LogAn (Log Analyzer)

LogAn is an intelligent log analysis tool that extracts key insights for SREs/Support Engineers/Developers, to identify and diagnose ongoing issues from logs. It generates two reports: (1) Summary Report presents a table of the representative log lines — each with its predicted golden signals and fault categories — along with the frequency of its occurrence. By using this approach, we've found that the tool can reduce the data volume by up to 90%, since most log lines are informational. (2) Diagnosis Report presents a chronologically ordered set of relevant log windows with user-configurable granularity (e.g., 30s, 1m).

Key Features

How to Run

Option 1 - Using Containers (Recommended)

Running Container Image

We provide a container image that you can run standalone or integrate into automated pipelines to analyze logs.

mkdir -p ./tmp/output

podman run --rm \
    -v ./examples/:/data/input/:z \
    -v ./tmp/output/:/data/output/:z \
    -e LOGAN_INPUT_FILES="/data/input/Linux_2k.log" \
    -e LOGAN_OUTPUT_DIR=/data/output/ \
    ghcr.io/Log-Analyzer/LogAn:latest

Environment Variables

Variable	Default	Description
LOGAN_INPUT_FILES	/data/input	Input files/directories (comma-separated for multiple)
LOGAN_OUTPUT_DIR	/data/output	Output directory for analysis results
LOGAN_TIME_RANGE	all-data	Time range filter: all-data, 1-day, 2-day, ..., 1-week, 2-week, 1-month
LOGAN_MODEL_TYPE	zero_shot	Model type: zero_shot, similarity, custom
LOGAN_MODEL	crossencoder	Model for classification: bart, crossencoder, or custom HuggingFace model
LOGAN_DEBUG_MODE	true	Enable debug mode (saves additional metadata files)
LOGAN_PROCESS_LOG_FILES	true	Process .log files found in directories
LOGAN_PROCESS_TXT_FILES	false	Process .txt files found in directories
LOGAN_CLEAN_UP	false	Clean output directory before running

Build Container Image

If you want to customize the container image, you can clone this repository and build it manually:

podman build -t logan -f Containerfile .

Option 2 - Local

You can also directly clone this repository and set up the project without using a container. Make sure you have Python 3 and uv available on your system.

# Setup venv
uv venv
source .venv/bin/activate

uv pip install -r requirements.txt

# Run Log Analysis
uv run logan analyze \
    -f "examples/Linux_2k.log" \
    -o "tmp/output"

How to View the Reports (Output)

uv run logan view -d "tmp/output"

# server should be available at http://localhost:8000/log_diagnosis

Examples

Check out tutorials for more examples on advanced usages.

🔥 Citation

If you use LogAn for publication, please cite the following research papers:

• Pranjal Gupta, Karan Bhukar, Harshit Kumar, Seema Nagar, Prateeti Mohapatra, and Debanjana Kar. 2025. Scalable and Efficient Large-Scale Log Analysis with LLMs: An IT Software Support Case Study. AAAI, 2026.

• Pranjal Gupta, Karan Bhukar, Harshit Kumar, Seema Nagar, Prateeti Mohapatra, and Debanjana Kar. 2025. LogAn: An LLM-Based Log Analytics Tool with Causal Inferencing. ICPE, 2025.

• Pranjal Gupta, Harshit Kumar, Debanjana Kar, Karan Bhukar, Pooja Aggarwal and Prateeti Mohapatra.2023 Learning Representations on Logs for AIOps. IEEE CLOUD, 2023.

Authors & Contributors

This project was originally developed by IBM Research and is actively supported and maintained by Red Hat.

IBM Research

• Pranjal Gupta
• Harshit Kumar
• Prateeti Mohapatra

Red Hat

• Pradeep Surisetty
• Pravin Satpute
• Rahul Shetty
• Jan Hutar
• Nikhil Jain

We welcome contributions from the community!