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 "opp/*.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

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