How to Integrate LangChain for wealth management with PostgreSQL for startups

Combining LangChain for wealth management with PostgreSQL gives you a practical setup for building AI agents that can answer portfolio questions, retrieve client history, and persist financial context across sessions. For startups, this matters because you need both reasoning and storage: LangChain handles the agent workflow, while PostgreSQL keeps holdings, transactions, risk profiles, and conversation state queryable and auditable.

Prerequisites

• •Python 3.10+
• •A running PostgreSQL instance
• •Access to a LangChain-compatible LLM provider API key
• •langchain, langchain-openai, langchain-community, psycopg2-binary, and sqlalchemy installed
• •A PostgreSQL database with credentials ready
• •Basic familiarity with SQL and Python async/sync calls

Install the core packages:

pip install langchain langchain-openai langchain-community psycopg2-binary sqlalchemy

Integration Steps

1. •Set up your PostgreSQL connection.

Use SQLAlchemy or a direct psycopg2 connection string. For agent systems, I prefer SQLAlchemy because it plugs into LangChain’s SQL tooling cleanly.

from sqlalchemy import create_engine, text

POSTGRES_URI = "postgresql+psycopg2://wealth_user:password@localhost:5432/wealth_db"
engine = create_engine(POSTGRES_URI)

with engine.connect() as conn:
    result = conn.execute(text("SELECT version();"))
    print(result.fetchone())

This confirms the database is reachable before you wire in the agent.

2. •Create a wealth management schema.

You want tables that map to real product needs: clients, portfolios, and transactions. Keep it normalized enough for reporting, but not so rigid that your startup can’t iterate.

from sqlalchemy import MetaData, Table, Column, Integer, String, Numeric, DateTime, ForeignKey
from sqlalchemy.sql import func

metadata = MetaData()

clients = Table(
    "clients",
    metadata,
    Column("id", Integer, primary_key=True),
    Column("name", String(255), nullable=False),
    Column("risk_profile", String(50), nullable=False),
)

portfolios = Table(
    "portfolios",
    metadata,
    Column("id", Integer, primary_key=True),
    Column("client_id", Integer, ForeignKey("clients.id")),
    Column("portfolio_name", String(255), nullable=False),
    Column("aum", Numeric(18, 2), nullable=False),
)

transactions = Table(
    "transactions",
    metadata,
    Column("id", Integer, primary_key=True),
    Column("portfolio_id", Integer, ForeignKey("portfolios.id")),
    Column("symbol", String(20), nullable=False),
    Column("side", String(10), nullable=False),
    Column("quantity", Numeric(18, 4), nullable=False),
    Column("price", Numeric(18, 4), nullable=False),
    Column("created_at", DateTime(timezone=True), server_default=func.now()),
)

metadata.create_all(engine)

For an AI agent in wealth management, these tables are the source of truth. Don’t let the model invent balances or positions.

3. •Wire LangChain to PostgreSQL for structured querying.

LangChain’s SQLDatabase and create_sql_query_chain are the right starting point when your agent needs to translate user questions into safe SQL against Postgres.

from langchain_community.utilities import SQLDatabase
from langchain_openai import ChatOpenAI
from langchain.chains import create_sql_query_chain

db = SQLDatabase.from_uri(POSTGRES_URI)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

query_chain = create_sql_query_chain(llm, db)

question = "What is the total AUM across all portfolios?"
sql_query = query_chain.invoke({"question": question})
print(sql_query)

That gives you generated SQL. In production, validate it before execution if the agent is allowed to write queries dynamically.

4. •Execute agent-generated queries and format responses.

Once you have SQL from LangChain, run it through PostgreSQL and turn the result into a client-ready answer. This is where you keep the agent grounded in actual financial data.

from sqlalchemy import text

generated_sql = query_chain.invoke({"question": "List all clients with high-risk profiles."})

with engine.connect() as conn:
    rows = conn.execute(text(generated_sql)).fetchall()

for row in rows:
    print(dict(row._mapping))

If you want a more complete agent loop, combine this with a prompt that instructs the model to explain results conservatively and avoid unsupported recommendations.

5. •Add conversational memory backed by PostgreSQL.

For startups building advisor copilots or client service agents, persistent memory matters. Store session context in Postgres so follow-up questions like “show me the same portfolio from last week” work without re-entering details.

A simple pattern is to store messages yourself in a table and hydrate them into LangChain messages on each request.

from sqlalchemy import Table, Column, Integer, Text

chat_history = Table(
    "chat_history",
    metadata,
    Column("id", Integer, primary_key=True),
    Column("session_id", String(100), nullable=False),
    Column("role", String(20), nullable=False),
    Column("content", Text(), nullable=False),
)
metadata.create_all(engine)

def save_message(session_id: str, role: str, content: str):
    with engine.begin() as conn:
        conn.execute(
            chat_history.insert().values(
                session_id=session_id,
                role=role,
                content=content,
            )
        )

This gives you durable session state without relying on ephemeral app memory.

Testing the Integration

Seed a few records and verify that LangChain can query them through PostgreSQL.

from sqlalchemy import insert

with engine.begin() as conn:
    conn.execute(insert(clients), [
        {"name": "Ava Chen", "risk_profile": "high"},
        {"name": "Noah Patel", "risk_profile": "moderate"},
    ])
    conn.execute(insert(portfolios), [
        {"client_id": 1, "portfolio_name": "Growth Alpha", "aum": 1250000},
        {"client_id": 2, "portfolio_name": "Balanced Income", "aum": 780000},
    ])

question = "Which portfolio has the highest AUM?"
sql_query = query_chain.invoke({"question": question})

with engine.connect() as conn:
    result = conn.execute(text(sql_query)).fetchall()

print(result)

Expected output:

[(1, 'Growth Alpha', Decimal('1250000.00'))]

If that comes back correctly, your LangChain-to-PostgreSQL path is working end to end.

Real-World Use Cases

• •Client portfolio Q&A
  • •Let advisors ask natural-language questions like “Show me all clients exposed to tech above 30%” and translate them into live Postgres queries.
• •Compliance-friendly assistant
  • •Persist every user question and generated response in PostgreSQL for audit trails and review.
• •Wealth operations copilot
  • •Build internal tools that summarize AUM trends, flag concentration risk, and retrieve transaction history for support teams.

The clean pattern here is simple: let LangChain handle reasoning and orchestration; let PostgreSQL hold authoritative financial data. That separation keeps your startup’s AI agent useful without turning it into an ungoverned black box.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.