Kartik
72 lines
3.1 KiB
Bash
Executable File
72 lines
3.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Hook: Stop (Codex)
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#
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# Fires when Codex finishes a turn. Reminds the agent to persist any
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# important learnings via the mem0 MCP tools before the turn closes.
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#
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# Input: JSON on stdin with session_id, turn_id, stop_hook_active,
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# last_assistant_message, transcript_path, cwd,
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# hook_event_name, model
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# Output: JSON on stdout (Codex rejects plain text on Stop).
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# - stop_hook_active=true -> {"continue": true} (let the turn end)
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# - stop_hook_active=false -> {"decision":"block","reason":"..."}
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# (continue the turn with the reminder as context)
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#
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# We must respect stop_hook_active or we'd loop forever: every "block"
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# reopens the turn, which triggers Stop again when the agent settles.
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set -uo pipefail
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if [ -n "${MEM0_DEBUG:-}" ]; then
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mkdir -p "$HOME/.mem0" && exec 2>>"$HOME/.mem0/hooks.log"
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fi
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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# shellcheck source=_identity.sh
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. "$SCRIPT_DIR/_identity.sh"
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INPUT=$(cat)
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STOP_HOOK_ACTIVE=$(echo "$INPUT" | jq -r '.stop_hook_active // false' 2>/dev/null || echo "false")
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if [ "$STOP_HOOK_ACTIVE" = "true" ]; then
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printf '{"continue":true}\n'
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exit 0
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fi
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# Telemetry: fire before report() deletes stats file
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_TELEM_CAT=$(python3 "$SCRIPT_DIR/session_stats.py" peek 2>/dev/null | python3 -c "import json,sys; d=json.load(sys.stdin); print(len(d.get('categories',[])))" 2>/dev/null || echo "0")
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python3 "$SCRIPT_DIR/telemetry.py" stop --categories_count="$_TELEM_CAT" 2>/dev/null &
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# Session-end report (best-effort, must not break JSON output)
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REPORT=$(python3 "$SCRIPT_DIR/session_stats.py" report 2>/dev/null || echo "")
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REPORT_BLOCK=""
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if [ -n "$REPORT" ]; then
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REPORT_BLOCK="---\nmem0 $REPORT\n---\n\n"
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mkdir -p "$HOME/.mem0" 2>/dev/null || true
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echo "$(date -u +%Y-%m-%dT%H:%M:%SZ) | $REPORT" >> "$HOME/.mem0/session-log.md" 2>/dev/null || true
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fi
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REASON=$(cat <<EOF
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${REPORT_BLOCK}Before finishing, check if there are important learnings from this interaction that should be persisted using the mem0 \`add_memory\` tool:
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1. Were any significant decisions made? -> Store with metadata \`{"type": "decision"}\`
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2. Were any new patterns or strategies discovered? -> Store with metadata \`{"type": "task_learning"}\`
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3. Did any approach fail? -> Store with metadata \`{"type": "anti_pattern"}\`
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4. Did you learn anything about the user's preferences? -> Store with metadata \`{"type": "user_preference"}\`
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5. Were there environment/setup discoveries? -> Store with metadata \`{"type": "environmental"}\`
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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.
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Always include \`app_id\` (the active project_id from SessionStart) as a top-level parameter in every \`add_memory\` call.
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If nothing notable happened in this interaction, it's fine to skip. Only store genuinely useful learnings.
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EOF
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)
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jq -cn --arg reason "$REASON" '{decision:"block", reason:$reason}'
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# Capture transcript state in the background via Mem0 REST API
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echo "$INPUT" | python3 "$SCRIPT_DIR/on_pre_compact.py" --source=session-end 2>/dev/null &
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exit 0
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