#!/usr/bin/env bash # Hook: Stop (Codex) # # Fires when Codex finishes a turn. Reminds the agent to persist any # important learnings via the mem0 MCP tools before the turn closes. # # Input: JSON on stdin with session_id, turn_id, stop_hook_active, # last_assistant_message, transcript_path, cwd, # hook_event_name, model # Output: JSON on stdout (Codex rejects plain text on Stop). # - stop_hook_active=true -> {"continue": true} (let the turn end) # - stop_hook_active=false -> {"decision":"block","reason":"..."} # (continue the turn with the reminder as context) # # We must respect stop_hook_active or we'd loop forever: every "block" # reopens the turn, which triggers Stop again when the agent settles. set -uo pipefail if [ -n "${MEM0_DEBUG:-}" ]; then mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log" fi INPUT=$(cat) STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false") if [ "$STOP_HOOK_ACTIVE" = "true" ]; then printf '{"continue":true}\n' exit 0 fi REASON=$(cat <<'EOF' Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 `add_memory` tool: 1. Were any significant decisions made? -> Store with metadata `{"type": "decision"}` 2. Were any new patterns or strategies discovered? -> Store with metadata `{"type": "task_learning"}` 3. Did any approach fail? -> Store with metadata `{"type": "anti_pattern"}` 4. Did you learn anything about the user's preferences? -> Store with metadata `{"type": "user_preference"}` 5. Were there environment/setup discoveries? -> Store with metadata `{"type": "environmental"}` Memories can be as detailed as needed — include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners. If nothing notable happened in this interaction, it's fine to skip. Only store genuinely useful learnings. EOF ) jq -cn --arg reason "$REASON" '{decision:"block", reason:$reason}' exit 0