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mem0/mem0-plugin/scripts/on_stop.sh
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2026-03-25 14:45:59 -07:00

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#!/usr/bin/env bash
# Hook: Stop
#
# Fires when Claude finishes responding.
# Reminds Claude to store any unsaved learnings, then spawns a background
# process to capture transcript state via the Mem0 REST API directly.
#
# Input: JSON on stdin with stop_hook_active, transcript_path, cwd
# Output: Text that becomes Claude's context (exit 0), or nothing
#
# IMPORTANT: Check stop_hook_active to avoid infinite loops.
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
INPUT=$(cat)
STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false")
if [ "$STOP_HOOK_ACTIVE" = "true" ]; then
exit 0
fi
cat <<'EOF'
Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 `add_memory` tool:
1. Were any significant decisions made? -> Store with metadata `{"type": "decision"}`
2. Were any new patterns or strategies discovered? -> Store with metadata `{"type": "task_learning"}`
3. Did any approach fail? -> Store with metadata `{"type": "anti_pattern"}`
4. Did you learn anything about the user's preferences? -> Store with metadata `{"type": "user_preference"}`
5. Were there environment/setup discoveries? -> Store with metadata `{"type": "environmental"}`
Memories can be as detailed as needed — include full context, reasoning, code snippets, file paths, and examples. Longer, searchable memories are more valuable than vague one-liners.
If nothing notable happened in this interaction, it's fine to skip. Only store genuinely useful learnings.
EOF
# Capture transcript state in the background via Mem0 REST API
echo "$INPUT" | python3 "$SCRIPT_DIR/on_pre_compact.py" --source=session-end 2>/dev/null &
exit 0