LogLead

LogLead performs log loading, log enhancement, log feature engineering, log analysis, log anomaly detection also via MCP-server.

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No install snippet on purpose. A working MCP config is a command, its arguments and an environment block — the last two are where API keys live, so this catalogue never stores them and cannot publish them. Follow the link above for the authors' own instructions.

LogLead is designed to efficiently benchmark log anomaly detection algorithms and log representations. LogLead is also used as a backend for projects such as LogDelta and VisualLogAnalyzer, which offer a more user-friendly approach to log analysis and log anomaly detection. MCP-server of Loglead allow AI agents like Claude code to perform log analysis with loglead.

Table of contents

Installing LogLead

Install with uv:

uv add loglead

Or with pip:

python -m pip install loglead

Then clone the project, move to demo folder and run some demos

git clone https://github.com/EvoTestOps/LogLead.git
cd LogLead
uv run demo/HDFS_samples.py
uv run demo/TB_samples.py

Or with pip (after installing LogLead into your environment):

cd LogLead/demo
python HDFS_samples.py
python TB_samples.py

uv run syncs the environment from pyproject.toml/uv.lock on first use, so there's no separate install step before running anything.

To start working with your own data, it is easiest to begin with the RawLoader. To try out RawLoader, run the RawLoaderDemo. For this, you will need the original BGL and HDFS datasets. You will also need to edit the RawLoaderDemo script or add a ".env" file to your LogLead root so that the demo knows where the data is located on your machine. See .env.sample as an example of how the ".env" file should look. After that run the demo

uv run demo/RawLoader_NoLabels.py

Or with pip:

python RawLoader_NoLabels.py

Finally, you can try downloading data. The downloader script fetches the public datasets listed in downloader/datasets.yml. See what's on offer, then pick what you want with --datasets:

uv run downloader/download_data.py --list
uv run downloader/download_data.py --datasets openstack-line-labels-22 hdfs

--datasets is a whitelist: only the datasets you name are fetched and every other entry in the config is skipped, whatever its download: flag says. Conversely a dataset you do name is fetched even if its entry says download: false. An unknown name stops the script before anything is downloaded, and the run starts by printing what it selected and how many entries it ignored. Leave --datasets off to download everything the config enables — that's ~104 GB unzipped for datasets.yml, so check the disk space note below first:

uv run downloader/download_data.py

Or with pip (after cloning the repo):

python downloader/download_data.py --datasets openstack-line-labels-22

If you've cloned the repo and want to run the test suite too, point it at one of the

From the project's README.