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Changelog

All notable changes to this project will be documented in this file.

[1.0.0] — 2026-02-14

Initial Release

A replication-aware worker system with explicit contracts, sandboxed orchestration, and structured observability. Designed for studying and testing AI agent replication policies in a controlled environment.

Features

  • Replication Contract — Configurable policy with max_depth, max_replicas, cooldown_seconds, optional expiration_seconds, and pluggable stop conditions evaluated during worker registration.
  • Manifest Signing — HMAC-SHA256 signed manifests that capture cloned state, resource quotas, parent/child lineage, and issuance time. Tamper-proof verification on registration.
  • Controller — Central authority that issues/signs manifests, enforces depth limits, replica quotas, cooldown periods, and stop conditions. Includes kill switch, audit trail, and stale worker reaping.
  • Sandbox Orchestrator — Simulates isolated container environments with CPU/RAM limits and network policy (egress-only to controller). Records lifecycle events and emits resource metrics.
  • Worker — Task-executing agent that registers with the controller, heartbeats, handles expiration, and optionally self-replicates subject to the contract.
  • Observability — Structured in-memory logger with audit trail support and metrics emission for lifecycle events, replication decisions, and resource enforcement.

Infrastructure

  • GitHub Actions CI with flake8 linting, mypy type checking, and pytest across Python 3.10/3.11/3.12.
  • Multi-stage Dockerfile for containerized deployment.
  • Comprehensive test suite covering replication depth, quota enforcement, stop conditions, kill switch, heartbeat reaping, and chaos scenarios.