Mgeeeek
0fd7cb9c5a
Two changes to the compaction flow: PreCompact: agent stores its summary with infer=False on_pre_compact.sh now instructs the agent to call add_memory with infer=False. The agent has full conversation context and writes a structured summary; without infer=False the platform runs a second LLM extraction pass over that summary, losing structure and producing fragmented facts. With it, the structured text is preserved verbatim. Same guidance added to mem0-mcp/SKILL.md so the agent applies it to other already-extracted writes (decisions, anti-patterns, conventions). SessionStart-compact: capture the platform-generated summary PreCompact fires BEFORE Claude Code generates its compact summary, so the summary is unreachable from that hook. SessionStart-compact fires AFTER, so a new background helper (capture_compact_summary.py) reads the transcript at that point, finds the isCompactSummary=true entry, and stores its content as a separate memory tagged metadata.type=compact_summary. The post-compaction bootstrap text now tells the agent to layer its recall queries across session_state (its own pre-compact summary), compact_summary (the platform's condensed view), and topical types like decision / anti_pattern. Net effect: three complementary memory types from one session boundary, none of which used to be filterable or even captured correctly.
78 lines
2.7 KiB
Bash
Executable File
78 lines
2.7 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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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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metadata={"type":"session_state","source":"pre-compaction"},
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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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