The AI agent for coding, data analysis and anomaly detection.
Anomx
anomx is the installable Python foundation of Anomx. It ships both reusable
time-series and anomaly-detection primitives and the anomx CLI agent for
interactive anomaly investigation, data analysis, and platform-connected edge
workflows.
The wider Anomx platform handles orchestration, workers, storage, connectors, audit trails, and human-in-the-loop review. This repository contains the portable modeling layer plus the operator-facing CLI agent that can run on any server, workstation, or laptop.
Goals
- Provide clean primitives for time-series datasets, scorers, detectors, and models.
- Support both batch and online anomaly detection workflows.
- Keep outputs interpretable: scores, thresholds, timestamps, labels, and metadata.
- Ship a practical CLI agent that is tuned for anomaly detection, time-series inspection, and data-quality analysis tasks.
- Stay modular enough to power the Anomx platform without coupling the library to the platform's storage or orchestration layer.
- Integrate with the broader time-series ecosystem, especially Darts, through optional adapters.
Installation
The package is structured for PyPI distribution:
pip install anomx
For local development:
git clone https://github.com/anomx/anomx.git
cd anomx
python -m pip install -e ".[dev]"
Optional extras:
pip install "anomx[darts]" # Darts forecasting model adapters
pip install "anomx[docs]" # Documentation tooling
pip install "anomx[platform]" # Platform-facing IO and connector dependencies
pip install "anomx[release]" # Build and PyPI publishing tooling
Python Quick Start
from anomx.datasets import make_sine_anomaly_dataset
from anomx.detectors import MovingAverageDetector
from anomx.models import NaiveSeasonalModel
dataset = make_sine_anomaly_dataset()
detector = MovingAverageDetector(window=24, threshold=3.0)
result = detector.fit_predict(dataset)
print(result.to_dataframe().tail())
model = NaiveSeasonalModel(season_length=24).fit(dataset)
forecast = model.predict(12)
print(forecast.to_dataframe())
Anomx CLI Agent
Installing the package also installs the anomx console command:
anomx
The CLI agent is meant to feel like a modern coding/data agent, but tuned for anomaly detection, time-series investigation, data quality problems, and operational analysis.
Useful startup examples:
anomx --version
anomx --print-home
anomx --provider openai --model gpt-5.5
anomx --provider anthropic --model claude-sonnet-4-6
anomx --provider kimi --model kimi-k3
anomx --ollama --model qwen3-coder:30b
Current CLI capabilities include:
- A full-screen terminal UI with persisted transcripts and session history.
- Multiple model backends: OpenAI, Anthropic, Kimi, DESY Assistant, JSC Blablador, and local Ollama.
- Five execution modes: Plan, Recommend (with a connected platform), Standard, Automatic, and Autonomous.
- Connected-platform skills for the Anomx API, recommendations, data, jobs, and systems, plus user-defined slash-command skills.
- Tool-backed repository and data inspection, plus background Worker agents for focused parallel tasks.
- A small inspectable home directory at
~/.anomx(orANOMX_HOME) that stores config, auth metadata, skills, and session transcripts.
The CLI home structure looks like this:
~/.anomx/
config.toml
auth.json
skills/<command>.md
session_index.jsonl
sessions/YYYY/MM/DD/rollout-<timestamp>-<id>.jsonl
Platform Connection
When an Anomx Platform instance is available, the CLI can be connected directly from the agent UI using normal platform credentials. The platform issues a dedicated CLI-agent token, tracks the CLI host name and client version, and keeps the machine attached to the same organization context as the platform.
That makes anomx the practical edge entry point for working where the data
lives first, then carrying that context back into the platform for visualization,
findings, follow-up jobs, and broader analysis workflows.
Package Layout
src/anomx/
agent/ Full-screen CLI agent, providers, skills, platform client
data/ Connectors and sequence containers for local and edge workflows
datasets/ TimeSeriesDataset, metadata, dataset loaders, transforms
scorers/ Anomaly score contracts and scoring implementations
detectors/ Batch and online anomaly detector contracts and implementations
models/ Forecasting model contracts and baseline forecasters
components/ Reusable component and offline pipeline abstractions
integrations/ Optional adapters, including Darts
Darts Integration
The default install stays lightweight. To use Darts models, install the optional extra and wrap any compatible Darts forecasting model:
from darts.models import ExponentialSmoothing
from anomx.datasets import make_sine_anomaly_dataset
from anomx.integrations import DartsForecastingModel
dataset = make_sine_anomaly_dataset()
model = DartsForecastingModel(ExponentialSmoothing())
forecast = model.fit(dataset).predict(24)
The intent is to let Anomx expose Darts-backed forecasting and anomaly workflows without making Darts a required dependency for every user.
Development
python -m pip install -e ".[dev]"
pytest
ruff check .
mypy src/anomx
python -m pip install -e ".[release]"
python -m build
Platform Context
Anomx prioritizes:
- Signal over noise: surface only relevant insights.
- Clarity over complexity: outputs should be interpretable.
- Actionability: every insight should support a decision.
The platform is designed around modular pipelines, real-time and batch workers, heterogeneous data sources, and versioned entities such as datasets, channels, jobs, runs, findings, model artifacts, and node services. This package is the focused modeling and agent layer used by that larger system.
Status
This repository is in pre-alpha stage. Public APIs and CLI ergonomics should be treated as provisional until the first stable release.
1 maintainer
Metadata
1 maintainer
Release files for anomx 0.2.48
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| anomx-0.2.48.tar.gz | 874.2 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| anomx-0.2.48-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / anomx-0.2.48.tar.gz
| Download URL | anomx-0.2.48.tar.gz |
|---|---|
| Size | 874.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
fc2591ba65260dab87e05ce139ededb3a63433ba7f6cdbff348330ab274f5c24
|
|
BLAKE2b-256 checksum How to use checksums |
7170e18607d3ea6b0cfea25c6649e95b6730e25f841da734de3850d8369c2b77
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.7
|
Release files / anomx-0.2.48-py3-none-any.whl
| Download URL | anomx-0.2.48-py3-none-any.whl |
|---|---|
| Size | 396.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
1ddee85361a09e7b839a015ee95ad9cf83a19998bd253a8b3c3d019ff37be960
|
|
BLAKE2b-256 checksum How to use checksums |
7baf018d946ae7405891e6d1d9a651f261c3d1b0f8b48ed6f4732cab0ade274f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.7
|
Security for anomx 0.2.48
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
