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)

container.sh contains wrapper for building and running the LogAn as container.

Build Container Image

bash container.sh build ## You can change ENV=docker/podman in the file

Running Container Image

1. Execute container.sh as follows:

   bash container.sh run OUTPUT_DIR LOG_FILE_PATH TIME_RANGE(OPTIONAL) -ProcessLogFiles(OPTIONAL) -ProcessTxtFiles(OPTIONAL) -DebugMode(OPTIONAL) 

   • OUTPUT_DIR - The directory where the tool's reports are stored
   • LOG_FILE_PATH - Folders/files separated by colon(:).
   • TIME_RANGE - Run analysis only on log lines that fall in the time range determined by the latest date in the data (Allowed values: [1-6]-day, [1-3]-week, 1-month, all-data)
   • -ProcessLogFiles - Enable this to process .LOG files (found in folders). This will not affect .LOG files that you have provided explicitly.
   • -ProcessTxtFiles - Enable this to process .TXT files (found in folders). This will not affect .TXT files that you have provided explicitly.
   • -DebugMode - Enable this flag to store metadata generated during a job for troubleshooting

   Example:

       bash container.sh run ./tmp/output ./examples/Linux_2k.log all-data

   In the above example:

   • ./examples/Linux_2k.log - File
   • ./tmp/output - Directory

Option 2 - Local

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

uv pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu
uv pip install -r requirements.txt

# Run Log Analysis
export OUTPUT_DIR="./tmp/output"

uv run python run_log_diagnosis.py \
    --input_files "./examples/Linux_2k.log" \
    --output_dir "$OUTPUT_DIR" \
    --model-name "cross-encoder/nli-MiniLM2-L6-H768"

How to View the Reports (Output)

uv run python -m http.server 8000 --directory "${OUTPUT_DIR}"

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

🔥 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!