System Design: Multi-Agent LLM Legal Analysis System (Coding)
Building multi-agent Large Language Model (LLM) systems involves creating a network of AI agents that can interact, collaborate, and solve complex tasks together. These systems leverage the strengths of multiple LLMs, each potentially specialized in different domains or tasks, to achieve more robust and scalable solutions.
Define the Scope and Objectives
- Identify the Legal Domain: Determine the specific area of law you wish to analyze, such as corporate law, intellectual property, or criminal law.
- Set Clear Goals: Establish what you aim to achieve with the analysis, whether it’s case prediction, legal research enhancement, or process optimization.
Employ Design Thinking Methodology
Design thinking offers a user-centric approach to problem-solving, which is beneficial in the legal field. It involves understanding user needs, redefining problems, and creating innovative solutions. This methodology has been applied to create new legal processes, develop legal tech products, and enhance user experiences.
Develop Legal Design Patterns
Legal design patterns provide structured formats that embed legal concepts, rules, and thinking. They facilitate interdisciplinary discussions and enhance problem-solving capabilities in legal scholarship. By documenting recurring solutions, these patterns aid in analyzing shifts between different legal domains and regimes.
Utilize Legal Ontologies
Ontologies in the legal domain help conceptualize and structure legal knowledge. They provide frameworks for organizing legal information, which is crucial for developing legal knowledge systems. Comparative studies of various legal ontologies can guide the selection of the most appropriate framework for your analysis.
Leverage Legal AI and Visualization Tools
Advanced tools like Lawmaps enable the visualization of the implicit structure of legislation and legal processes. By modeling and expressing these structures visually, such tools assist in making legal knowledge more accessible and facilitate the development of legal AI systems.
Implement Case-Based Reasoning Systems
Systems like HYPO model reasoning with cases and hypotheticals in the legal domain. They utilize past cases to inform decision-making, which is particularly useful in common law systems where precedent plays a significant role.
Foster Interdisciplinary Collaboration
Collaborate with legal professionals, technologists, and designers to ensure a holistic approach. Interdisciplinary teams can provide diverse perspectives, leading to more innovative and effective solutions.
What Are AI Agents?
Artificial Intelligence (AI) agents are autonomous entities that can perceive their environment, make decisions, and take actions to achieve specific goals. In the context of LangChain, AI agents are powered by Large Language Models (LLMs) and can be orchestrated to perform complex tasks through workflows, tools, and memory. This article provides an introduction to AI agents in LangChain, along with example code using LangChain, Ollama, and LangGraph.
AI agents are software programs that:
Perceive: Take input from their environment (e.g., user queries, documents).
Reason: Use LLMs to process information and make decisions.
Act: Execute tasks using tools (e.g., APIs, databases) or generate responses.
Learn: Improve over time through feedback or memory.
In LangChain, agents are designed to work with LLMs like GPT-4, LLaMA 3, DeepSeek V3 or custom fine-tuned models. They can be combined into multi-agent systems to solve complex problems collaboratively.
Key Components of AI Agents in LangChain
- LLM Backbone: The core model (e.g., GPT-4, LLaMA 3, DeepSeek V3) that powers the agent’s reasoning.
- Tools: Functions or APIs that agents can use to perform tasks (e.g., web search, database queries).
- Memory: Stores context or history for the agent to reference during interactions.
- Orchestration: Manages workflows and interactions between multiple agents.
Basic AI Agent in LangChain
This example demonstrates a simple AI agent that uses a tool to answer user queries.
from langchain.agents import Tool, AgentExecutor, initialize_agent
from langchain_community.llms import Ollama
from langchain.prompts import PromptTemplate
# Initialize LLM
llm = Ollama(model="llama3")
# Define a tool
def search_tool(query: str) -> str:
"""Mock search tool"""
return "LangChain is a framework for building LLM-powered applications."
tools = [
Tool(
name="Search",
func=search_tool,
description="Useful for answering questions about LangChain."
)
]
# Initialize agent
agent = initialize_agent(
tools,
llm,
agent="zero-shot-react-description",
verbose=True
)
# Run the agent
response = agent.run("What is LangChain?")
print(response)Multi-Agent System with LangGraph
Designing a Multi-Agent System with LangGraph allows for modular and efficient handling of complex tasks by delegating them to specialized agents.
We’ll create two agents: Legal Advisor: Provides general legal advice. Contract Advisor: Specializes in contract-related queries. Each agent will be implemented as a function that processes input and determines whether to handle the request or delegate it to the other agent.
To integrate Ollama into your multi-agent legal analysis system using LangChain and LangGraph, follow these steps:
from langchain.agents import Tool, AgentExecutor, ZeroShotAgent, AgentOutputParser
from langchain.prompts import PromptTemplate
from langchain import LLMChain
from langchain.llms import Ollama
from langchain.schema import AgentAction, AgentFinish
from typing import List, Union
# 1. Define the Ollama LLM
ollama_llm = Ollama(base_url="http://localhost:11434", model="llama3") # Replace with your Ollama URL and model
# 2. Define the Tools
legal_advice_tool = Tool(
name="LegalAdvisor",
func=lambda input: "General legal advice: " + ollama_llm(f"Provide general legal advice on: {input}"),
description="Useful for general legal questions.",
)
contract_advice_tool = Tool(
name="ContractAdvisor",
func=lambda input: "Contract-specific advice: " + ollama_llm(f"Provide contract-specific advice on: {input}"),
description="Useful for questions related to contracts.",
)
tools = [legal_advice_tool, contract_advice_tool]
# 3. Define the Agent Output Parser
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
if "FINAL ANSWER:" in llm_output:
final_answer = llm_output.split("FINAL ANSWER:")[-1].strip()
return AgentFinish({"output": final_answer})
else:
try:
action = llm_output.split("ACTION:")[-1].split("INPUT:")[0].strip()
action_input = llm_output.split("INPUT:")[-1].split("OBSERVATION:")[0].strip()
return AgentAction(tool=action, tool_input=action_input, log=llm_output)
except IndexError:
return AgentFinish({"output": "Could not parse the LLM output."})
# 4. Define the Prompt Template
prompt_template = """
You are a multi-agent legal analysis system. You have access to the following tools: {tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the tool to use. Must be one of the tools provided.
Action Input: the input to the action
Observation: the result of the action
...
Thought: you should always think about what to do
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought: Let's think step by step.
"""
prompt = PromptTemplate(
template=prompt_template, input_variables=["input", "tools"]
)
# 5. Initialize the Agent
llm_chain = LLMChain(llm=ollama_llm, prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, output_parser=CustomOutputParser())
agent_executor = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, llm=ollama_llm
)
# 6. Test the Multi-Agent System - CRUCIAL CHANGE: FORMAT THE TOOLS LIST!
user_query = "What are the key elements of a valid contract, and are there any specific clauses I should be aware of?"
formatted_tools = "\n".join([f"- {tool.name}: {tool.description}" for tool in tools]) # Format tools!
result = agent_executor.run({"input": user_query, "tools": formatted_tools}) # Pass formatted tools
print(result)
user_query = "Is a verbal agreement for selling a car legally binding?"
formatted_tools = "\n".join([f"- {tool.name}: {tool.description}" for tool in tools]) # Format tools!
result = agent_executor.run({"input": user_query, "tools": formatted_tools}) # Pass formatted tools
print(result)Agent Role Specification
Assign distinct roles to each agent to promote specialization and efficiency:
To add web search functionality to your Multi-Agent LLM Legal Analysis System, you can integrate a web search tool that allows agents to retrieve real-time information from the internet. This is particularly useful for tasks like retrieving the latest legal precedents, regulatory updates, or case law.
Using SerpAPI (Google Search)
SerpAPI provides a simple API for Google search results. You’ll need an API key from SerpAPI.
from langchain.utilities import SerpAPIWrapper
# Initialize SerpAPI wrapper (REPLACE with your API key)
search = SerpAPIWrapper(serpapi_api_key="YOUR_SERPAPI_API_KEY") # ***IMPORTANT***
web_search_tool = Tool(
name="WebSearch",
func=search.run,
description="Useful for searching the web for legal precedents, regulations, or case law."
)
# Add tool to agent
tools = [web_search_tool]
# Example usage
query = "latest GDPR compliance updates 2025"
result = web_search_tool.run(query)
print(result)Using DuckDuckGo Search (Open Source)
If you prefer an open-source solution, you can use the DuckDuckGo Search tool.
!pip install langchain duckduckgo-search
from langchain.tools import Tool
from langchain.utilities import DuckDuckGoSearchAPIWrapper
# Initialize DuckDuckGo search wrapper
search = DuckDuckGoSearchAPIWrapper()
# Define web search tool
web_search_tool = Tool(
name="WebSearch",
func=search.run,
description="Useful for searching the web for legal precedents, regulations, or case law."
)
# Add tool to agent
tools = [web_search_tool]
# Example usage
query = "latest GDPR compliance updates 2025"
result = web_search_tool.run(query)
print(result)The combination of LangChain, Ollama, and LangGraph is sufficient for building a Multi-Agent LLM Legal Analysis System. These open-source tools provide a robust foundation for automating legal tasks, enabling collaboration between agents, and orchestrating complex workflows.
If you need additional functionality (e.g., fine-tuning, advanced storage), you can extend the system with other open-source tools like Hugging Face or Pinecone.
If you found this article insightful and want to explore how these technologies can benefit your specific case, don’t hesitate to seek expert advice. Whether you need consultation or hands-on solutions, taking the right approach can make all the difference. You can support the author by clapping below 👏🏻 Thanks for reading!
