run postgres backed llm memory

llm_postgres_memory.txt

Official Way According to LangGraph Docs
1. Short-Term Memory via PostgresSaver

This handles thread-scoped persistence (i.e., remembering the conversation within the current session). Documentation says:

from langgraph.checkpoint.postgres import PostgresSaver
from langgraph.graph import StateGraph

DB_URI = "postgresql://postgres:postgres@localhost:5442/postgres?sslmode=disable"
with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    builder = StateGraph(...)
    graph = builder.compile(checkpointer=checkpointer)


You must call checkpointer.setup() once (initial migration) to create the necessary tables. 
LangChain
LangChain Docs

There's also an async version, AsyncPostgresSaver, if your flow is async. 
LangChain

2. Long-Term Memory via PostgresStore

If you ever want to persist user data across sessions (not just per-thread memory), LangGraph supports that too—although for your use case, this is optional.

from langgraph.store.postgres import PostgresStore
from langgraph.graph import StateGraph

store = PostgresStore.from_conn_string(DB_URI, ...)
builder = StateGraph(...)
graph = builder.compile(checkpointer=checkpointer, store=store)


You can use embedded semantic search capabilities with this. 
LangChain Docs
Medium

How to Integrate into Your Code (Fast, Clean, and Worker-Safe)

You don't need to reinvent the wheel. Just refactor your session/memory logic to use LangGraph's built-in Postgres capabilities. Here's a rough sketch:

memory.py — Replace In-Memory Saver with Postgres
from langgraph.checkpoint.postgres import AsyncPostgresSaver
import os

DB_URI = os.environ["DATABASE_URL"]

async def get_checkpointer():
    saver = await AsyncPostgresSaver.from_conn_string(DB_URI)
    await saver.setup()  # Run migrations (only first time)
    return saver

In Your API Endpoint (simplified):
from your_memory_module import get_checkpointer
from langgraph.graph import StateGraph, MessagesState, START

async def stream_chat_with_memory(...):
    checkpointer = await get_checkpointer()

    builder = StateGraph(MessagesState)
    # ... add nodes, etc.
    graph = builder.compile(checkpointer=checkpointer)

    async for ev in graph.astream(
        {"messages": [{"role": "user", "content": message}]},
        {"configurable": {"thread_id": session_id, "sys_text": sys_text}},
        stream_mode="values",
    ):
        yield ai_utils.chunk_text(ev["messages"][-1].content)


Why this works:

Shared across all workers—Postgres is centralized, so any worker can read/write session threads.

No race conditions—since the checkpointer handles concurrency at the DB level.

Auto-expiry—you can periodically or lazily prune old sessions if you like, but it's not strictly necessary.

DSL-compatible—you can still use LangGraph’s native APIs (StateGraph, etc.) as intended.

TL;DR

Use PostgresSaver (or AsyncPostgresSaver) for short-term/thread memory.

(Optional) Use PostgresStore for long-term memory if you ever want to persist beyond a single session.

This is exactly how LangGraph recommends production deployments be handled. 
LangChain
LangChain Docs
Medium

Want me to rewrite your entire FastAPI handler or memory.py in fully functional form that just swaps in Postgres, keeping your API surface identical?