Boost Text2SQL Accuracy with AI Agents: A LangChain Practical Guide

1. Introduction

In the previous article "How to Optimize Text2SQL with Prompt Engineering" we introduced prompt‑based techniques for improving Text2SQL. Beyond prompts, we can use AI agents to further boost large‑model applications. This article examines four aspects of using agents to improve LLM‑driven Text2SQL conversion.

2. Agent Overview

2.1 What is an Agent?

In the LLM domain, an Agent is a design concept that mimics human or other intelligent behavior. An Agent perceives its environment, processes information, plans strategies, takes actions, and adapts based on feedback.

Agents teach large models specific reasoning methodologies, providing a framework of modules that support the overall system.

2.2 Core Components

Agent : a proxy service capable of handling complex operations, powered by a large model.

Tools : external capabilities such as calculation, web search, code execution, etc., offered to the Agent.

Memory : a storage (database or other) that retains interaction history to prevent forgetting.

Planning : LLM abilities like reflection, goal decomposition, chain‑of‑thought reasoning.

2.2.1 Planning

Complex tasks are broken into steps. Agents need to know and plan these steps, including:

Sub‑goal and task decomposition

Reflection and improvement

Sub‑goal decomposition uses techniques such as:

Chain‑of‑Thought (CoT) – step‑by‑step reasoning.

Tree‑of‑Thought (ToT) – generates multiple reasoning branches.

Task decomposition can be prompted with simple instructions like "Steps for X.Y.Z." or domain‑specific commands such as "Write a story outline."

2.2.2 Reflection and Improvement

Agents can self‑calibrate by reflecting on past actions, learning from errors, and improving future steps. Notable frameworks include:

ReAct : combines reasoning (Thought) and acting (Action) to interact with external tools.

Reflexion : adds dynamic memory and self‑reflection to enhance reasoning.

Chain of Hindsight (CoH) : feeds sequential output history back into the model for better predictions.

2.2.3 Tools

Agents can call external APIs, databases, or other models to augment problem‑solving capabilities. The MRKL system routes requests to expert modules, similar to ChatGPT plugins or OpenAI function calls.

3. Agent Module in LangChain

3.1 Overview

LangChain provides several Agent types, including Zero‑shot ReAct, Conversational, and Plan‑and‑Execute. ReAct follows a "Reasoning + Acting" pattern, breaking complex queries and fetching missing information via tools.

3.2 Example Code

# Set up a search tool
search = SerpAPIWrapper()
tools = [
    Tool(
        name="Current Search",
        func=search.run,
        description="useful for when you need to answer questions about current events or the current state of the world"
    ),
]
# Provide a memory store
memory = ConversationBufferMemory(memory_key="chat_history")
# Initialize the agent chain
llm = OpenAI(temperature=0)
agent_chain = initialize_agent(
    tools, llm, agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory
)

Running a query:

agent_chain.run(input="whats the current temperature in pomfret?")

> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? Yes
Action: Current Search
Action Input: Current temperature in Pomfret
Observation: Partly cloudy skies. High around 70F. Winds W at 5 to 10 mph. Humidity 41%.
Thought: Do I need to use a tool? No
AI: The current temperature in Pomfret is around 70F with partly cloudy skies and winds W at 5 to 10 mph. The humidity is 41%.
> Finished chain.

3.3 ReAct Pattern Analysis

The prompt template after setting agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION forces the model to output in the format: Question → Thought → Action → Observation, repeating until a final answer is produced.

This approach opens a new avenue; popular systems like AutoGPT and BabyAGI use similar mechanisms.

4. Optimizing Text2SQL with Agents

4.1 Existing Issue

Using the Chinook database, the requirement is to list customers who placed orders in two consecutive months. The initial SQL generated only captured the most recent two months, not the full requirement.

4.2 Agent‑Based Solution

LangChain’s SQL Agent offers a more flexible interaction than SQLDatabaseChain. It can:

Answer questions based on the database schema and content.

Execute generated queries, capture errors, and regenerate corrected SQL.

Initialize the SQL Agent:

from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit

# db = SQLDatabase.from_uri("sqlite:///xxx/Chinook.db")
agent_executor = create_sql_agent(
    llm=OpenAI(temperature=0),
    toolkit=SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0)),
    verbose=True,
    agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)

Run the query:

agent_executor.run("在订单表中，连续两个月都下订单的客户有哪些?")

The resulting SQL satisfies the requirement, and the execution trace can be inspected via LangSmith.

5. Future Plans

This article demonstrated how agents can improve Text2SQL results by iteratively applying the Thought→Action→Observation loop, leveraging tools, memory, and planning. Remaining challenges include context length limits, long‑term planning, task decomposition complexity, and reliability of natural‑language interfaces. Ongoing work will address these to further advance agent‑based LLM applications.

References:

https://lilianweng.github.io/posts/2023-06-23-agent

https://python.langchain.com/docs/modules/agents

https://python.langchain.com/docs/integrations/toolkits/sql_database

https://python.langchain.com/docs/use_cases/qa_structured/sql

https://zhuanlan.zhihu.com/p/643799381

https://zhuanlan.zhihu.com/p/664281311