Memory Manager

Memory Manager

Mission

Build compounding capability by turning execution traces into reusable, evidence-linked memory, with retrieval centered on prior experience rather than only current working state.

Load References

Load these files before writing or promoting records:

1. references/memory-layout.md
2. references/memory-templates.md
3. references/sqlite-schema.sql

Memory Types

Manage these layers:

1. working

• run-scoped continuity state
• resume after compaction, interruption, or long waits

1. episode

• concrete run case records
• useful for similar errors, repeated attempts, and local history

1. procedure

• highest-priority execution memory
• default retrieval layer before acting

1. insight

• cross-task abstraction, tradeoffs, boundaries, and contradiction handling

1. persona

• behavior config only

Working Memory Contract

working must include:

1. goal
2. stage
3. hypothesis
4. last_action
5. last_observation
6. next_step
7. blockers
8. evidence_refs
9. active_action_ids
10. todo_active
11. todo_done
12. todo_blocked

Todo granularity should be task-level (small stages/subtasks), not command-level.

Experience-First Retrieval Policy

Prior experience retrieval is the default. working is important for continuity, but it is not the only retrieval path and should not crowd out reusable experience.

Mandatory retrieval triggers:

1. every new user turn
2. every execution batch before acting
3. every replan
4. every significant failure or new error signature
5. every high-resource or irreversible action
6. every long-action resume or post-poll decision
7. before final answer or report handoff
8. when modifying memory-manager or another Memory-related skill/instruction
9. when compaction markers such as Compact, 压缩, or Summary appear

Default retrieval order:

1. procedure

• mandatory before every execution batch

1. episode

• mandatory when a similar failure, repeated attempt, or same task type is present

1. insight

• mandatory during planning, tradeoff analysis, contradiction handling, or final answer shaping

1. working

• mandatory for resume, compaction recovery, long-action reconciliation, and final handoff

Query strategy:

1. query by project, task_type, error_signature, and stage first
2. add tags and FTS when exact filters miss
3. prefer active procedures/insights when confidence is similar
4. prefer recent local episodes over shared memory unless local retrieval is clearly low-yield
5. if retrieval is low-yield, keep going, but record memory_skip_reason or memory_low_yield_reason

Shared Retrieval Policy

Treat shared memory as an optional read-only source, not as project-local memory.

1. Query project-local memory first.
2. If local retrieval is low-yield, query the user-configured shared repo from project-context.
3. Resolve the local shared repo path from memory.shared_repo.path; if missing, ask the user where the repo should live and persist it through project-context.
4. Use read-only retrieval against the local shared repo checkout; do not mirror shared records into .project_local by default.
5. Avoid syncing the shared repo on every run or stage.
6. Sync only when:

• the shared repo checkout is missing and the user approved clone/bootstrap
• a retrieval gap remains and the local shared repo is suspected stale
• immediately before exporting shared memory

1. Treat hits as external/shared evidence until they are validated in the current project.
2. Do not rewrite shared records into local episode/procedure/insight as if they were observed locally unless the current run reproduced them.

Writeback Policy

Write conservatively, but more frequently than before:

1. write a concise working delta after every execution batch
2. write a concise working delta after every long-action poll cycle that changes status or next step
3. write episode at milestones, major failure, replan, or human intervention
4. create procedure draft after repeated successful pattern or validated recovery workflow
5. create insight draft after cross-task recurring evidence
6. store evidence pointers, not narrative only
7. when a completed long-running action produces results that affect later decisions, record the result summary before leaving watch mode

Error-Resolution Memory

For significant errors, capture:

1. error_signature
2. reproduction condition
3. attempted fixes
4. observed outcomes
5. final fix (if any)
6. unresolved hypotheses
7. retrieved procedures/episodes that influenced the fix

Working Freshness Rules

Treat stale continuity state as risk:

1. refresh after plan changes, tool-call batches, or diagnosis updates
2. refresh after long-action polls that change status
3. review at least every 15 minutes in active execution
4. force review before high-resource actions
5. force review after interruptions or unexpected failures

Invocation Schedule (Experience-First, Frequent but Targeted)

1. Mandatory once-per-run operations:

• bootstrap retrieve/init-working after intake and before planning/execution
• close-out writeback before final task completion

1. Mandatory per-turn operations:

• retrieve relevant experience on every new user turn

1. Mandatory per-batch operations:

• retrieve procedure before every execution batch
• write working delta after every execution batch

1. Mandatory trigger-based operations:

• retrieve episode on problem, failure, repeated attempt, or new error signature
• retrieve insight on planning/replanning/tradeoff/final answer
• retrieve procedure plus episode before high-resource actions
• reread working during resume, compaction recovery, long-action reconciliation, and final handoff
• retrieve procedure plus episode immediately after stalled or failed poll outcomes
• retrieve insight after completed poll outcomes when interpretation or next-step selection is needed

1. Cooldown:

• skip only duplicate retrievals when objective, stage, and error signature are unchanged and the same hit set is still fresh
• cooldown does not suppress a new-trigger retrieval

1. When skipped, log memory_skip_reason for auditability.

Post-Compression Recovery (Required)

When memory is auto-compressed/summarized:

1. immediately run a working reread before the next execution step
2. rebuild working fields from recent evidence:

• latest stage report
• latest action/observation logs
• latest todo diff (todo_active/todo_done/todo_blocked)
• active long-action records

1. publish a compact post-compression state snapshot and continue only after snapshot is consistent

Layered Retrieval Timing

Use layer-specific timing to keep retrieval frequent but useful:

1. procedure retrieve:

• before every execution batch
• before high-resource or irreversible actions
• after stalled or failed background jobs

1. episode retrieve:

• at run start for same project/task_type
• at replan or major failure
• when repeated failure indicates recent local history may help

1. insight retrieve:

• during planning/replanning for hypothesis shaping
• when evidence conflicts or root cause is unclear
• before final report/answer to run boundary checks

1. working retrieve:

• bootstrap
• resume/reconcile
• after memory compression
• before final handoff

1. persona retrieve:

• once at run start
• on interaction mode switch or explicit user preference change
• before final user-facing delivery

Recovery on Context Drift

If execution becomes repetitive or confused:

1. rebuild working state from action and observation logs
2. run targeted retrieval by project/task/error signature
3. if drift followed compaction or summary-style recovery, read prior Memory before publishing or trusting a compact state summary
4. publish compact state summary before continuing

Compaction Recovery Policy

When context may have been compressed:

1. inspect available status/state/context files for markers such as Compact, 压缩, Summary, or equivalent summary/compression techniques
2. if any marker is present, call memory-manager to read prior Memory before editing instructions, planning next actions, or resuming execution
3. if prior Memory cannot be read, treat that as an active blocker because key context may be missing
4. record the compaction trigger and retrieval result in working state or the next stage report

Promotion Policy

Promote only with evidence:

1. procedure draft -> active after successful reuse and stable boundaries
2. insight draft -> active after multi-episode support
3. require human review for safety-critical or expensive procedures
4. deprecate entries when contradictions accumulate

Shared Export Policy

Treat shared export as post-task work:

1. do not export during main task execution
2. export only verified/high-value records
3. never export noisy working state
4. require human-checkpoint before publishing
5. sync the shared repo before export so dedupe/conflict checks run against the latest branch tip

Shared Repository Contract

When exporting:

1. target https://github.com/TenureAI/open-research-memory
2. use pull-based flow: local export -> codex/* branch -> PR -> review -> merge
3. never push directly to main
4. enforce schema and required sections

Shared Retrieval Helper

Use the helper script for lightweight read-only search of a local shared repo checkout:

python3 .agents/skills/memory-manager/scripts/shared_memory_retrieval.py \
  --repo-root /path/to/open-research-memory \
  --query "cuda out of memory" \
  --type procedure \
  --task-type debug \
  --limit 5

Required Operation Output

For each memory operation, emit:

1. Run
2. Action (retrieve|write|promote|deprecate|export)
3. Target
4. Layers
5. Rationale
6. Query
7. Hits
8. Working Update
9. memory_skip_reason when applicable

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: TenureAI
• Source: TenureAI/PhD-Zero
• License: MIT

Install and usage instructions live in the source repository linked above.