Understanding the Target Audience
The target audience for the Advanced LangGraph Multi-Agent Research Pipeline includes business professionals, data scientists, and researchers eager to harness AI technologies for improved research capabilities. This group typically comprises:
- Data analysts aiming to automate insights generation.
- Business managers seeking efficient research workflows.
- Developers interested in implementing AI-driven solutions.
Common challenges faced by this audience include:
- Time-consuming manual research processes.
- Difficulty in synthesizing large volumes of data into actionable insights.
- Challenges in integrating various AI tools into cohesive workflows.
Their goals often focus on:
- Streamlining research processes to save time and resources.
- Improving the accuracy and relevance of insights generated.
- Enhancing decision-making capabilities through data-driven reports.
Interests typically include the latest advancements in AI and machine learning, best practices for data analysis, and tools that facilitate automation in research. In terms of communication, this audience prefers clear technical documentation, hands-on tutorials, and interactive webinars for practical learning.
Advanced LangGraph Multi-Agent Research Pipeline
This pipeline utilizes Google’s free-tier Gemini model to create an end-to-end research workflow. Below, we outline the steps to set up and implement this advanced system.
Installation
To get started, install the necessary libraries:
!pip install -q langgraph langchain-google-genai langchain-coreSetting Up the Environment
Begin by importing the required modules and setting up your environment:
import os
from typing import TypedDict, Annotated, List, Dict, Any
from langgraph.graph import StateGraph, END
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
import operator
import json
os.environ["GOOGLE_API_KEY"] = "Use Your Own API Key"Next, define the structure for the agent’s state and initialize the language model:
class AgentState(TypedDict):
messages: Annotated[List[BaseMessage], operator.add]
current_agent: str
research_data: dict
analysis_complete: bool
final_report: str
llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.7)Simulating Web Search and Data Analysis
We create functions to simulate web searches and data analysis:
def simulate_web_search(query: str) -> str:
return f"Search results for '{query}': Found relevant information about {query} including recent developments, expert opinions, and statistical data."
def simulate_data_analysis(data: str) -> str:
return f"Analysis complete: Key insights from the data include emerging trends, statistical patterns, and actionable recommendations."Research Agent Implementation
The research agent gathers information based on the user’s query:
def research_agent(state: AgentState) -> AgentState:
messages = state["messages"]
last_message = messages[-1].content
search_results = simulate_web_search(last_message)
prompt = f"""You are a research agent. Based on the query: "{last_message}"
Here are the search results: {search_results}
Conduct thorough research and gather relevant information. Provide structured findings with:
1. Key facts and data points
2. Current trends and developments
3. Expert opinions and insights
4. Relevant statistics
Be comprehensive and analytical in your research summary."""
response = llm.invoke([HumanMessage(content=prompt)])
research_data = {
"topic": last_message,
"findings": response.content,
"search_results": search_results,
"sources": ["academic_papers", "industry_reports", "expert_analyses"],
"confidence": 0.88,
"timestamp": "2024-research-session"
}
return {
"messages": state["messages"] + [AIMessage(content=f"Research completed on '{last_message}': {response.content}")],
"current_agent": "analysis",
"research_data": research_data,
"analysis_complete": False,
"final_report": ""
}Analysis Agent Implementation
The analysis agent processes the research data:
def analysis_agent(state: AgentState) -> AgentState:
research_data = state["research_data"]
analysis_results = simulate_data_analysis(research_data.get('findings', ''))
prompt = f"""You are an analysis agent. Analyze this research data in depth:
Topic: {research_data.get('topic', 'Unknown')}
Research Findings: {research_data.get('findings', 'No findings')}
Analysis Results: {analysis_results}
Provide deep insights including:
1. Pattern identification and trend analysis
2. Comparative analysis with industry standards
3. Risk assessment and opportunities
4. Strategic implications
5. Actionable recommendations with priority levels
Be analytical and provide evidence-based insights."""
response = llm.invoke([HumanMessage(content=prompt)])
return {
"messages": state["messages"] + [AIMessage(content=f"Analysis completed: {response.content}")],
"current_agent": "report",
"research_data": state["research_data"],
"analysis_complete": True,
"final_report": ""
}Report Agent Implementation
The report agent creates a comprehensive executive report:
def report_agent(state: AgentState) -> AgentState:
research_data = state["research_data"]
analysis_message = None
for msg in reversed(state["messages"]):
if isinstance(msg, AIMessage) and "Analysis completed:" in msg.content:
analysis_message = msg.content.replace("Analysis completed: ", "")
break
prompt = f"""You are a professional report generation agent. Create a comprehensive executive report based on:
Research Topic: {research_data.get('topic')}
Research Findings: {research_data.get('findings')}
Analysis Results: {analysis_message or 'Analysis pending'}
Generate a well-structured, professional report with these sections:
## EXECUTIVE SUMMARY
## KEY RESEARCH FINDINGS
[Detail the most important discoveries and data points]
## ANALYTICAL INSIGHTS
[Present deep analysis, patterns, and trends identified]
## STRATEGIC RECOMMENDATIONS
[Provide actionable recommendations with priority levels]
## RISK ASSESSMENT & OPPORTUNITIES
[Identify potential risks and opportunities]
## CONCLUSION & NEXT STEPS
[Summarize and suggest follow-up actions]
Make the report professional, data-driven, and actionable."""
response = llm.invoke([HumanMessage(content=prompt)])
return {
"messages": state["messages"] + [AIMessage(content=f" FINAL REPORT GENERATED:\n\n{response.content}")],
"current_agent": "complete",
"research_data": state["research_data"],
"analysis_complete": True,
"final_report": response.content
}Workflow Management
We manage the workflow through a state graph:
def should_continue(state: AgentState) -> str:
current_agent = state.get("current_agent", "research")
if current_agent == "research":
return "analysis"
elif current_agent == "analysis":
return "report"
elif current_agent == "report":
return END
else:
return END
workflow = StateGraph(AgentState)
workflow.add_node("research", research_agent)
workflow.add_node("analysis", analysis_agent)
workflow.add_node("report", report_agent)
workflow.add_conditional_edges(
"research",
should_continue,
{"analysis": "analysis", END: END}
)
workflow.add_conditional_edges(
"analysis",
should_continue,
{"report": "report", END: END}
)
workflow.add_conditional_edges(
"report",
should_continue,
{END: END}
)
workflow.set_entry_point("research")
app = workflow.compile()
def run_research_assistant(query: str):
initial_state = {
"messages": [HumanMessage(content=query)],
"current_agent": "research",
"research_data": {},
"analysis_complete": False,
"final_report": ""
}
print(f" Starting Multi-Agent Research on: '{query}'")
print("=" * 60)
current_state = initial_state
print(" Research Agent: Gathering information...")
current_state = research_agent(current_state)
print(" Research phase completed!\n")
print(" Analysis Agent: Analyzing findings...")
current_state = analysis_agent(current_state)
print(" Analysis phase completed!\n")
print(" Report Agent: Generating comprehensive report...")
final_state = report_agent(current_state)
print(" Report generation completed!\n")
print("=" * 60)
print(" MULTI-AGENT WORKFLOW COMPLETED SUCCESSFULLY!")
print("=" * 60)
final_report = final_state['final_report']
print(f"\n COMPREHENSIVE RESEARCH REPORT:\n")
print(final_report)
return final_stateConclusion
This modular setup enables rapid prototyping of complex workflows. Each agent specializes in a distinct phase of intelligence gathering, interpretation, and delivery, allowing for real API integration or the addition of new tools as needs evolve. Experimenting with custom tools and refining agent prompts ensures adaptability across various domains.
For further exploration, check out our GitHub page for tutorials, codes, and notebooks. Join our community on Twitter and subscribe to our newsletter for the latest updates.
FAQ
- What is the purpose of the Advanced LangGraph Multi-Agent Research Pipeline?
This pipeline automates the research process by utilizing AI agents to gather, analyze, and report findings efficiently. - Who can benefit from using this pipeline?
Business professionals, data scientists, and researchers looking to streamline their research workflows can greatly benefit. - What are the main features of this research pipeline?
The pipeline includes automated web searches, data analysis, and report generation through a multi-agent system. - How can I get started with this pipeline?
Begin by installing the required libraries and setting up your environment as outlined in the tutorial. - Is it necessary to have programming skills to use this pipeline?
While some programming knowledge is helpful, the tutorial provides step-by-step instructions to guide users through the setup and implementation.
