Memory DAG Compactor

What it does

Standard memory compaction is lossy — older entries get truncated and details disappear forever. Memory DAG Compactor replaces flat compaction with a directed acyclic graph (DAG) of hierarchical summaries inspired by lossless-claw's Lossless Context Management approach.

Each depth in the DAG uses a purpose-built prompt tuned for that abstraction level:

Depth	Name	What it preserves	Timeline granularity
d0	Leaf	File operations, timestamps, specific actions, errors	Hours
d1	Condensed	What changed vs. previous context, decisions made	Sessions
d2	Arc	Goal → outcome → carries forward	Days
d3+	Durable	Long-term context that survives weeks of inactivity	Date ranges

The raw MEMORY.md entries are never deleted — only organized into a searchable, multi-level summary hierarchy.

When to invoke

Automatically nightly at 11pm (cron) — compacts the day's memory entries
When MEMORY.md grows beyond a configurable threshold (default: 200 entries)
Before a long-running task — ensures memory is compact and searchable
When the agent reports "I don't remember" for something that should be in memory

How to use

python3 compact.py --compact                      # Run leaf + condensation passes
python3 compact.py --compact --depth 0            # Only leaf summaries (d0)
python3 compact.py --compact --depth 2            # Condense up to d2 arcs
python3 compact.py --status                       # Show DAG stats and health
python3 compact.py --tree                         # Print the summary DAG as a tree
python3 compact.py --search "deployment issue"    # Search across all depths
python3 compact.py --inspect <summary-id>         # Show a summary with its children
python3 compact.py --dissolve <summary-id>        # Reverse a condensation
python3 compact.py --format json                  # Machine-readable output

Procedure

Step 1 — Run compaction

python3 compact.py --compact

The compactor:

Reads all entries from MEMORY.md
Groups entries into chunks (default: 20 entries per leaf)
Generates d0 leaf summaries preserving operational detail
When leaf count exceeds fanout (default: 5), condenses into d1 summaries
Repeats condensation at each depth until DAG is within budget
Writes the summary DAG to state

Step 2 — Search memory across depths

python3 compact.py --search "API migration"

Searches raw entries and all summary depths. Results ranked by relevance and depth — deeper summaries (d0) are more detailed, shallower (d3+) give the big picture.

Step 3 — Inspect and repair

python3 compact.py --tree             # Visualize the full DAG
python3 compact.py --inspect s-003    # Show summary with lineage
python3 compact.py --dissolve s-007   # Reverse a bad condensation

Depth-aware prompt design

d0 (Leaf) — Operational detail

Preserves: timestamps, file paths, commands run, error messages, specific values. Drops: conversational filler, repeated attempts, verbose tool output.

d1 (Condensed) — Session context

Preserves: what changed vs. previous state, decisions made and why, blockers encountered. Drops: per-file details, exact timestamps, intermediate steps.

d2 (Arc) — Goal-to-outcome arcs

Preserves: goal definition, final outcome, what carries forward, open questions. Drops: session-level detail, individual decisions, specific tools used.

d3+ (Durable) — Long-term context

Preserves: project identity, architectural decisions, user preferences, recurring patterns. Drops: anything that wouldn't matter after 2 weeks of inactivity.

Configuration

Parameter	Default	Description
`chunk_size`	20	Entries per leaf summary
`fanout`	5	Max children before condensation triggers
`max_depth`	4	Maximum DAG depth
`token_budget`	8000	Target token count for assembled context

State

DAG structure, summary content, and lineage stored in ~/.openclaw/skill-state/memory-dag-compactor/state.yaml.

Fields: last_compact_at, dag_nodes, dag_edges, entry_count, compact_history.

Notes

Never modifies or deletes MEMORY.md — the DAG is an overlay
Each summary includes a [Expand for details about: ...] footer listing what was compressed
Dissolve reverses a condensation, restoring child summaries to the active set
Inspired by lossless-claw's DAG-based summarization hierarchy and depth-aware prompt system