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.
- Tool blocks show the latest activity while running and a count-based summary when the agent moves on to text. Completed work folds into an expandable "Worked for ..." line, leaving the final answer visible. The Extended work visualization setting keeps the full transcript open.
- 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 integrations, storage browsing, acquisition, datasets, jobs, model training, findings, and systems, plus user-defined 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, session transcripts, and a persistent private brain.
The CLI home structure looks like this:
~/.anomx/
config.toml
auth.json
skills/<command>/SKILL.md
skills/<command>/references/
brain/INDEX.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.
The agent's brain/ folder persists across sessions and synchronizes with the
same user's cloud agent before and after each turn. It can hold notes, plans, and
supporting files independently of the user's project. Offline edits remain local
until the next successful sync. Concurrent edits preserve a conflict copy instead
of silently overwriting changes. Keep credentials out of notes; sync is private
to the connected account and bounded to 512 paths, 1 MiB per file, and 8 MiB total.
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
For new flexible jobs, use the Darts-native Dataset, PyTorchModel, scorer and
detector APIs and the host-provided WorkContext. See
flexible jobs and ONNX training for a complete starter,
backend channel loading, custom networks, and portable model publication.
pip install "anomx[ml]"
python examples/flexible_job_training.py
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.
Runs and workspaces
Use anomx.init(project="experiment") and anomx.log("loss", value) for portable,
step-based execution tracking. See Runs and workspace editing for
offline logging, platform delivery, hosted jobs, retries, and multi-file editing.
1 maintainer
Metadata
1 maintainer
Release files for anomx 0.2.77
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