Workflow automation for scientific research
Run, monitor, and orchestrate complex analyses across heterogeneous compute environments—from laptops to HPC clusters.
What is asimov?
A workflow management framework that orchestrates complex scientific computing at scale.
asimov was created by the LIGO, Virgo, and KAGRA gravitational-wave physics collaborations to orchestrate hundreds of parameter estimation analyses. It managed the complete O3 and O4 observing run catalogues. But asimov isn't limited to gravitational waves—its extensible architecture makes it powerful for any large-scale scientific workflow.
Write your analysis logic in Python. Call shell scripts, compiled code, containers, or other languages from anywhere. asimov handles the orchestration, monitoring, dependency resolution, and fault recovery. It integrates with your existing compute infrastructure—whether that's HTCondor, SLURM, Kubernetes, or the cloud.
Built for research workflows
Asimov solves the practical problems researchers face when running computational analyses.
Unified pipeline interface
Write your analysis configuration once and deploy it across multiple pipelines. Switch between Bilby, LALInference, RIFT, Bayeswave, or custom codes without reconfiguring your analyses. Perfect for comparing methodologies or testing new approaches.
Automated job management
Asimov monitors your analyses, detects failures, and handles recovery automatically. It manages dependencies between jobs using DAG orchestration and submits work to HTCondor clusters. Focus on your research, not job babysitting.
Centralized configuration
All project metadata, configurations, and status tracked in a single YAML ledger. Hierarchical configuration with intelligent precedence (global → pipeline → event → analysis) means you define defaults once and override only what's needed.
Reproducible by design
Every configuration is version-controlled. Results are stored in read-only locations. Complete log archival and exact configuration replay ensure your analyses can be independently verified and reproduced.
Genuinely extensible
Add new pipelines via Python plugins without modifying core code. Hook system allows custom behavior at every stage. Template-based configuration generation supports any pipeline format. Built for customization, not just configuration.
Scale effortlessly
From single-event exploratory analysis on your laptop to hundreds of coordinated production runs on a cluster. The same configuration works at any scale. Proven in production for major gravitational wave catalogue analyses.
Why asimov?
Most workflow tools are either too generic (requiring substantial custom code) or too rigid (locking you into specific analysis approaches). Asimov strikes a balance: it understands scientific workflows while remaining flexible enough to adapt to your needs.
- ✓ No vendor lock-in: Plugin architecture means you can add any analysis pipeline
- ✓ Battle-tested: Used for major LIGO/Virgo/KAGRA catalogue production
- ✓ Multi-pipeline by design: Compare different codes on the same data seamlessly
- ✓ Collaborative: Built-in authentication, role-based access, and audit logging
- ✓ Python-native: Simple API for programmatic project management
- ✓ Self-healing: Automatic failure detection and recovery reduces manual intervention
Who uses asimov?
From single-event analyses to large-scale catalogue production.
Gravitational wave researchers
Coordinate parameter estimation across multiple pipelines and events. Automatically manage PSD generation, waveform selection, and post-processing workflows.
Catalogue production teams
Systematically process hundreds of events with consistent configurations. Track progress across the entire catalogue and identify problematic analyses at a glance.
Methodology comparison studies
Run the same analysis with different pipelines, approximants, or priors. Unified configuration means you change only what matters for your comparison.
Cross-institutional collaborations
Role-based access control and audit logging support team workflows. SciTokens integration provides institutional authentication.
Multi-stage data processing
Automatic dependency resolution ensures analyses run in the correct order. Share data between stages (e.g., PSDs from Bayeswave to Bilby) seamlessly.
Custom analysis pipelines
Plugin architecture allows you to integrate your own codes. Benefit from asimov's monitoring, configuration, and results management for any analysis tool.
How it works
Asimov manages the entire lifecycle of your analyses.
Configure
Define analyses in YAML blueprints
Submit
Asimov builds DAGs and submits jobs
Monitor
Automatic failure detection and recovery
Collect
Results organized and archived
Designed for extension
Asimov's plugin architecture means you're never locked in. Add new analysis pipelines by subclassing a simple Python interface. Use hooks to inject custom behavior at any stage. Template-based configuration generation adapts to any pipeline's input format.
The same extensibility that allows asimov to support multiple gravitational wave pipelines makes it straightforward to integrate with your own analysis codes. You get the benefit of robust workflow orchestration without rebuilding it yourself.
"We developed asimov because we needed to coordinate hundreds of analyses across different pipelines. It's designed to handle real research complexity."
Get started
Install asimov and create your first project in minutes.
Installation
# Install via pip
$ pip install asimov
# Or via conda
$ conda install -c conda-forge asimovCreate your first project
# Create a project directory
$ mkdir my-analysis && cd my-analysis
# Initialize asimov project
$ asimov init "My Analysis Project"
# Load default configurations
$ asimov apply -f https://git.ligo.org/asimov/data/-/raw/main/defaults/production-pe.yaml
# Add an event (e.g., GW150914)
$ asimov apply -f https://git.ligo.org/asimov/data/-/raw/main/events/gwtc-1/GW150914_095045.yaml
# Add an analysis
$ asimov manage analysis add bilby
# Check project status
$ olivaw monitorStart orchestrating your analyses
Join the researchers using asimov for reproducible, scalable workflow automation.
Get Started