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import json
from typing import Dict, Any, List
import time
import requests
import os
from .base_agent import BaseAgent, AgentConfig, AgentResponse, TaskInput
class InformationSeekerAgent(BaseAgent):
"""
Information Seeker Agent that follows ReAct pattern (Reasoning + Acting)
This agent takes decomposed sub-questions or tasks from parent agents,
thinks interleaved (reasoning -> action -> reasoning -> action),
uses MCP tools to gather information, and returns structured results.
"""
def __init__(self, config: AgentConfig = None, shared_mcp_client=None):
# Set default agent name if not specified
if config is None:
config = AgentConfig(agent_name="InformationSeekerAgent")
elif config.agent_name == "base_agent":
config.agent_name = "InformationSeekerAgent"
super().__init__(config, shared_mcp_client)
def _build_system_prompt(self) -> str:
"""Build the system prompt for the ReAct agent"""
tool_schemas_str = json.dumps(self.tool_schemas, ensure_ascii=False)
system_prompt_template = """You are an Information Seeker Agent that follows the ReAct pattern (Reasoning + Acting).
Your role is to:
1. Take decomposed sub-questions or tasks from parent agents
2. Think step-by-step through reasoning
3. Use available tools to gather information when needed
4. Continue reasoning based on tool results
5. Repeat this process until you have sufficient information
6. Call info_seeker_objective_task_done to provide a structured summary
### Optimized Workflow:
Follow this optimized workflow for information gathering:
1. INITIAL RESEARCH:
- Use `batch_web_search` to find relevant URLs for your queries. When calling the search statement, consider the language of the user's question. For example, for a Chinese question, generate a part of the search statement in Chinese.
- Analyze the search results (titles, snippets, URLs) to identify promising sources
2. CONTENT EXTRACTION:
- For important URLs, use `url_crawler` to:
a) Extract full content from the webpage
b) Save the content to a file in the workspace
- Store results with meaningful file paths (e.g., \"research/ai_trends_2024.txt\")
3. CONTENT ANALYSIS:
- Use `document_qa` to ask specific questions about the saved files:
a) Formulate focused questions to extract key insights
b) Use answers to deepen your understanding
- You can ask multiple questions about the same file
4. FILE MANAGEMENT:
- Use `file_write` to save important findings or summaries
- For reviewing saved content:
a) Prefer `document_qa` to ask specific questions about the content
b) Use `file_read` ONLY for small files (<1000 tokens) when you need the entire content
c) Avoid reading large files directly as it may exceed context limits
5. TASK COMPLETION:
- When ready to report, call `info_seeker_objective_task_done` with:
a) Comprehensive markdown summary of your process and findings
b) List of key files created with descriptions
### Usage of Systematic Tool:
- `think` is a systematic tool. After receiving the response from the complex tool or before invoking any other tools, you must **first invoke the `think` tool**: to deeply reflect on the results of previous tool invocations (if any), and to thoroughly consider and plan the user's task. The `think` tool does not acquire new information; it only saves your thoughts into memory.
- `reflect` is a systematic tool. When encountering a failure in tool execution, it is necessary to invoke the reflect tool to conduct a review and revise the task plan. It does not acquire new information; it only saves your thoughts into memory.
Always provide clear reasoning for your actions and synthesize information effectively.
Below, within the <tools></tools> tags, are the descriptions of each tool and the required fields for invocation:
<tools>
$tool_schemas
</tools>
For each function call, return a JSON object placed within the [unused11][unused12] tags, which includes the function name and the corresponding function arguments:
[unused11][{\"name\": <function name>, \"arguments\": <args json object>}][unused12]
"""
return system_prompt_template.replace("$tool_schemas", tool_schemas_str)
@staticmethod
def _build_initial_message_from_task_input(task_input: TaskInput) -> str:
"""Build the initial user message from TaskInput"""
message = task_input.format_for_prompt()
message += "\nPlease analyze this task and start your ReAct process:\n"
message += "1. Reason about what information you need to gather\n"
message += "2. Use appropriate tools to get that information\n"
message += "3. Continue reasoning and acting until you have sufficient information\n"
message += "4. Call task_done when ready to provide your complete findings\n\n"
message += "Begin with your initial reasoning about the task."
return message
def execute_task(self, task_input: TaskInput) -> AgentResponse:
"""
Execute a task using ReAct pattern (Reasoning + Acting)
Args:
task_input: TaskInput object with standardized task information
Returns:
AgentResponse with results and process trace
"""
start_time = time.time()
try:
self.logger.info(f"Starting information seeker task: {task_input.task_content}")
# Reset trace for new task
self.reset_trace()
# Initialize conversation history
conversation_history = []
# Build initial system prompt for ReAct
system_prompt = self._build_system_prompt()
# Build initial user message from TaskInput
user_message = self._build_initial_message_from_task_input(task_input)
# Add to conversation
conversation_history.append({"role": "system", "content": system_prompt})
conversation_history.append({"role": "user", "content": user_message+" /no_think"})
iteration = 0
task_completed = False
# Get model endpoint configuration from env-backed config
from config.config import get_config
config = get_config()
model_config = config.get_custom_llm_config()
pangu_url = model_config.get('url') or os.getenv('MODEL_REQUEST_URL', '')
model_token = model_config.get('token') or os.getenv('MODEL_REQUEST_TOKEN', '')
headers = {'Content-Type': 'application/json', 'csb-token': model_token}
# ReAct Loop: Reasoning -> Acting -> Reasoning -> Acting...
while iteration < self.config.max_iterations and not task_completed:
iteration += 1
self.logger.info(f"Planning iteration {iteration}")
try:
# Get LLM response (reasoning + potential tool calls)
retry_num = 1
max_retry_num = 10
while retry_num < max_retry_num:
try:
response = requests.post(
url=pangu_url,
headers=headers,
json={
"model": self.config.model,
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<s>[unused9]系统：[unused10]' }}{% endif %}{% if message['role'] == 'system' %}{{'<s>[unused9]系统：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'assistant' %}{{'[unused9]助手：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'tool' %}{{'[unused9]工具：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'function' %}{{'[unused9]方法：' + message['content'] + '[unused10]'}}{% endif %}{% if message['role'] == 'user' %}{{'[unused9]用户：' + message['content'] + '[unused10]'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '[unused9]助手：' }}{% endif %}",
"messages": conversation_history,
"temperature": self.config.temperature,
"spaces_between_special_tokens": False,
"max_tokens": self.config.max_tokens,
},
timeout=model_config.get("timeout", 180)
)
response = response.json()
self.logger.debug(f"API response received")
break
except Exception as e:
time.sleep(3)
retry_num += 1
if retry_num == max_retry_num:
raise ValueError(str(e))
continue
assistant_message = response["choices"][0]["message"]
# Log the reasoning
try:
if assistant_message["content"]:
reasoning_content = assistant_message["content"].split("[unused16]")[-1].split("[unused17]")[0]
if len(reasoning_content) > 0:
self.log_reasoning(iteration, reasoning_content)
except Exception as e:
self.logger.warning(f"Tool call parsing error: {e}")
# Parse error, rerun
followup_prompt = f"There is a problem with the format of model generation: {e}. Please try again."
conversation_history.append({"role": "user", "content": followup_prompt + " /no_think"})
continue
def extract_tool_calls(content):
import re
if not content:
return []
tool_call_str = re.findall(r"\[unused11\]([\s\S]*?)\[unused12\]", content)
if len(tool_call_str) > 0:
try:
tool_calls = json.loads(tool_call_str[0].strip())
except:
return []
else:
return []
return tool_calls
# Add assistant message to conversation
conversation_history.append({
"role": "assistant",
"content": assistant_message["content"]
})
tool_calls = extract_tool_calls(assistant_message["content"])
# Execute tool calls if any (Acting phase)
for tool_call in tool_calls:
arguments = tool_call["arguments"]
# Check if planning is complete
if tool_call["name"] in ["info_seeker_objective_task_done"]:
task_completed = True
self.log_action(iteration, tool_call["name"], arguments, arguments)
break
if tool_call["name"] in ["think", "reflect"]:
tool_result = {"tool_results": "You can proceed to invoke other tools if needed."}
else:
tool_result = self.execute_tool_call(tool_call)
# Log the action using base class method
self.log_action(iteration, tool_call["name"], arguments, tool_result)
# Add tool result to conversation
conversation_history.append({
"role": "tool",
"content": json.dumps(tool_result, ensure_ascii=False, indent=2) + " /no_think"
})
# If no tool calls, encourage continued planning
if len(tool_calls) == 0:
# Add follow-up prompt to encourage action or completion
followup_prompt = (
"Continue your planning process. Use available tools to assign tasks to agents, "
"search for information, or coordinate work. When you have a complete answer, "
"call info_seeker_objective_task_done. /no_think"
)
conversation_history.append({"role": "user", "content": followup_prompt})
if iteration == self.config.max_iterations-3:
followup_prompt = "Due to length and number of rounds restrictions, you must now call the `info_seeker_objective_task_done` tool to report the completion of your task. /no_think"
conversation_history.append({"role": "user", "content": followup_prompt})
except Exception as e:
error_msg = f"Error in planning iteration {iteration}: {e}"
self.log_error(iteration, error_msg)
break
execution_time = time.time() - start_time
# Extract final result
if task_completed:
# Find the info_seeker_objective_task_done result in the trace
task_done_result = None
for step in reversed(self.reasoning_trace):
if step.get("type") == "action" and step.get("tool") == "info_seeker_objective_task_done":
task_done_result = step.get("result")
break
return self.create_response(
success=True,
result=task_done_result,
iterations=iteration,
execution_time=execution_time
)
else:
return self.create_response(
success=False,
error=f"Task not completed within {self.config.max_iterations} iterations",
iterations=iteration,
execution_time=execution_time
)
except Exception as e:
execution_time = time.time() - start_time
self.logger.error(f"Error in execute_task: {e}")
return self.create_response(
success=False,
error=str(e),
iterations=iteration if 'iteration' in locals() else 0,
execution_time=execution_time
)
def _build_agent_specific_tool_schemas(self) -> List[Dict[str, Any]]:
"""
Build tool schemas for InformationSeekerAgent using proper MCP architecture.
Schemas come from MCP server via client, not direct imports.
"""
# Get MCP tool schemas from server via client (proper MCP architecture)
schemas = super()._build_agent_specific_tool_schemas()
# Add schemas for built-in task assignment tools
builtin_assignment_schemas = [
{
"type": "function",
"function": {
"name": "think",
"description": "Use the tool to think about something. It will not obtain new information or make any changes to the repository, but just log the thought. Use it when complex reasoning or brainstorming is needed.",
"parameters": {
"type": "object",
"properties": {
"thought": {
"type": "string",
"description": "Your thoughts."
}
},
"required": ["thought"]
}
}
},
{
"type": "function",
"function": {
"name": "reflect",
"description": "When multiple attempts yield no progress, use this tool to reflect on previous reasoning and planning, considering possible overlooked clues and exploring more possibilities. It will not obtain new information or make any changes to the repository.",
"parameters": {
"type": "object",
"properties": {
"reflect": {
"type": "string",
"description": "The specific content of your reflection"
}
},
"required": ["reflect"]
}
}
},
{
"type": "function",
"function": {
"name": "info_seeker_objective_task_done",
"description": "Structured reporting of task completion details including summary, decisions, outputs, and status",
"inputSchema": {
"type": "object",
"properties": {
"task_summary": {
"type": "string",
"description": "Comprehensive markdown covering what the agent was asked to do, steps taken, tools used, key findings, files created, challenges, and final deliverables.",
"format": "markdown"
},
"task_name": {
"type": "string",
"description": "The name of the task currently assigned to the agent, usually with underscores (e.g., 'web_research_ai_trends')"
},
"key_files": {
"type": "array",
"items": {
"type": "object",
"properties": {
"file_path": {
"type": "string",
"description": "Relative path to created/modified file"
},
"desc": {
"type": "string",
"description": "File contents and creation purpose"
},
"is_final_output_file": {
"type": "boolean",
"description": "Whether file is primary deliverable"
}
},
"required": ["file_path", "desc", "is_final_output_file"]
},
"description": "List of key files generated or modified during the task, with their details."
},
"completion_status": {
"type": "string",
"enum": ["completed", "partial", "failed"],
"description": "Final task status"
}
},
"required": ["task_summary", "task_name", "key_files", "completion_status"]
}
}
},
]
schemas.extend(builtin_assignment_schemas)
return schemas
# Factory function for creating the agent
def create_objective_information_seeker(
model: Any = None,
max_iterations: Any = None,
shared_mcp_client=None,
**kwargs
) -> InformationSeekerAgent:
"""
Create an InformationSeekerAgent instance with server-managed sessions.
Args:
model: The LLM model to use
max_iterations: Maximum number of iterations
shared_mcp_client: Optional shared MCP client from parent agent (prevents extra sessions)
**kwargs: Additional configuration options
Returns:
Configured InformationSeekerAgent instance with appropriate tools
"""
# Import the enhanced config function
from ..agents.base_agent import create_agent_config
# Create agent configuration (session managed by MCP server)
config = create_agent_config(
agent_name="InformationSeekerAgent",
model=model,
max_iterations=max_iterations,
**kwargs
)
# Create agent instance with shared MCP client (filtered tools for information seeking)
agent = InformationSeekerAgent(config=config, shared_mcp_client=shared_mcp_client)
return agent
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