kartik-mem0
82ab253f2c
- Sanitize project_id in onboard marker path (tr '/:' '--') to handle slashes from env var overrides - Fire on_pre_compact.py REST backup from PreCompact hook (was only called from Stop hooks — crash before Stop = data loss) - Source _identity.sh in on_stop_codex.sh and on_stop_cursor.sh so MEM0_PROJECT_ID + API key fallback are available - Note dream --schedule is Claude Code-only (Codex/Cursor lack schedule)
86 lines
3.1 KiB
Bash
Executable File
86 lines
3.1 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Hook: PreCompact
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#
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# Fires BEFORE context compaction. This is the last chance to capture
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# the full context before it gets compressed.
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#
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# Output: Text instructions injected into Claude's context.
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# Claude still has the full conversation and can write an accurate summary,
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# which it stores via add_memory(infer=False) so the platform preserves
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# the structure verbatim instead of running a second extraction pass.
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set -euo 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 "$0")" && pwd)"
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INPUT=$(cat)
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# Fire REST API transcript backup in background (safety net if agent
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# can't complete the add_memory call before compaction finishes)
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echo "$INPUT" | python3 "$SCRIPT_DIR/on_pre_compact.py" --source=pre-compaction 2>/dev/null &
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cat <<'EOF'
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## CRITICAL: Pre-Compaction Session Summary
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Context compaction is about to happen. You are about to lose most of your conversation history. You MUST store a comprehensive session summary NOW using the mem0 `add_memory` tool.
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### Step 1: Store session summary
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Call `add_memory` with `infer=False` and a thorough summary covering ALL of the following.
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`infer=False` is critical here: you've already done the extraction work yourself using full context. Without it, the platform runs a second LLM pass that loses your structure and pulls fragmented facts. With it, your summary is preserved verbatim.
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```
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## Session Summary (Pre-Compaction)
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### User's Goal
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[What the user originally asked for and their intent]
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### What Was Accomplished
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[Numbered list of tasks completed, features built, bugs fixed]
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### Key Decisions Made
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[Architectural choices, design decisions, trade-offs discussed]
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### Files Created or Modified
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[List of important file paths with what changed in each]
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### Current State
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[What is in progress RIGHT NOW — the task you were in the middle of]
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[Any pending items, blockers, or next steps]
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### Important Context
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[User preferences observed, coding patterns, anything that would help
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the post-compaction agent continue without asking redundant questions]
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```
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Tool call shape:
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```
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add_memory(
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messages=[{"role":"user","content":"<the summary above>"}],
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user_id="<the active user_id from the SessionStart bootstrap>",
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app_id="<the active project_id / app_id from SessionStart>",
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metadata={"type":"session_state","source":"pre-compaction","branch":"<active branch>"},
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infer=False,
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)
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```
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### Step 2: Store any unstored learnings
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If there are learnings from this session that you haven't stored yet, store them as separate memories with `infer=False` (same reasoning -- you've already extracted the fact, don't re-extract):
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- Failed approaches -> metadata `{"type": "anti_pattern"}`
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- Successful strategies -> metadata `{"type": "task_learning"}`
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- Architecture decisions -> metadata `{"type": "decision"}`
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### Step 3: Acknowledge
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After storing, briefly tell the user that session state has been saved and you're ready for compaction.
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Do this NOW. Do not skip any section. The quality of this summary directly determines whether you can continue the user's task after compaction.
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EOF
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exit 0
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