🤖 Langgraph Multi-Agent Text2SQL System

A Text2SQL agent that converts natural language queries into validated SQL statements using a multi-agent architecture built with LangGraph. The system mainly uses iterative feedback loops to refine SQL generation, ensuring accuracy through programmatic validation and evaluation stages.

🏗️ Architecture

The agent follows a state graph workflow orchestrated by LangGraph, with agents that handle different stages of the SQL generation process:

graph TD
A[User Prompt] --> B{Prompt Processor};
B --> B1[Relevance Checker];
B1 -- Irrelevant --> B_OUT["I can only answer questions about..."];
B1 -- Relevant --> B2[Prompt Optimizer];
B2 --> C{Iterative Feedback Loop - Max N Retries};
subgraph "Iteration Loop"
C --> D[LLM Generator];
D -- SQL Query --> E["SQL Validator"];
E -- Syntax/Schema Error --> F[Feedback Formatter];
E -- Valid SQL --> G[Query Evaluator];
G -- Execution/Result Error --> F;
F -- Corrective Feedback --> D;
G -- Correct Data --> H[Successful Exit];
end
C -- Loop Fails after 3 Retries --> I_FAIL[Return Last SQL & Failure Message];
H --> I_SUCCESS[Generate Final Response];
I_SUCCESS --> I_OUT[LLM Generated Natural Language Answer And Final SQL];

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🧩 Core Components

Graph Orchestration (graph.py)

Defines the state machine using LangGraph's StateGraph, connecting agent nodes and defining conditional routing between them. The graph manages the workflow from initial prompt processing through SQL generation, validation, and final response generation.

Agent Nodes (agents.py)

• Relevance Checker: Determines if the query is relevant to the database schema
• SQL Generator: Creates SQL queries from natural language using LLMs
• SQL Validator: Validates SQL syntax and schema compliance
• Query Evaluator: Assesses if the generated SQL correctly answers the user's question
• Feedback Formatter: Creates feedback for the generator when SQL needs refinement
• Finalizer: Generates the final natural language response

State Management (states.py)

The FullState maintains context throughout the workflow, including:

• Conversation messages
• User query and optimized query
• Generated SQL and validation results
• Feedback history
• Loop counters and final verdict

Configuration (configuration.py)

Manages runtime configuration through the Configuration class, allowing customization of:

• LLM models for different agent roles
• Maximum feedback loops
• Database schema

🚀 Usage

💻 Command Line Interface

The system can be run via the command line using main.py:

uv run main.py --query "Find customers from last month" --database-schema-json-path example_schema.json

Available options:

• --query: Natural language query (required)
• --database-schema-json-path: Path to database schema JSON (required)
• --max-feedback-loops: Maximum number of refinement attempts (default: 3)
• --relevance-checker-model: LLM for relevance checking (default: llama-3.1-8b-instant)
• --query-generator-model: LLM for SQL generation (default: moonshotai/kimi-k2-instruct)
• --query-evaluator-model: LLM for query evaluation (default: moonshotai/kimi-k2-instruct)
• --finalizing-model: LLM for final response generation (default: moonshotai/kimi-k2-instruct)

🔧 Environment Configuration

Create a .env file with your API keys:

GROQ_API_KEY=your_api_key_here

🗃️ Database Schema Format

The system requires a JSON schema defining your database structure:

{
"tables": [
{
"table_name": "X",
"columns": {
}
}
]
}

📦 Dependencies

The system requires:

• Python 3.13+
• UV Package Manager (Recommended)
• Groq API key (or compatible LLM provider)
• Required packages listed in pyproject.toml

Install dependencies:

uv pip install -r requirements.txt

OR

uv sync

🛠️ TO-DO

• Add support for more LLM providers
• Write better prompts
• Add an executor that executes the query on a copy database, or generate mock data from the schema and execute the query.

❤️ Contributing

Contributions are always welcome :)