Codex Autoresearch is an autonomous, goal-driven experimentation CLI tool for Codex. It allows users to define a numeric target (e.g., reduce error count to zero) and then repeatedly runs a loop: the Codex model inspects the repo, makes one focused code change, the control script commits it, runs a verification command to measure the metric, keeps improvements or reverts failures, and appends audit events. The system supports both foreground (Codex Goal-driven) and background (detached controller with `codex exec` workers) modes, with strict state validation, atomic file operations, and Git-based rollback. Architecture: The codebase is split into three Python modules under `scripts/`—`autoresearch.py` (CLI and lifecycle orchestration), `autoresearch_core.py` (primitives for Git, command execution, event validation, and process management), and `autoresearch_report.py` (read-only rendering of history as terminal tables, TSV, or self-contained HTML). State is stored in `autoresearch-results/` as immutable `run.json` and append-only `events.jsonl`. The skill entrypoint is `SKILL.md`, which references three documentation files. The design emphasizes contract enforcement over silent recovery: malformed input, unexpected Git drift, or failed commands halt with precise diagnostics. The repository demonstrates high craftsmanship in defensive programming, strict JSON schema validation, atomic file writes, process tree management, and comprehensive unit/integration tests (including race condition testing for background controllers). It is a practical and well-documented example of building a reliable autonomous agent harness for codebases.
EvoMap Evolver is a self-evolving AI agent engine built as a Node.js >=22.12 CLI daemon. It runs a genome-evolution loop: scans session memory/logs, extracts signals, selects 'Genes' (prompt/strategy patches) and 'Capsules' (validated skill bundles) from a local JSON/JSONL asset store, generates GEP-framed prompts for coding agents (Claude Code, Codex, Cursor, Gemini CLI, opencode, Kiro), executes those sessions, and records outcomes in a memory graph to adapt future selections. The system solidifies successful patterns into new reusable assets, auto-distills failure patterns and SKILL.md files into Genes/Capsules, and can open PRs or run validation sandboxes against candidate changes. It also ships an LLM proxy (Anthropic/OpenAI/Gemini/Vertex/Ollama routes with trace encryption and token-savings attribution), an ATP (Agent Transaction Protocol) marketplace for buying/completing agent tasks with auto-buyer and auto-deliver, a validation daemon for decentralized task verification, a local Web UI dashboard, and an experiment runner for A/B comparing evolution strategies. Hub synchronization (SSE event delivery, mailbox sync, OAuth device login) connects nodes to the evomap.ai network, but the system is designed to degrade gracefully into fully offline solo mode.