How to Build a LangGraph Agent with FastAPI and SSE Streaming

Building an AI agent is relatively straightforward. Making it accessible through an API, integrating real tools, and streaming execution updates to a client is where the architecture becomes more interesting.

In this tutorial, we'll look at a small, working example that combines:

• LangGraph for agent orchestration
• FastAPI for HTTP endpoints
• Server-Sent Events (SSE) for streaming responses
• Open-Meteo as a real external tool

The goal isn't to build a production-ready platform in one tutorial. It's to establish a working foundation and understand the responsibilities of each component.

Source code: LangGraph + FastAPI Example on GitHub

1. Why LangGraph and FastAPI?

LangGraph handles the agent's execution flow.

FastAPI exposes that execution to external clients.

These are different responsibilities, and separating them makes the application easier to understand and extend.

A useful mental model is:

The client shouldn't need to understand how the agent chooses tools or manages its internal execution flow.

It should interact with a clear HTTP interface.

2. Give the agent a real tool

An agent becomes more interesting when it can do something beyond generating text.

In this example, the agent uses a weather tool backed by Open-Meteo.

That introduces a realistic execution path:

1. The user asks a weather-related question.
2. The model determines whether it needs the tool.
3. The tool requests external weather information.
4. The result returns to the agent.
5. The agent uses the result to answer.

This is a useful foundation for more advanced agents that interact with APIs, databases, and business systems.

It also introduces concerns that don't exist in a simple LLM completion: network failures, tool errors, latency, and retries.

3. Expose the agent through FastAPI

The next step is making the agent accessible through an HTTP API.

The example demonstrates two response patterns.

Regular JSON responses

The client sends a request and waits for the agent's result.

This approach is appropriate for simple integrations where intermediate execution updates aren't necessary.

Streaming responses

The client receives updates while the request is being processed.

This is useful when agent execution involves multiple operations and the user would otherwise wait without feedback.

Both patterns can be built around the same agent architecture.

4. Why use Server-Sent Events?

Server-Sent Events provide a way to deliver a stream of updates over an HTTP response.

Instead of waiting for one final response, the client can process events as they arrive.

A simplified SSE message looks like this:

event: message
data: {"content":"Processing your request..."}

The example above illustrates SSE formatting, not the exact event contract of the repository.

For AI applications, streaming can improve the experience by making long-running operations more visible.

However, streaming also introduces engineering considerations:

• What happens when the client disconnects?
• How are errors communicated after the stream begins?
• How does the client recognize completion?
• What happens if a tool fails halfway through execution?

These questions matter when moving from a working demonstration to a reliable service.

5. Keep the agent separate from the API

One important design principle is to avoid mixing agent orchestration with HTTP transport logic.

The agent should own its execution flow and tool interactions.

The FastAPI layer should handle requests, responses, and streaming transport.

That separation makes it easier to test the agent independently, evolve the API, and introduce additional clients later.

It also creates a clearer boundary for future capabilities such as authentication, persistence, and observability.

6. What this example does not solve yet

A working agent API is not automatically production-ready.

Before using this architecture for critical workflows, several additional concerns need attention.

Persistence and checkpointing

Long-running or multi-step agents may need to recover execution state after interruptions.

Retries and idempotency

Retrying a failed operation can be dangerous when tools modify external systems.

For example, retrying a payment operation must not charge the same customer twice.

Checkpointing alone doesn't guarantee idempotency. External side effects need their own protection.

Observability

Production systems need visibility into agent execution, LLM calls, tool inputs and outputs, errors, and latency.

LangSmith is one option for tracing LangGraph applications at the execution, node, and tool-call levels.

Security and operational controls

Authentication, rate limiting, secrets management, structured logging, and deployment configuration need to be addressed according to the environment.

These aren't reasons to avoid building a small example. They're reasons to distinguish a working foundation from a complete production system.

7. Run the example yourself

The public repository contains the implementation and setup instructions.

GitHub: https://github.com/salada-dados/langgraph-fastapi-example

Start with the README, run the example locally, and inspect how the agent, tool integration, FastAPI endpoints, and SSE streaming fit together.

The most useful next exercise is to replace the weather tool with an API relevant to your own application.

That helps expose the operational questions you'll eventually need to solve.

8. From a working example to production

I'm also building a Production LangGraph + FastAPI Starter, focused on reducing the repetitive engineering work involved in taking an agent beyond a local demonstration.

The goal is to provide a production-oriented starting point rather than claim that every deployment concern can be solved by a template.

If you're working on an agent and running into challenges with persistence, retries, observability, or deployment, I'd be interested in hearing what's been hardest.

Interested in the production-oriented Starter? Join the Early Access list.

Read the original tutorial: https://saladadedados.com/en/blog/langgraph-fastapi-sse

Explore the source code: https://github.com/salada-dados/langgraph-fastapi-example

What has been the hardest part of moving your LangGraph agent from a local prototype to a real application?