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Upgrading

Notes for moving between releases. Each entry lists only user-affecting changes.

0.2.x to 0.3.0

  • Study runner is a package CLI. Use silisocs-study (or python -m silisocs.studies.run_study). The legacy study script shims and aliases were removed.
  • Scenarios resolve by name. The scenario library is bundled and addressed by name, for example --config-path election. Example scenarios are no longer shipped in the installed wheel; reference a bundled scenario by name or pass an explicit path.
  • Class paths are validated at startup. An invalid class_path now fails fast during construction instead of part-way through a run.
  • num_agents is a declared total, not a cap. When a class needs more agents than it has personas, the persona records recycle with numbered suffixes, and a mismatch between the built agent count and num_agents logs a warning.
  • Checkpoint restore is atomic and checkpoint-owned. Restore validates then applies state (see ADR 0003). Multi-GM runs keep per-GM logs and support per-GM restore overrides; the Mastodon backend self-restores from its embedded action history.
  • Auto-resume is on by default. A run now resumes from its own output directory when that directory already contains checkpoints, unless you set an explicit sim.checkpoint.source_run. A fresh output directory (the usual case) still starts from scratch. Set sim.checkpoint.auto_resume: false to force a fresh start into a directory that already has checkpoints.
  • More built-in LLM providers. Common providers are available as named presets (anthropic, gemini, openrouter, groq, together, deepseek, mistral, fireworks, xai, ollama). Set sim.llm.provider to the name; see Configuration. Existing openai / openai_compatible configs are unchanged.