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feat(agent): Preventing the model from looping output and repeatedly searching web pages #94

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Preventing the model from looping output and repeatedly searching web…
mmltt-star Nov 5, 2025
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mmltt-star Nov 5, 2025
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135 changes: 125 additions & 10 deletions src/core/orchestrator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from typing import Any, Optional
import importlib
from config.agent_prompts.base_agent_prompt import BaseAgentPrompt

from collections import defaultdict
from omegaconf import DictConfig


Expand Down Expand Up @@ -124,6 +124,45 @@ def __init__(
):
self.sub_agent_llm_client.task_log = task_log

# Record used subtask / q / Query
self.max_repeat_queries = 3
self.used_queries = {}

def _get_query_from_tool_call(
self, tool_name: str, arguments: dict
) -> Optional[str]:
"""
Extracts the query from tool call arguments based on tool_name.
Supports google_search, wiki_get_page_content, search_wiki_revision, search_archived_webpage, and scrape_website.
"""
if tool_name == "google_search":
return "q:" + arguments.get("q")
elif tool_name == "wiki_get_page_content":
return "entity:" + arguments.get("entity")
elif tool_name == "search_wiki_revision":
return (
"entity:"
+ arguments.get("entity")
+ "_year:"
+ str(arguments.get("year"))
+ "_month:"
+ str(arguments.get("month"))
)
elif tool_name == "search_archived_webpage":
return (
"url:"
+ arguments.get("url")
+ "_year:"
+ str(arguments.get("year"))
+ "_month:"
+ str(arguments.get("month"))
+ "_day:"
+ str(arguments.get("day"))
)
elif tool_name == "scrape_website":
return "url:" + arguments.get("url")
return None

async def _handle_llm_call_with_logging(
self,
system_prompt,
Expand Down Expand Up @@ -430,6 +469,7 @@ async def run_sub_agent(
turn_count = 0
all_tool_results_content_with_id = []
task_failed = False # Track whether task failed
should_hard_stop = False

while turn_count < max_turns:
turn_count += 1
Expand Down Expand Up @@ -521,9 +561,36 @@ async def run_sub_agent(

call_start_time = time.time()
try:
tool_result = await self.sub_agent_tool_managers[
sub_agent_name
].execute_tool_call(server_name, tool_name, arguments)
query_str = self._get_query_from_tool_call(tool_name, arguments)
if query_str:
cache_name = sub_agent_name + "_" + tool_name
self.used_queries.setdefault(
cache_name, defaultdict(lambda: [0, ""])
)
count = self.used_queries[cache_name][query_str][0]
cache_result = self.used_queries[cache_name][query_str][1]
if count > 0:
tool_result = {
"server_name": server_name,
"tool_name": tool_name,
"result": f"{cache_result}.\nNotice: This query has already been used in previous {tool_name}. Please try a different query or keyword.",
}
if count >= self.max_repeat_queries:
should_hard_stop = True
self.used_queries[cache_name][query_str][0] += 1
else:
tool_result = await self.sub_agent_tool_managers[
sub_agent_name
].execute_tool_call(server_name, tool_name, arguments)
if "error" not in tool_result:
self.used_queries[cache_name][query_str][1] = (
tool_result["result"]
)
self.used_queries[cache_name][query_str][0] += 1
else:
tool_result = await self.sub_agent_tool_managers[
sub_agent_name
].execute_tool_call(server_name, tool_name, arguments)

call_end_time = time.time()
call_duration_ms = int((call_end_time - call_start_time) * 1000)
Expand Down Expand Up @@ -603,6 +670,15 @@ async def run_sub_agent(
message_history, all_tool_results_content_with_id, tool_calls_exceeded
)

if should_hard_stop:
task_failed = True
self.task_log.log_step(
"too_many_repeated_queries_in_sub_agent",
f"{self.max_repeat_queries} repeated queries in sub agent {sub_agent_name}, stopping the task",
"warning",
)
break

# Continue execution
logger.debug(
f"\n=== Sub Agent {sub_agent_name} Completed ({turn_count} turns) ==="
Expand Down Expand Up @@ -793,6 +869,7 @@ async def run_main_agent(
max_turns = sys.maxsize
turn_count = 0
task_failed = False # Track whether task failed
should_hard_stop = False
while turn_count < max_turns:
turn_count += 1
logger.debug(f"\n--- Main Agent Turn {turn_count} ---")
Expand Down Expand Up @@ -877,13 +954,42 @@ async def run_main_agent(
"result": sub_agent_result,
}
else:
tool_result = (
await self.main_agent_tool_manager.execute_tool_call(
server_name=server_name,
tool_name=tool_name,
arguments=arguments,
query_str = self._get_query_from_tool_call(tool_name, arguments)
if query_str:
cache_name = "main_" + tool_name
self.used_queries.setdefault(
cache_name, defaultdict(lambda: [0, ""])
)
count = self.used_queries[cache_name][query_str][0]
cache_result = self.used_queries[cache_name][query_str][1]
if count > 0:
tool_result = {
"server_name": server_name,
"tool_name": tool_name,
"result": f"{cache_result}.\nNotice: This query has already been used in previous {tool_name}. Please try a different query or keyword.",
}
if count >= self.max_repeat_queries:
should_hard_stop = True
self.used_queries[cache_name][query_str][0] += 1
else:
tool_result = await self.main_agent_tool_manager.execute_tool_call(
server_name=server_name,
tool_name=tool_name,
arguments=arguments,
)
if "error" not in tool_result:
self.used_queries[cache_name][query_str][1] = (
tool_result["result"]
)
self.used_queries[cache_name][query_str][0] += 1
else:
tool_result = (
await self.main_agent_tool_manager.execute_tool_call(
server_name=server_name,
tool_name=tool_name,
arguments=arguments,
)
)
)

call_end_time = time.time()
call_duration_ms = int((call_end_time - call_start_time) * 1000)
Expand Down Expand Up @@ -959,6 +1065,15 @@ async def run_main_agent(
message_history, all_tool_results_content_with_id, tool_calls_exceeded
)

if should_hard_stop:
task_failed = True
self.task_log.log_step(
"too_many_repeated_queries",
f"{self.max_repeat_queries} repeated queries, stopping the task",
"warning",
)
break

# Record main loop end
if turn_count >= max_turns:
if (
Expand Down
16 changes: 16 additions & 0 deletions src/llm/providers/mirothinker_sglang_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -142,6 +142,22 @@ async def _create_message(
)
raise Exception("LLM finish_reason is 'stop', but content is empty")

# identify repeated messages and retry
# Check if the last 100 characters of the response appear more than 5 times in the response content.
# If so, treat it as a severe repeat and trigger a retry.
resp_content = response.choices[0].message.content or ""

if resp_content and len(resp_content) >= 50:
tail_50 = resp_content[-50:]
repeat_count = resp_content.count(tail_50)
if repeat_count > 5:
self.task_log.log_step(
"warning",
"LLM | Repeat Detected",
"Severe repeat: the last 50 chars appeared over 5 times, retrying...",
)
raise Exception("Severe repeat detected in response, please retry.")

logger.debug(
f"LLM call finish_reason: {getattr(response.choices[0], 'finish_reason', 'N/A')}"
)
Expand Down
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