] = []
tool_mapping[act.tool_name].append(act)
for tool_name, action in tool_mapping.items():
# Execute action using browser tool and unpack all return values
if isinstance(self.tools[tool_name], Tool):
message = self.tools[tool_name].step(action)
elif isinstance(self.tools[tool_name], AsyncTool):
# todo sandbox
message = await self.tools[tool_name].step(action, agent=agent)
else:
logger.warning(f"Unsupported tool type: {self.tools[tool_name]}")
continue
observation, reward, terminated, _, info = message.payload
# observation, reward, terminated, _, info = action_result_transform(message, sandbox=None)
observations.append(observation)
for i, item in enumerate(action):
tool_output = ToolResultOutput(
tool_type=tool_name,
tool_name=item.tool_name,
data=observation.content,
origin_tool_call=ToolCall.from_dict({
"function": {
"name": item.action_name,
"arguments": item.params,
}
})
)
await self.outputs.add_output(tool_output)
# Check if there's an exception in info
if info.get("exception"):
color_log(f"Step {step} failed with exception: {info['exception']}", color=Color.red)
msg = f"Step {step} failed with exception: {info['exception']}"
logger.info(f"step: {step} finished by tool action: {action}.")
log_ob = Observation(content='' if observation.content is None else observation.content,
action_result=observation.action_result)
trace_logger.info(f"{tool_name} observation: {log_ob}", color=Color.green)
return msg, reward, terminated
def _get_step_span_id(self, step, cur_agent_name):
key = (step, cur_agent_name)
if key not in self.step_agent_counter:
self.step_agent_counter[key] = 0
else:
self.step_agent_counter[key] += 1
exp_index = self.step_agent_counter[key]
return f"{self.task.id}_{step}_{cur_agent_name}_{exp_index}"
class LoopWorkflowRunner(WorkflowRunner):
async def _do_run(self, context: Context = None) -> TaskResponse:
observation = self.observation
if not observation:
raise RuntimeError("no observation, check run process")
start = time.time()
step = 1
msg = None
# Use trace.span to record the entire task execution process
with trace.span(f"task_execution_{self.task.id}", attributes={
"task_id": self.task.id,
"task_name": self.task.name,
"start_time": start
}) as task_span:
try:
for i in range(self.max_steps):
await self._common_process(task_span)
step += 1
except Exception as err:
logger.error(f"Runner run failed, err is {traceback.format_exc()}")
finally:
await self.outputs.mark_completed()
color_log(f"task token usage: {self.context.token_usage}",
color=Color.pink,
logger_=trace_logger)
for _, tool in self.tools.items():
if isinstance(tool, AsyncTool):
await tool.close()
else:
tool.close()
task_span.set_attributes({
"end_time": time.time(),
"duration": time.time() - start,
"error": msg
})
return TaskResponse(msg=msg,
answer=observation.content,
success=True if not msg else False,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage)
class HandoffRunner(TaskRunner):
def __init__(self, task: Task, *args, **kwargs):
super().__init__(task=task, *args, **kwargs)
async def do_run(self, context: Context = None) -> TaskResponse:
resp = await self._do_run(context)
self._task_response = resp
return resp
async def _do_run(self, context: Context = None) -> TaskResponse:
"""Multi-agent general process based on handoff.
NOTE: Use the agent's finished state to control the loop, so the agent must carefully set finished state.
Args:
context: Context of runner.
"""
start = time.time()
observation = self.observation
info = dict()
step = 0
max_steps = self.conf.get("max_steps", 100)
results = []
swarm_resp = None
self.loop_detect = []
# Use trace.span to record the entire task execution process
with trace.span(f"task_execution_{self.task.id}", attributes={
"task_id": self.task.id,
"task_name": self.task.name,
"start_time": start
}) as task_span:
try:
while step < max_steps:
# Loose protocol
result_dict = await self._process(observation=observation, info=info)
results.append(result_dict)
swarm_resp = result_dict.get("response")
logger.info(f"Step: {step} response:\n {result_dict}")
step += 1
if self.swarm.finished or endless_detect(self.loop_detect,
self.endless_threshold,
self.swarm.communicate_agent.id()):
logger.info("task done!")
break
if not swarm_resp:
logger.warning(f"Step: {step} swarm no valid response")
break
observation = result_dict.get("observation")
if not observation:
observation = Observation(content=swarm_resp)
else:
observation.content = swarm_resp
time_cost = time.time() - start
if not results:
logger.warning("task no result!")
task_span.set_attributes({
"status": "failed",
"error": f"task no result!"
})
return TaskResponse(msg=traceback.format_exc(),
answer='',
success=False,
id=self.task.id,
time_cost=time_cost,
usage=self.context.token_usage)
answer = results[-1].get('observation').content if results[-1].get('observation') else swarm_resp
return TaskResponse(answer=answer,
success=True,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage)
except Exception as e:
logger.error(f"Task execution failed with error: {str(e)}\n{traceback.format_exc()}")
task_span.set_attributes({
"status": "failed",
"error": f"Task execution failed with error: {str(e)}\n{traceback.format_exc()}"
})
return TaskResponse(msg=traceback.format_exc(),
answer='',
success=False,
id=self.task.id,
time_cost=(time.time() - start),
usage=self.context.token_usage)
finally:
color_log(f"task token usage: {self.context.token_usage}",
color=Color.pink,
logger_=trace_logger)
for _, tool in self.tools.items():
if isinstance(tool, AsyncTool):
await tool.close()
else:
tool.close()
task_span.set_attributes({
"end_time": time.time(),
"duration": time.time() - start,
})
async def _process(self, observation, info) -> Dict[str, Any]:
if not self.swarm.initialized:
raise RuntimeError("swarm needs to use `reset` to init first.")
start = time.time()
step = 0
max_steps = self.conf.get("max_steps", 100)
self.swarm.cur_agent = self.swarm.communicate_agent
pre_agent_name = None
# use communicate agent every time
if override_in_subclass('async_policy', self.swarm.cur_agent.__class__, Agent):
message = self.swarm.cur_agent.run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
else:
message = await self.swarm.cur_agent.async_run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
self.loop_detect.append(self.swarm.cur_agent.id())
policy = message.payload
if not policy:
logger.warning(f"current agent {self.swarm.cur_agent.id()} no policy to use.")
exp_id = self._get_step_span_id(step, self.swarm.cur_agent.id())
with trace.span(f"step_execution_{exp_id}") as step_span:
step_span.set_attributes({
"exp_id": exp_id,
"task_id": self.task.id,
"task_name": self.task.name,
"trace_id": trace.get_current_span().get_trace_id(),
"step": step,
"agent_id": self.swarm.cur_agent.id(),
"pre_agent": pre_agent_name,
"observation": json.dumps(observation.model_dump(exclude_none=True),
ensure_ascii=False,
cls=NumpyEncoder),
"actions": json.dumps([action.model_dump() for action in policy], ensure_ascii=False)
})
return {"msg": f"current agent {self.swarm.cur_agent.id()} no policy to use.",
"steps": step,
"success": False,
"time_cost": (time.time() - start)}
color_log(f"{self.swarm.cur_agent.id()} policy: {policy}")
msg = None
response = None
return_entry = False
cur_agent = None
cur_observation = observation
finished = False
try:
while step < max_steps:
terminated = False
exp_id = self._get_step_span_id(step, self.swarm.cur_agent.id())
with trace.span(f"step_execution_{exp_id}") as step_span:
try:
step_span.set_attributes({
"exp_id": exp_id,
"task_id": self.task.id,
"task_name": self.task.name,
"trace_id": trace.get_current_span().get_trace_id(),
"step": step,
"agent_id": self.swarm.cur_agent.id(),
"pre_agent": pre_agent_name,
"observation": json.dumps(cur_observation.model_dump(exclude_none=True),
ensure_ascii=False,
cls=NumpyEncoder),
"actions": json.dumps([action.model_dump() for action in policy], ensure_ascii=False)
})
except:
pass
if is_agent(policy[0]):
status, info, ob = await self._social_agent(policy, step)
if status == 'normal':
self.swarm.cur_agent = self.swarm.agents.get(policy[0].agent_name)
policy = info
cur_observation = ob
# clear observation
observation = None
elif is_tool_by_name(policy[0].tool_name):
status, terminated, info = await self._social_tool_call(policy, step)
if status == 'normal':
observation = info
cur_observation = observation
else:
logger.warning(f"Unrecognized policy: {policy[0]}")
return {"msg": f"Unrecognized policy: {policy[0]}, need to check prompt or agent / tool.",
"response": "",
"steps": step,
"success": False}
if status == 'break':
return_entry = info
break
elif status == 'return':
return info
step += 1
pre_agent_name = self.swarm.cur_agent.id()
if terminated and self.swarm.cur_agent.finished:
logger.info(f"{self.swarm.cur_agent.id()} finished")
break
if observation:
if cur_agent is None:
cur_agent = self.swarm.cur_agent
if not override_in_subclass('async_policy', cur_agent.__class__, Agent):
message = cur_agent.run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
else:
message = await cur_agent.async_run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
policy = message.payload
color_log(f"{cur_agent.id()} policy: {policy}")
if policy:
response = policy[0].policy_info if policy[0].policy_info else policy[0].action_name
# All agents or tools have completed their tasks
if all(agent.finished for _, agent in self.swarm.agents.items()) or (all(
tool.finished for _, tool in self.tools.items()) and len(self.swarm.agents) == 1):
logger.info("entry agent finished, swarm process finished.")
finished = True
if return_entry and not finished:
# Return to the entrance, reset current agent finished state
self.swarm.cur_agent._finished = False
return {"steps": step,
"response": response,
"observation": observation,
"msg": msg,
"success": True if not msg else False}
except Exception as e:
logger.error(f"Task execution failed with error: {str(e)}\n{traceback.format_exc()}")
return {
"msg": str(e),
"response": "",
"traceback": traceback.format_exc(),
"steps": step,
"success": False
}
async def _social_agent(self, policy: List[ActionModel], step):
# only one agent, and get agent from policy
policy_for_agent = policy[0]
agent_name = policy_for_agent.tool_name
if not agent_name:
agent_name = policy_for_agent.agent_name
cur_agent: Agent = self.swarm.agents.get(agent_name)
if not cur_agent:
raise RuntimeError(f"Can not find {agent_name} agent in swarm.")
if cur_agent.id() == self.swarm.communicate_agent.id() or cur_agent.id() == self.swarm.cur_agent.id():
# Current agent is entrance agent, means need to exit to the outer loop
logger.info(f"{cur_agent.id()} exit to the outer loop")
return 'break', True, None
if self.swarm.cur_agent.handoffs and agent_name not in self.swarm.cur_agent.handoffs:
# Unable to hand off, exit to the outer loop
return "return", {"msg": f"Can not handoffs {agent_name} agent "
f"by {cur_agent.id()} agent.",
"response": policy[0].policy_info if policy else "",
"steps": step,
"success": False}, None
# Check if current agent done
if cur_agent.finished:
cur_agent._finished = False
logger.info(f"{cur_agent.id()} agent be be handed off, so finished state reset to False.")
observation = Observation(content=policy_for_agent.policy_info)
self.loop_detect.append(cur_agent.id())
if cur_agent.step_reset:
cur_agent.reset({"task": observation.content,
"tool_names": cur_agent.tool_names,
"agent_names": cur_agent.handoffs,
"mcp_servers": cur_agent.mcp_servers})
if not override_in_subclass('async_policy', cur_agent.__class__, Agent):
message = cur_agent.run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
else:
message = await cur_agent.async_run(observation,
step=step,
outputs=self.outputs,
stream=self.conf.get("stream", False))
agent_policy = message.payload
if not agent_policy:
logger.warning(
f"{observation} can not get the valid policy in {policy_for_agent.agent_name}, exit task!")
return "return", {"msg": f"{policy_for_agent.agent_name} invalid policy",
"response": "",
"steps": step,
"success": False}, None
color_log(f"{cur_agent.id()} policy: {agent_policy}")
return 'normal', agent_policy, observation
async def _social_tool_call(self, policy: List[ActionModel], step: int):
observation = None
terminated = False
# group action by tool name
tool_mapping = dict()
# Directly use or use tools after creation.
for act in policy:
if not self.tools or (self.tools and act.tool_name not in self.tools):
# dynamic only use default config in module.
conf: ToolConfig = self.tools_conf.get(act.tool_name)
tool = ToolFactory(act.tool_name, conf=conf, asyn=conf.use_async if conf else False)
if isinstance(tool, Tool):
tool.reset()
elif isinstance(tool, AsyncTool):
await tool.reset()
tool_mapping[act.tool_name] = []
self.tools[act.tool_name] = tool
if act.tool_name not in tool_mapping:
tool_mapping[act.tool_name] = []
tool_mapping[act.tool_name].append(act)
for tool_name, action in tool_mapping.items():
# Execute action using browser tool and unpack all return values
if isinstance(self.tools[tool_name], Tool):
message = self.tools[tool_name].step(action)
elif isinstance(self.tools[tool_name], AsyncTool):
message = await self.tools[tool_name].step(action)
else:
logger.warning(f"Unsupported tool type: {self.tools[tool_name]}")
continue
observation, reward, terminated, _, info = message.payload
for i, item in enumerate(action):
tool_output = ToolResultOutput(data=observation.content, origin_tool_call=ToolCall.from_dict({
"function": {
"name": item.action_name,
"arguments": item.params,
}
}))
await self.outputs.add_output(tool_output)
# Check if there's an exception in info
if info.get("exception"):
color_log(f"Step {step} failed with exception: {info['exception']}", color=Color.red)
logger.info(f"step: {step} finished by tool action {action}.")
log_ob = Observation(content='' if observation.content is None else observation.content,
action_result=observation.action_result)
color_log(f"{tool_name} observation: {log_ob}", color=Color.green)
# The tool results give itself, exit; give to other agents, continue
tmp_name = policy[0].agent_name
if self.swarm.cur_agent.id() == self.swarm.communicate_agent.id() and (
len(self.swarm.agents) == 1 or tmp_name is None or self.swarm.cur_agent.id() == tmp_name):
return "break", terminated, True
elif policy[0].agent_name:
policy_for_agent = policy[0]
agent_name = policy_for_agent.agent_name
if not agent_name:
agent_name = policy_for_agent.tool_name
cur_agent: Agent = self.swarm.agents.get(agent_name)
if not cur_agent:
raise RuntimeError(f"Can not find {agent_name} agent in swarm.")
if self.swarm.cur_agent.handoffs and agent_name not in self.swarm.cur_agent.handoffs:
# Unable to hand off, exit to the outer loop
return "return", {"msg": f"Can not handoffs {agent_name} agent "
f"by {cur_agent.id()} agent.",
"response": policy[0].policy_info if policy else "",
"steps": step,
"success": False}
# Check if current agent done
if cur_agent.finished:
cur_agent._finished = False
logger.info(f"{cur_agent.id()} agent be be handed off, so finished state reset to False.")
return "normal", terminated, observation
def _get_step_span_id(self, step, cur_agent_name):
key = (step, cur_agent_name)
if key not in self.step_agent_counter:
self.step_agent_counter[key] = 0
else:
self.step_agent_counter[key] += 1
exp_index = self.step_agent_counter[key]
return f"{self.task.id}_{step}_{cur_agent_name}_{exp_index}"
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