memory-attention-router

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name: memory-attention-router description: Deterministic long-term memory routing for OpenClaw. Route, write, reflect on, and refresh reusable memory for multi-step agent work. Use when the task depends on prior sessions, durable user preferences, reusable procedures, past failures, project summaries, or stale memories that need replacement. Trigger on explicit memory phrases like "from now on", "remember this", "always", "prefer", "avoid", "my rule is", "replace my previous rule", and "going forward", and whenever an agent step needs a compact working-memory packet instead of raw history or plain RAG.

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Memory Attention Router Skill

Turn long-term memory into a small, role-aware working-memory packet.

Do not use this skill as plain document RAG. Do not dump raw memory lists into model context. Route to the right memory blocks, compose a compact packet, write back new learnings, and retire stale memory when better evidence appears.

Trigger cues

Trigger immediately when the user states a durable rule or asks to preserve or replace memory, especially with phrases like:

from now on

remember this

always

prefer

avoid

my rule is

replace my previous rule

going forward

Also trigger when a planning, execution, critique, or response step needs compact memory state rather than raw history.

Step roles

Choose the current step role before reading memory:

planner

executor

critic

responder

Current type preferences:

planner -> preference, procedure, summary

executor -> preference, procedure, episode, reflection

critic -> reflection, preference, summary

responder -> preference, summary, procedure

Important implication:

executor should preserve durable hard constraints as well as reusable procedures

Read flow

1. Build a route request with: - goal - step_role - session_id if known - task_id if known - user_constraints - recent_failures - unresolved_questions 2. Run: python3 {baseDir}/scripts/memory_router.py route --input-json '' 3. Read the packet. 4. Use the packet in downstream reasoning. 5. Inspect debug.selected_blocks and debug.selected_memories when you need to understand why a memory was selected.

The router uses a deterministic two-stage flow:

1. select the best blocks from task_scoped, session_scoped, durable_global, and recent_fallback 2. score memories only inside the selected blocks

Write flow

Store memory after important outcomes:

python3 {baseDir}/scripts/memory_router.py add --input-json ''

Write memory when:

a durable user preference or rule is learned

a reusable procedure becomes clear

a tool result will matter later

a failure pattern should influence future behavior

a stable summary is worth keeping

If a new memory replaces an older one, include replaces_memory_id. The router will retire the old memory, link it forward to the replacement, and persist a retirement reason.

Reflect flow

At the end of meaningful work or after a failure cluster, create reflection and optionally procedure memory:

python3 {baseDir}/scripts/memory_router.py reflect --input-json ''

Use reflection for:

lessons

warnings

failure patterns

reusable procedures derived from successful work

Refresh flow

When new evidence invalidates or replaces older memory:

python3 {baseDir}/scripts/memory_router.py refresh --input-json ''

Use refresh to:

deactivate stale memories

mark replacements with replacement_memory_id

persist why the memory was retired with refresh_reason

create contradiction links when a replacement exists

Packet rules

A good packet contains:

hard_constraints

relevant_facts

procedures_to_follow

pitfalls_to_avoid

open_questions

selected_memory_ids

Current compactness targets:

selected_memory_ids -> cap at 5

hard_constraints -> cap at 4

relevant_facts -> cap at 3

procedures_to_follow -> cap at 3

pitfalls_to_avoid -> cap at 3

open_questions -> cap at 5

Prefer small, high-signal packets over broad recall.

Routing rules

Prefer durable, reusable memory over noisy transient notes.

Preserve hard constraints for execution steps, not only planning steps.

Use support edges to help validated memories win borderline ranking decisions.

Treat contradicts edges directionally: penalize the stale target, not the newer memory asserting the contradiction.

Use summary instead of verbose raw history when both carry the same signal.

Retire stale memory when replacement is clear; do not allow conflicting active memories to accumulate indefinitely.

Bootstrap

Initialize the database:

python3 {baseDir}/scripts/memory_router.py init

Default DB path behavior:

if MAR_DB_PATH is set, that path is used

otherwise, when installed at /skills/memory-attention-router, the default is /.openclaw-memory-router.sqlite3

Inspect stored memories:

python3 {baseDir}/scripts/memory_router.py list --limit 20

Inspect one memory:

python3 {baseDir}/scripts/memory_router.py inspect --memory-id

File guide

See:

reference guide

memory schema

prompt templates

testing guide