opp-repl 0.1.3

OMNeT++ Python read-eval-print-loop for running and testing simulations

opp_repl

An interactive Python REPL for OMNeT++ — run simulations, compare results, optimize parameters, and run a wide range of regression tests. Provides an MCP server for AI assistants. All features are accessible from both the interactive REPL and command-line tools. See the Overview for the full feature list.

Installation

Requires Python 3.10+.

pip install opp_repl

See Installation for details on optional extras and environment setup.

Quick Start

First, source the OMNeT++ environment:

. /path/to/omnetpp/setenv

Then launch the REPL using existing omnetpp installation:

opp_repl --load "etc/*.opp"

Then run simulations from the REPL:

In [1]: run_simulations(simulation_project=fifo_project)

See Getting started for a full walkthrough.

Documentation

• Overview — features, CLI options, command-line tools
• Installation — requirements, install, optional extras, environment setup
• Getting started — first launch, running simulations, next steps
• The REPL — launch options, namespace, autoreload, user module
• Concepts — core concepts and how they fit together
• OPP files — .opp file format, parameters, and examples
• Simulation workspaces — project registry, loading, lookup, defaults
• OMNeT++ projects — OMNeT++ installations, executables, building
• Simulation projects — model projects, source layout, dependencies, building
• Simulation configs — INI file discovery, filtering, run counts
• Simulation tasks — task creation, runners, build modes, re-running
• Task results — result codes, inspection, filtering, re-running
• Running simulations — running simulations, building projects, cleaning
• Comparing simulations — simulation comparison
• Smoke tests — smoke tests
• Fingerprint tests — fingerprint tests
• Statistical tests — statistical tests
• Speed tests — speed tests
• Chart tests — chart tests
• Sanitizer tests — sanitizer tests
• Feature tests — feature tests, release tests, running all tests
• Bisecting — bisecting git commits for test failures
• Parameter optimization — parameter optimization
• Code coverage — coverage reports
• Profiling — performance profiling with perf and Hotspot
• Overlay builds — overlay builds
• Cluster — SSH cluster execution
• GitHub Actions — GitHub Actions integration
• MCP server — MCP server for AI assistants