Build a reliable, citation-verified legal research assistant using Tavily, LangChain, and Quotient

https://twitter.com/doesdatmaksense

July 25, 2025

Introduction

Legal research is one of the most essential yet time-consuming tasks in any law firm or in-house legal team. Associates often spend hours searching through statutes, case law, and regulations, often manually verifying every citation and structuring the results into a memo.

While large language models (LLMs) can help accelerate drafting, they often introduce a new risk: hallucinated or unsupported citations that can’t be traced back to a source. That’s a problem when your output needs to hold up in front of clients or in court.

In this guide, we’ll show how to build a legal research assistant that combines:

• Tavily’s real-time search tools for high-quality legal search and content extraction
• LangChain’s agent framework for orchestrating multi-step reasoning
• Quotient AI for evaluating relevance and for automatic hallucination detection

The result is a fast and auditable research pipeline that produces generates structured, verifiable legal memos, backed by real sources.

What We’re Building

We’ll create a legal assistant that can answer practical legal questions like:

“What is the standard for summary judgment under Rule 56?” or “When can attorney-client privilege be waived in federal court?”

The assistant will perform the following steps automatically:

• Search legal sources using Tavily given user query in natural language
• Extract relevant source content from trusted sites
• Draft a professional legal memorandum using OpenAI model through Langhcain Agent