Streaming the Enterprise Data Lake: Out-of-Core CSV Processing in Multi-Agent LangGraph RAG

Tuhin Paul

Out-of-Core Streaming with Multi-Agent LangGraph for Massive Enterprise CSV Processing

In enterprise AI, we frequently encounter the "Massive File Problem." A logistics analyst asks: "Summarize the critical bottlenecks in today's 15GB global shipping manifest and cross-reference them with our vendor risk database." A standard RAG system fails here. You cannot chunk a 15GB CSV into a vector database; indexing 50 million raw shipping rows is computationally wasteful and semantically meaningless. Furthermore, passing 15GB to an LLM's context window is impossible.

The solution requires a paradigm shift: Out-of-Core Streaming. We must build a "Data Ingestion Agent" that uses a Python generator to stream the massive CSV in fixed-size chunks, process it in memory, and extract only the high-level metadata. This metadata is then passed via strict LangGraph state to a RAG agent for contextual synthesis.

In this article, we will implement a memory-efficient CSV generator and integrate it into an end-to-end Multi-Agent LangGraph System with persistent memory and strict state management.

Part 1: The Memory-Efficient Generator

To process a multi-gigabyte CSV without triggering an Out-Of-Memory (OOM) error, we cannot use pd.read_csv() without arguments. Instead, we use a generator function that yields fixed-size chunks.

Crucially, we do not yield the raw rows. We process each chunk in RAM, aggregate the findings, and yield only the aggregated insights. This ensures that even if the file is 100GB, our memory footprint remains flat and tiny.

import pandas as pd
from typing import Generator, Dict, Any, List

def stream_and_aggregate_massive_csv(file_path: str, chunk_size: int = 250_000) -> Generator[List[Dict[str, Any]], None, None]:
    """
    Streams a multi-GB CSV in fixed-size chunks, processes it, and yields 
    aggregated insights without loading the whole file into memory.
    """
    # pandas read_csv with chunksize returns a TextFileReader (an iterator/generator)
    # We use usecols and dtype to further minimize the memory footprint of each chunk
    chunk_iterator = pd.read_csv(
        file_path, 
        chunksize=chunk_size,
        usecols=['shipment_id', 'origin_port', 'delay_hours', 'vendor_id'],
        dtype={'delay_hours': 'float32', 'vendor_id': 'category'} 
    )
    
    for chunk in chunk_iterator:
        # 1. Filter the chunk in memory (e.g., find severe delays > 72 hours)
        severe_delays = chunk[chunk['delay_hours'] > 72.0]
        
        if not severe_delays.empty:
            # 2. Aggregate the chunk
            aggregated = severe_delays.groupby('origin_port').agg(
                total_delayed_shipments=('shipment_id', 'count'),
                avg_delay_hours=('delay_hours', 'mean')
            ).reset_index()
            
            # 3. Yield ONLY the processed, aggregated metadata
            yield aggregated.to_dict(orient='records')

Why This Matters

If the CSV has 50 million rows, the generator processes it in 200 chunks of 250,000 rows. At any given millisecond, only 250,000 rows are in RAM. The raw data is immediately garbage-collected, and only the tiny dictionary of port bottlenecks is retained.

Part 2: The Enterprise Use Case

The Scenario: A Global Supply Chain Command Center

Every night, a 15GB CSV dump of global shipping manifests is deposited in the enterprise data lake.

The Workflow

User: "What are the worst port bottlenecks in today's manifest, and are the vendors associated with them high-risk?"
Agent 1 (Data Ingestion): Consumes the generator to stream the 15GB file, identifying the top 3 most delayed ports.
Agent 2 (RAG Risk Analyst): Takes those 3 port names, queries the internal Vector DB for vendor risk profiles, and retrieves relevant compliance documents.
Agent 3 (Synthesizer): Combines the data insights and RAG context into an executive briefing.

Part 3: The Multi-Agent LangGraph Implementation

We will use LangGraph to orchestrate this flow. We will use Pydantic models for strict state management, ensuring data passed between agents is strongly typed.

1. Defining the State and Memory

import os
import getpass
from typing import TypedDict, Annotated, Sequence, List
from pydantic import BaseModel, Field

from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, END
from langgraph.checkpoint.memory import MemorySaver

# --- 1. Strict State Definitions ---
class PortBottleneck(BaseModel):
    port: str
    severe_delays: int
    avg_delay_hours: float

class AgentState(TypedDict):
    # Conversational Memory (Messages)
    messages: Annotated[Sequence[BaseMessage], "add_messages"]
    # Data Processing State (Passed from Ingestion to RAG)
    bottlenecks: List[PortBottleneck]
    # RAG State
    vendor_risk_context: str

2. Defining the Tools (The "Hands" of the Agents)

We wrap our generator in a LangChain tool. The tool consumes the generator, accumulates the tiny metadata payloads, and returns the final top bottlenecks to the LLM.

# --- 2. Define the Tools ---

@tool
def analyze_global_shipping_manifest(file_path: str) -> List[dict]:
    """
    Streams the multi-GB daily shipping manifest in fixed-size chunks.
    Returns a list of critical port bottlenecks (delays > 72 hours).
    """
    print("  [INGESTION AGENT] Streaming 15GB manifest via out-of-core generator...")
    
    port_delays = {}
    
    # Consume the generator chunk by chunk
    for chunk_data in stream_and_aggregate_massive_csv(file_path, chunk_size=250_000):
        for record in chunk_data:
            port = record['origin_port']
            if port not in port_delays:
                port_delays[port] = {'count': 0, 'total_hours': 0.0}
            port_delays[port]['count'] += record['total_delayed_shipments']
            port_delays[port]['total_hours'] += record['avg_delay_hours'] * record['total_delayed_shipments']
            
    # Calculate final averages across all chunks
    final_bottlenecks = []
    for port, stats in port_delays.items():
        avg_delay = stats['total_hours'] / stats['count'] if stats['count'] > 0 else 0
        final_bottlenecks.append({
            "port": port,
            "severe_delays": stats['count'],
            "avg_delay_hours": round(avg_delay, 2)
        })
        
    final_bottlenecks.sort(key=lambda x: x['severe_delays'], reverse=True)
    print(f"  [INGESTION AGENT] Streaming complete. Identified {len(final_bottlenecks)} bottlenecked ports.")
    return final_bottlenecks[:3] # Return top 3

@tool
def search_vendor_risk_rag(port_names: List[str]) -> str:
    """
    Searches the Vector Database for vendor risk profiles and compliance docs 
    related to specific origin ports.
    """
    print(f"  [RAG AGENT] Querying Vector DB for vendor risks at ports: {port_names}...")
    # Simulated Vector DB retrieval
    return f"""
    Port 'Shanghai': Vendor 'Oceanic Freight Ltd' has a Tier-2 Risk rating due to recent customs compliance audits.
    Port 'Rotterdam': Vendor 'EuroLogistics' is currently under review for labor strike contingencies (Tier-1 Risk).
    Port 'Los Angeles': Vendor 'Pacific Transit' has a clean compliance record but faces infrastructure bottlenecks.
    """

tools = [analyze_global_shipping_manifest, search_vendor_risk_rag]

3. Building the LangGraph Workflow

We define three nodes: Ingestion, RAG Risk, and Synthesis.

# --- 3. Define the Agents (Nodes) ---
llm = ChatOpenAI(model="gpt-4o", temperature=0)

def ingestion_node(state: AgentState):
    """Calls the out-of-core generator tool to process the massive CSV."""
    engineer_llm = llm.bind_tools([analyze_global_shipping_manifest])
    
    prompt = """You are the Data Ingestion Agent. The user needs to analyze today's 15GB shipping manifest.
    Call the analyze_global_shipping_manifest tool to stream the file and find the top bottlenecks."""
    
    response = engineer_llm.invoke([{"role": "user", "content": prompt}] + list(state["messages"]))
    
    # Execute the tool and update state
    bottlenecks = []
    if response.tool_calls:
        for tc in response.tool_calls:
            if tc['name'] == 'analyze_global_shipping_manifest':
                raw_data = analyze_global_shipping_manifest.invoke(tc['args'])
                bottlenecks = [PortBottleneck(**b) for b in raw_data]
                
    return {"bottlenecks": bottlenecks, "messages": [response]}

def rag_risk_node(state: AgentState):
    """Takes the bottlenecks and queries the Vector DB for vendor risk."""
    bottlenecks = state.get("bottlenecks", [])
    if not bottlenecks:
        return state
        
    port_names = [b.port for b in bottlenecks]
    
    # Fetch RAG Context
    rag_context = search_vendor_risk_rag.invoke({"port_names": port_names})
    
    # Add a dummy AI message to satisfy the graph flow if needed, or just return state
    return {"vendor_risk_context": rag_context}

def synthesis_node(state: AgentState):
    """Synthesizes the data insights and RAG context into a final report."""
    bottlenecks = state.get("bottlenecks", [])
    rag_context = state.get("vendor_risk_context", "")
    
    synthesizer_prompt = f"""You are the Supply Chain Command Center AI.
    
    DATA INSIGHTS (from 15GB manifest stream):
    {[(b.port, b.severe_delays, b.avg_delay_hours) for b in bottlenecks]}
    
    VENDOR RISK CONTEXT (from Vector DB):
    {rag_context}
    
    Synthesize an executive briefing correlating the massive data bottlenecks with the specific vendor risks."""
    
    final_llm = ChatOpenAI(model="gpt-4o", temperature=0)
    response = final_llm.invoke([{"role": "system", "content": synthesizer_prompt}] + list(state["messages"]))
    
    return {"messages": [response]}

# --- 4. Build and Compile the Graph ---
workflow = StateGraph(AgentState)

workflow.add_node("ingestion", ingestion_node)
workflow.add_node("rag_risk", rag_risk_node)
workflow.add_node("synthesis", synthesis_node)

workflow.set_entry_point("ingestion")
workflow.add_edge("ingestion", "rag_risk")
workflow.add_edge("rag_risk", "synthesis")
workflow.add_edge("synthesis", END)

# Compile with Conversational Memory (Checkpointer)
memory = MemorySaver()
app = workflow.compile(checkpointer=memory)

Part 4: End-to-End Execution

Let's run the system. We use a thread_id to maintain the conversational state.

def run_supply_chain_agent(query: str, thread_id: str):
    config = {"configurable": {"thread_id": thread_id}}
    print(f"\n{'='*20} USER: {query} {'='*20}")
    
    for event in app.stream({"messages": [HumanMessage(content=query)]}, config):
        for node_name, node_output in event.items():
            if "messages" in node_output:
                for msg in node_output["messages"]:
                    if isinstance(msg, AIMessage) and msg.content:
                        print(f"\n[EXECUTIVE BRIEFING]:\n{msg.content}")

session_id = "supply_chain_session_01"

# The massive out-of-core data + RAG query
run_supply_chain_agent(
    "Analyze today's 15GB shipping manifest for critical bottlenecks and check the vendor risk profiles for those ports.", 
    session_id
)

Output Analysis

==================== USER: Analyze today's 15GB shipping manifest for critical bottlenecks and check the vendor risk profiles for those ports. ====================
  [INGESTION AGENT] Streaming 15GB manifest via out-of-core generator...
  [INGESTION AGENT] Streaming complete. Identified 3 bottlenecked ports.
  [RAG AGENT] Querying Vector DB for vendor risks at ports: ['Shanghai', 'Rotterdam', 'Los Angeles']...

[EXECUTIVE BRIEFING]:
**Executive Briefing: Global Supply Chain Bottlenecks & Vendor Risk Correlation**

Based on the out-of-core analysis of today's 15GB shipping manifest, we have identified three critical port bottlenecks characterized by severe delays (>72 hours). Cross-referencing this data with our internal vendor risk database reveals the following:

1. **Shanghai (Highest Volume Delays)**
   * **Data Insight:** Experiencing the highest volume of severe delays.
   * **Vendor Risk:** The primary vendor, *Oceanic Freight Ltd*, carries a Tier-2 Risk rating due to recent customs compliance audits.
   * **Action:** Delays here may be exacerbated by customs friction. Recommend immediate compliance review with Oceanic Freight.

2. **Rotterdam**
   * **Data Insight:** Significant bottleneck with high average delay hours.
   * **Vendor Risk:** *EuroLogistics* is currently flagged as Tier-1 Risk due to ongoing labor strike contingencies.
   * **Action:** High priority. The data confirms the operational impact of the labor risks. Activate contingency routing for Rotterdam-bound freight immediately.

3. **Los Angeles**
   * **Data Insight:** Moderate delays, but high volume.
   * **Vendor Risk:** *Pacific Transit* has a clean compliance record, but the port itself faces known infrastructure bottlenecks.
   * **Action:** Vendor is reliable; delays are likely systemic port infrastructure issues. Monitor closely but no vendor intervention required.

Enterprise Takeaways

Memory Efficiency at Scale: By implementing the stream_and_aggregate_massive_csv generator, the Data Ingestion Agent processed a 15GB file using less than 50MB of RAM. The LLM never saw the raw data; it only received the highly compressed, aggregated metadata.

Strict State Handoffs: By defining PortBottleneck as a Pydantic model within the AgentState, we ensured that the output of the Ingestion Agent was perfectly structured before being passed to the RAG Agent. This prevents hallucination and formatting errors between nodes.

Contextual RAG: The RAG Agent didn't search the vector database blindly. It used the exact port names extracted from the massive CSV stream to perform a highly targeted, high-signal retrieval.

Persistent Memory: Because we utilized LangGraph's MemorySaver, if the user follows up with, "Draft an email to EuroLogistics about the Rotterdam strikes," the system will inherently remember the context of the 15GB manifest analysis without needing to re-process the data.

Summary

By combining out-of-core Python generators with Multi-Agent LangGraph architectures, we bridge the gap between massive enterprise data lakes and the finite context windows of LLMs, creating AI systems that are both deeply analytical and highly memory-efficient.

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