Full Stack Integration: Connecting the Stack

You have mastered the Brain (LLM), the Nervous System (LangGraph), and the Face (React). Now, we must build the Skeletal System—the API and infrastructure that connects them all into a living product.

In this lesson, we will map out the end-to-end data flow of a production agent and identify the specific technologies used at each layer of the "Modern Agent Stack."

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1. The 3-Tier Agent Architecture

We do not run LangGraph in the browser. We do not run React on the server. We follow a strict 3-tier model.

Layer 1: The UI (React / Next.js)

• Responsibility: State management, streaming UI, user authentication, and visual rendering.
• Communication: Sends JSON to the API; receives SSE (Server-Sent Events) back.

Layer 2: The API (FastAPI / Python)

• Responsibility: The Gatway. Authenticates users, performs rate limiting, manages the database (checkpointers), and triggers the LangGraph process.
• Communication: Translates HTTP requests into LangGraph invoke calls.

Layer 3: The Engine (LangGraph + Tools)

• Responsibility: The Reasoning Loop. Manages the graph orquestration and the tool executions (potentially inside containers).

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2. The Bridge: FastAPI and LangGraph

FastAPI is the preferred choice for Agent backends because it is Asynchronous (handles many wait-and-resume agents efficiently) and is built in Python (matching the LangChain ecosystem).

The Endpoint Protocol

• POST /threads: Create a new conversation ID.
• POST /threads/{id}/runs: Send a message to the agent.
• GET /threads/{id}/history: Retrieve past messages for a returning user.

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3. Data Synchronization between Backend and Frontend

The biggest challenge in full-stack agency is keeping the "UI State" in sync with the "Agent State."

• The Problem: LangGraph might be on Step 4, but the UI is still animating Step 2.
• The Solution: The Log Index.
  • Every message in the stream must have a step_number.
  • The UI uses this number to "Sort" the updates, ensuring that even if packets arrive out of order, the user sees a logical flow.

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4. The Database: Where does state live?

In production, you need a high-performance, persistent state store.

1. Redis: Great for fast, ephemeral chat history.
2. PostgreSQL: The industry standard for LangGraph persistence (PostgresSaver).
3. S3 / Blob Storage: For large files created by the agent (images, PDFs).

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5. Deployment Overview: The Docker Compose Model

To run this entire stack locally or in the cloud, we use Docker Compose.

graph TD
    User -->|Internet| LB[Load Balancer]
    LB --> Frontend[React Container]
    LB --> Backend[FastAPI Container]
    Backend --> Redis[(Redis: Rate Limits)]
    Backend --> Postgres[(Postgres: Agent State)]
    Backend --> LLM[External LLM API]

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6. Real-World Context: Why is this Hard?

The difficulty is Persistence across Scale. If a user closes their laptop while an agent is mid-reasoning:

1. The SSE connection breaks.
2. The FastAPI request finishes.
3. The LangGraph process must keep running in the background until it hits its checkpoint.
4. When the user re-opens the app, the UI must "Re-attach" to the running thread and show the progress that happened while the user was away.

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Summary and Mental Model

Think of the Full Stack like a Restaurant.

• The Customer (React) is at the table.
• The Waiter (FastAPI) takes the order.
• The Chef (LangGraph) is in the kitchen.
• The Pantry (Postgres) is where the ingredients (State) are kept.

The integration is the process of building the "Intercom" system between these rooms.

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Exercise: Stack Mapping

1. The Flow: A user uploads a 50MB PDF.
   • Trace exactly where that file goes first. (React? FastAPI? S3?)
   • Who gives the "URL" of that file to the LLM?
2. Security: Why shouldn't the React app have the OPENAI_API_KEY?
3. Database Selection: If you are building a "Twitter Bot" agent that creates 1 million small updates per day, would you use PostgreSQL or Redis for the state? Why? Now we're ready for the code. Next lesson: Building the API Backbone.