Modern systems produce large volumes of logs.
Application logs, server logs, and infrastructure logs are often the first clue when something is broken. The problem is not the lack of data, but the effort required to read and understand it.
Engineers typically scroll through thousands of lines, looking for error codes, and trying to correlate events over time. It is slow and error prone, especially during events.
A loganalyzer agent solves this problem by acting like a calm, experienced engineer who reads the logs for you and explains what’s going on.
In this article, you will learn how to build such an agent Fastpyfor , for , for , . Lingchenand an OpenAI model.
We’ll walk through the backend, log analysis logic, and a simple web UI that lets you upload a log file and get insights in seconds. We will also upload this app to Seoul so you can share your project with the world.
You only need some basic knowledge of Python and HTML/CSS/JavaScript to finish this tutorial.
Here is the complete code For reference.
What we will cover
What the Lognealizer Agent actually does
A loganalyzer agent takes raw log text as input and produces a human-friendly analysis as output.
Instead of returning a list of errors, it describes the main failures, the possible root cause, and what to do next. This is important because logs are written for machines, not for people under pressure.
In this project, the agent behaves like a senior site reliability engineer. It reads the log in chunks, identifies patterns, and summarizes them in plain language. Intelligence comes from a language model, while reliability comes from careful input handling and collisions.
High level architecture
This system has three main parts.
The first part is a web UI built with simple HTML, CSS, and JavaScript. This UI allows the user to upload a text file and start analysis.
The second part is a FastPI backend that receives the file, validates it, and coordinates the analysis.
The third part is the self-analysis engine, which uses Langchain and the OpenAI model to interpret logs.
The flow is simple: the browser sends a log file to the backend. The backend reads the file, splits it into manageable chunks, and sends each chunk to the language model with an explicit pointer. The responses are combined and sent back to the browser as an analysis.
Designing a working gesture
The heart of any AI agent is cues. A weak cue gives vague answers, while a strong cue produces useful insights.
In this project, the gesture asks the model to act like a senior site reliability engineer. It asks for four things: critical errors, probable root cause, practical next steps, and suspicious patterns.
The quick template used in the backend is:
log_analysis_prompt_text = """
You are a senior site reliability engineer.
Analyze the following application logs.
1. Identify the main errors or failures.
2. Explain the likely root cause in simple terms.
3. Suggest practical next steps to fix or investigate.
4. Mention any suspicious patterns or repeated issues.
Logs:
{log_data}
Respond in clear paragraphs. Avoid jargon where possible.
"""
This tip is simple but effective. It gives the model a role, a defined task and constraints on the output style. Asking for clear paragraphs helps ensure that the answer is readable and useful even for non-experts.
Secure handling of large log files
Language models have input limitations. You can’t send a large log file in one request and expect good results. To handle this, the backend splits the log into smaller chunks. Each section overlaps slightly with the next to preserve context.
We will use RecursiveCharacterTextSplitter From Lingchen for this purpose. This ensures that pieces are not cut in awkward places and that important lines are not lost.
def split_logs(log_text: str):
"""Split log text into manageable chunks"""
splitter = RecursiveCharacterTextSplitter(
chunk_size=2000,
chunk_overlap=200
)
return splitter.split_text(log_text)
This approach allows the agent to scale large files while staying within the model’s limits. Each part is analyzed independently, and the results are later combined.
Analyzing Logs with Lengchain and Openei
Once the logs are split, each part is passed through the language model using a prompt template. The model used here is a lightweight but capable option, designed with low temperatures to keep the reaction focused and consistent.
llm = ChatOpenAI(
temperature=0.2,
model="gpt-4o-mini"
)
The parse function runs on all parts, shapes the pointer, invokes the model, and stores the result.
def analyze_logs(log_text: str):
"""Analyze logs by splitting and processing each chunk"""
chunks = split_logs(log_text)
combined_analysis = ()
for chunk in chunks:
formatted_prompt = log_analysis_prompt_text.format(log_data=chunk)
result = llm.invoke(formatted_prompt)
combined_analysis.append(result.content)
return "\n\n".join(combined_analysis)
It keeps the design logic easy to understand. Each produces a small analysis, and the final output is a description of the entire log file together.
Building a FastPy backend
Fastpy is a good choice for this project because it is fast, simple and easy to read. The backend exposes three endpoints. The root endpoint serves the HTML UI. /analyze The endpoint accepts a log file and returns an analysis. And /health The endpoint is used to check if the service is running and properly configured.
The analysis endpoint performs several important checks. It ensures that the file is a text file, verifies that it is not empty, and handles errors gracefully. This prevents unnecessary calls to the model and improves the user experience.
@app.post("/analyze")
async def analyze_log_file(file: UploadFile = File(...)):
"""Analyze uploaded log file"""
if not file.filename.endswith(".txt"):
return JSONResponse(
status_code=400,
content={"error": "Only .txt log files are supported"}
)
try:
content = await file.read()
log_text = content.decode("utf-8", errors="ignore")
if not log_text.strip():
return JSONResponse(
status_code=400,
content={"error": "Log file is empty"}
)
insights = analyze_logs(log_text)
return {"analysis": insights}
except Exception as e:
return JSONResponse(
status_code=500,
content={"error": f"Error analyzing logs: {str(e)}"}
)
This careful handling makes the agent more robust and production friendly.
Creating a simple and clean web UI
A good agent is not useful if people cannot easily communicate with it. The project consists of a single HTML file with front-end embedded CSS and JavaScript. It focuses on clarity and speed rather than complexity.
The UI allows users to select a log file, view the file name, click the Analyze button, and view the results in a formatted area. The loading spinner provides feedback while the analysis is running. Errors are clearly shown without technical noise.
The upload and parsing logic is handled by a small JavaScript function that sends the file to the backend using a retrieve request.
async function uploadLog() {
const fileInput = document.getElementById("logFile");
const file = fileInput.files(0);
if (!file) {
alert("Please select a log file first");
return;
}
const formData = new FormData();
formData.append("file", file);
const response = await fetch("/analyze", {
method: "POST",
body: formData
});
const data = await response.json();
document.getElementById("result").textContent = data.analysis;
}
This minimalistic approach keeps the front end easy to maintain and adapt.
Running the application locally
To run this project, you need Python, a virtual environment, and an OpenAI API key. The API key is loaded with a .env file to keep the code away from the code. Once the dependencies are installed, you can start the server using UVCorn.
if __name__ == "__main__":
import uvicorn
port = int(os.getenv("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)
After starting the server, you can open a browser, upload a log file, and watch the agent in action.
Deployed to Seoul
You can choose any cloud provider, such as AWS, Digitalosine, or others, to host your service. I’ll use Siola for this example.
Seoul is a developer-friendly PAAS provider. It offers application hosting, database, object storage, and static site hosting for your projects.
Each platform will charge you to create a cloud resource. Siola comes with a $20 credit for our use, so we won’t charge for this instance.
Let’s push this project to GitHub so that we can connect our repository to Seolla. We can also enable auto-deployments so that any new changes to the repository are automatically deployed.
Login Click on Seolako and Applications → Create New Application.
You can see the option to link your GitHub repository to create a new application. Use the default settings. Then click Create a request.
Now we need to add our openui API key to the environment variables. Click on Environmental variables Section Once the application is created, and save OPENAI_API_KEY value as an environmental variable.
Now we are ready to deploy our application. click Deployment And click Deploy now. The deployment will take 2–3 minutes to complete.
Once complete, click View the app. You will see a request ending with a URL sevalla.app. This is your new root URL. You can change localhost:8000 With this URL and start using it.
Congratulations! Your Login Analyzer is now live. You can find a sample log in the GitHub repository that you can use to test the service.
You can extend it by adding other capabilities and pushing your code to GitHub. Seola will automatically deploy your application to production.
The result
A practical way to construct a naturalizer agent is to apply the language model to real engineering problems. Logs are everywhere, and understanding them quickly can save hours during events. By combining FastPy, LangChain, and an explicit notation, you can turn raw text into actionable insights.
The key ideas are simple: distribute large inputs, guide the model with strong roles and functions, and present the results in a clean interface. With these principles, you can adapt this agent to many other analysis tasks outside of logs.
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