Agentic-SQL is a sophisticated multi-agent system designed to generate accurate SQL queries from natural language inputs across databases with 100+ tables . Built using LangGraph's orchestration framework, the system employs specialized agents that collaborate to understand schema relationships, generate optimized queries, and validate results . This publication presents a production-ready implementation that bridges the gap between natural language understanding and complex database querying through agentic AI architecture.
Modern enterprise databases often contain hundreds of interconnected tables, making it challenging for non-technical users to extract meaningful insights. Traditional text-to-SQL approaches struggle with:
Agentic-SQL addresses these challenges through a multi-agent architecture where specialized AI agents collaborate to decompose the problem, analyze schema relationships, generate queries, and validate outputs .
The system implements a coordinated multi-agent workflow using LangGraph, consisting of:
User Query → Planning Agent → Schema Agent → SQL Generator → Validator → SQL Output
↓ ↓ ↓ ↓
Agent Logs Schema Viz Query Draft Validation
Backend
Frontend
The system automatically analyzes database schemas to understand:
Real-time visibility into agent decision-making process :
{ "agent": "schema_agent", "action": "analyzing_relationships", "tables": ["users", "orders", "products"], "identified_joins": ["users.id → orders.user_id"] }
Dynamic graph representation of table relationships enables users to understand the data model before query generation .
Configurable support for multiple LLM providers ensures flexibility and cost optimization :
# Core agent workflow structure from langgraph.graph import StateGraph from langchain_groq import ChatGroq class SQLAgentState(TypedDict): query: str schema: dict sql_output: str validation_result: dict # Agent orchestration workflow = StateGraph(SQLAgentState) workflow.add_node("planner", planning_agent) workflow.add_node("schema_analyzer", schema_agent) workflow.add_node("sql_generator", generation_agent) workflow.add_node("validator", validation_agent)
POST /api/query
{ "natural_language_query": "Show me top 10 customers by revenue in 2024", "database_config": { "type": "sqlite", "path": "./sample.db" } }
Response
{ "sql_query": "SELECT c.name, SUM(o.total) as revenue...", "agent_logs": [...], "execution_time": "2.3s", "confidence_score": 0.92 }
// Real-time agent log streaming const eventSource = new EventSource('/api/query/stream'); eventSource.onmessage = (event) => { const agentLog = JSON.parse(event.data); updateAgentLogUI(agentLog); };
1. Backend Setup
cd backend python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -r requirements.txt
2. Environment Configuration
# .env file GROQ_API_KEY=your_groq_key MISTRAL_API_KEY=your_mistral_key DATABASE_URL=sqlite:///./sample.db LOG_LEVEL=INFO
3. Start Services
# Backend uvicorn main:app --reload --port 8000 # Frontend cd frontend python -m http.server 3000
Access the application at http://localhost:3000
Business analysts can query complex data warehouses without SQL knowledge:
Data scientists can rapidly prototype queries for exploratory data analysis without context-switching between notebooks and database tools.
DBAs can use natural language to generate complex administrative queries for schema analysis and optimization tasks.
Agentic-SQL demonstrates the power of multi-agent systems in solving complex domain problems like natural language to SQL generation . By decomposing the problem across specialized agents with distinct responsibilities, the system achieves higher accuracy and transparency compared to monolithic approaches. The architecture provides a foundation for building production-grade AI systems that require coordination between multiple reasoning steps and domain-specific knowledge.
Contributions are welcome! Areas of interest:
For questions, issues, or collaboration opportunities, please open an issue on GitHub or reach out through the repository.
Tags: multi-agent-systems langgraph text-to-sql ai-agents fastapi llm sql-generation natural-language-processing