How to Integrate LangChain for investment banking with PostgreSQL for startups

LangChain for investment banking gives you the orchestration layer for research, retrieval, and workflow logic. PostgreSQL gives you durable state, auditability, and structured storage for trades, deal notes, KYC artifacts, and model outputs. Together, they let you build startup-grade AI agents that can answer finance questions with memory and traceability instead of treating every prompt like a fresh conversation.

Prerequisites

• •Python 3.10+
• •A PostgreSQL instance running locally or in your cloud environment
• •A database user with CREATE, SELECT, INSERT, and UPDATE permissions
• •An OpenAI-compatible API key or your chosen LLM provider configured for LangChain
• •Installed packages:
  • •langchain
  • •langchain-openai
  • •langchain-community
  • •psycopg2-binary
  • •sqlalchemy
• •A PostgreSQL database schema ready for agent logs and finance records

Install the Python dependencies:

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

Integration Steps

1. •

   Set up PostgreSQL connection details

   Start by defining a SQLAlchemy connection string. LangChain’s PostgreSQL integrations use this connection under the hood for persistence and retrieval.

import os

POSTGRES_URI = os.getenv(
    "POSTGRES_URI",
    "postgresql+psycopg2://agent_user:agent_password@localhost:5432/investment_ai"
)

2. •

   Create a finance-focused table for agent memory

   For startup systems, don’t store everything in raw chat logs. Keep structured records for client requests, deal metadata, and generated outputs.

from sqlalchemy import create_engine, text

engine = create_engine(POSTGRES_URI)

with engine.begin() as conn:
    conn.execute(text("""
        CREATE TABLE IF NOT EXISTS investment_agent_memory (
            id SERIAL PRIMARY KEY,
            session_id TEXT NOT NULL,
            user_query TEXT NOT NULL,
            agent_response TEXT NOT NULL,
            created_at TIMESTAMP DEFAULT NOW()
        )
    """))

3. •

   Initialize the LangChain LLM and prompt chain

   Use LangChain to wrap the model call and enforce a finance-specific response format. For investment banking workflows, keep outputs structured so downstream systems can parse them.

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an investment banking assistant. Be precise, concise, and use financial terminology correctly."),
    ("user", "{question}")
])

chain = prompt | llm

4. •

   Write agent output to PostgreSQL after each response

   This is the core integration pattern: run the LangChain chain, then persist the result in PostgreSQL for audit trails and session continuity.

def ask_investment_agent(session_id: str, question: str) -> str:
    response = chain.invoke({"question": question})
    answer_text = response.content

    with engine.begin() as conn:
        conn.execute(
            text("""
                INSERT INTO investment_agent_memory (session_id, user_query, agent_response)
                VALUES (:session_id, :user_query, :agent_response)
            """),
            {
                "session_id": session_id,
                "user_query": question,
                "agent_response": answer_text,
            }
        )

    return answer_text

5. •

   Add retrieval so the agent can use prior context

   In banking workflows, continuity matters. Pull recent history from PostgreSQL before generating a new answer so the agent can reference prior deal discussions or client preferences.

def get_recent_context(session_id: str, limit: int = 5) -> str:
    with engine.begin() as conn:
        rows = conn.execute(
            text("""
                SELECT user_query, agent_response
                FROM investment_agent_memory
                WHERE session_id = :session_id
                ORDER BY created_at DESC
                LIMIT :limit
            """),
            {"session_id": session_id, "limit": limit}
        ).fetchall()

    context_lines = []
    for row in reversed(rows):
        context_lines.append(f"User: {row.user_query}\nAgent: {row.agent_response}")

    return "\n\n".join(context_lines)

You can then inject that context into the next prompt:

def ask_with_memory(session_id: str, question: str) -> str:
    history = get_recent_context(session_id)

    full_prompt = f"""
Previous context:
{history}

New question:
{question}
"""

    response = chain.invoke({"question": full_prompt})
    answer_text = response.content

    with engine.begin() as conn:
        conn.execute(
            text("""
                INSERT INTO investment_agent_memory (session_id, user_query, agent_response)
                VALUES (:session_id, :user_query, :agent_response)
            """),
            {
                "session_id": session_id,
                "user_query": question,
                "agent_response": answer_text,
            }
        )

    return answer_text

Testing the Integration

Run a simple end-to-end check: generate an answer, store it in PostgreSQL, then read it back.

if __name__ == "__main__":
    session_id = "deal-team-001"
    question = "Summarize the key risks in a Series A term sheet for a fintech startup."

    answer = ask_with_memory(session_id, question)
    print("Agent Answer:\n", answer)

    with engine.begin() as conn:
        latest_row = conn.execute(
            text("""
                SELECT user_query, agent_response
                FROM investment_agent_memory
                WHERE session_id = :session_id
                ORDER BY created_at DESC
                LIMIT 1
            """),
            {"session_id": session_id}
        ).fetchone()

    print("\nLatest DB Record:")
    print(latest_row.user_query)
    print(latest_row.agent_response)

Expected output:

Agent Answer:
The main risks are liquidation preference structure, pro-rata rights dilution...
...

Latest DB Record:
Summarize the key risks in a Series A term sheet for a fintech startup.
The main risks are liquidation preference structure...

Real-World Use Cases

• •

  Deal room assistant

  • •Store diligence questions, analyst notes, and generated summaries in PostgreSQL while LangChain handles retrieval and synthesis.
• •

  Investment memo generator

  • •Pull structured company data from PostgreSQL tables and use LangChain to draft IC memos with consistent formatting.
• •

  Client-facing Q&A memory

  • •Persist prior conversations so relationship teams can ask follow-up questions without losing deal context across sessions.

If you want to take this further in production, add row-level access controls in PostgreSQL, separate schemas per tenant or fund team, and log every model output with versioned prompts. That gives you traceability when someone asks why the system recommended one valuation assumption over another.

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.