remembering

name	remembering
description	Advanced memory operations reference. Basic patterns (profile loading, simple recall/remember) are in project instructions. Consult this skill for background writes, memory versioning, complex queries, edge cases, session scoping, retention management, type-safe results, proactive memory hints, GitHub access detection, autonomous curation, episodic scoring, and decision traces.
metadata	{"version":"5.2.0"}

Remembering - Advanced Operations

Basic patterns are in project instructions. This skill covers advanced features and edge cases.

For development context, see references/CLAUDE.md.

Two-Table Architecture

Table	Purpose	Growth
config	Stable operational state (profile + ops + journal)	Small, mostly static
memories	Timestamped observations	Unbounded

Config loads fast at startup. Memories are queried as needed.

Boot Sequence

Load context at conversation start to maintain continuity across sessions.

from scripts import boot
print(boot())

Performance: ~150ms (single HTTP request). All queries go directly to Turso.

Boot includes a # CAPABILITIES section reporting GitHub access and installed utilities. See references/advanced-operations.md for details.

Memory Type System

Type is required on all write operations. Valid types:

Type	Use For	Defaults
decision	Explicit choices: prefers X, always/never do Y	conf=0.8
world	External facts: tasks, deadlines, project state
anomaly	Errors, bugs, unexpected behavior
experience	General observations, catch-all
procedure	Workflows, step-by-step processes, decision trees	conf=0.9, priority=1
analysis	Findings from structured analysis or research

from scripts import TYPES  # {'decision', 'world', 'anomaly', 'experience', 'procedure', 'analysis', ...}

Procedural Memories (v4.4.0)

Store reusable workflows and operational patterns as first-class memories:

from scripts import remember

# Store a workflow
id = remember(
    "Deploy workflow: 1) Run tests 2) Build artifacts 3) Push to staging 4) Smoke test 5) Promote to prod",
    "procedure",
    tags=["deployment", "workflow"],
)

# Retrieve workflows
procedures = recall(type="procedure", tags=["deployment"])

Procedural memories default to confidence=0.9 and priority=1 (important), ensuring they survive age-based pruning. Use tags to categorize by domain and workflow name for targeted retrieval.

Core Operations

Remember

from scripts import remember, remember_bg, flush

# Blocking write (default)
id = remember("User prefers dark mode", "decision", tags=["ui"], conf=0.9)

# Background write (non-blocking)
remember("Quick note", "world", sync=False)

# Ensure all background writes complete before conversation ends
flush()

When to use sync=False: Storing derived insights during active work, when latency matters. When to use sync=True (default): User explicitly requests storage, critical memories, handoffs.

Recall

from scripts import recall

# FTS5 search with BM25 ranking + Porter stemmer
memories = recall("dark mode")

# Filtered queries
decisions = recall(type="decision", conf=0.85, n=20)
tasks = recall("API", tags=["task"], n=15)
urgent = recall(tags=["task", "urgent"], tag_mode="all", n=10)

# Comprehensive retrieval (v4.1.0)
all_memories = recall(fetch_all=True, n=1000)  # Get all memories without search filtering

# Time-windowed queries (v4.3.0) - since/until with inclusive bounds
recent = recall("API", since="2025-02-01")
jan_memories = recall(since="2025-01-01", until="2025-01-31T23:59:59Z")

# Multi-tag convenience (v4.3.0)
both = recall(tags_all=["correction", "bsky"])    # AND: must have all tags
either = recall(tags_any=["therapy", "self-improvement"])  # OR: any tag matches

# Wildcard patterns are NOT supported - use fetch_all instead
# recall("*", n=1000)  # ❌ Raises ValueError
# recall(fetch_all=True, n=1000)  # ✅ Correct approach

Results return as MemoryResult objects with attribute and dict access. Common aliases (m.content -> m.summary, m.conf -> m.confidence) resolve transparently.

Decision Alternatives (v4.2.0)

Track rejected alternatives on decision memories to prevent revisiting settled conclusions:

from scripts import remember, get_alternatives

# Store decision with alternatives considered
id = remember(
    "Chose PostgreSQL for the database",
    "decision",
    tags=["architecture", "database"],
    alternatives=[
        {"option": "MongoDB", "rejected": "Schema-less adds complexity for our relational data"},
        {"option": "SQLite", "rejected": "Doesn't support concurrent writes at our scale"},
    ]
)

# Later: retrieve what was considered
alts = get_alternatives(id)
for alt in alts:
    print(f"Rejected {alt['option']}: {alt.get('rejected', 'no reason')}")

Alternatives are stored in the refs field as a typed object alongside memory ID references. The alternatives computed field is automatically extracted on MemoryResult objects for decision memories.

Reference Chain Traversal (v4.3.0)

Follow reference chains to build context graphs around a memory:

from scripts import get_chain

# Follow refs up to 3 levels deep (default)
chain = get_chain("memory-uuid", depth=3)
for m in chain:
    print(f"[depth={m['_chain_depth']}] {m['summary'][:80]}")

# Useful for understanding supersede chains, consolidated memory origins, etc.

Handles cycles via visited set. Max depth capped at 10.

Batch Operations (v4.5.0)

Execute multiple memory operations in a single HTTP round-trip, reducing tool call overhead:

from scripts import recall_batch, remember_batch

# Multiple searches in one call (uses server-side FTS5 with BM25 ranking)
results = recall_batch(["architecture", "turso", "FTS5"], n=5)
for i, result_set in enumerate(results):
    print(f"Query {i}: {len(result_set)} results")

# Multiple stores in one call
ids = remember_batch([
    {"what": "User prefers dark mode", "type": "decision", "tags": ["ui"]},
    {"what": "Project uses React", "type": "world", "tags": ["tech"]},
    {"what": "Found auth bug", "type": "anomaly", "conf": 0.7},
])

recall_batch() uses server-side FTS5 with composite scoring (BM25 × recency × priority). Falls back to sequential recall() calls if server FTS5 is unavailable. remember_batch() validates each item independently — per-item errors return {"error": str} without blocking other items.

Forget and Supersede

from scripts import forget, supersede

# Soft delete — supports both full UUIDs and partial ID prefixes (v5.1.0, #244)
forget("memory-uuid")          # Full UUID
forget("d5022772")             # Partial prefix (must be unique)

# Version without losing history (also supports partial IDs)
supersede(original_id, "User now prefers Python 3.12", "decision", conf=0.9)

Partial IDs resolve to the unique memory matching the prefix. If the prefix matches zero or multiple memories, a ValueError is raised. All ID-based functions (forget, supersede, reprioritize, strengthen, weaken) support partial IDs.

Decision Traces (v5.1.0)

Store structured decision traces with standardized format for architectural documentation:

from scripts import decision_trace

id = decision_trace(
    choice="Chose Turso FTS5 over local SQLite cache",
    context="Need to simplify architecture after cache sync bugs",
    rationale="Cache latency savings (~145ms) negligible vs tool overhead (~3-4s). "
              "Eliminating sync eliminates a whole class of bugs.",
    alternatives=[
        {"option": "Keep local cache", "rejected": "Ongoing sync bugs"},
        {"option": "Redis", "rejected": "External dependency, overkill"}
    ],
    tradeoffs="Slightly higher latency per query, network dependency",
    contraindications="If tool call overhead drops below 500ms, reconsider caching",
    tags=["architecture", "turso"]
)

Decision traces are automatically tagged "decision-trace" and stored at priority=1. The structured body captures context, rationale, alternatives, tradeoffs, and contraindications — answering why not just what.

Autonomous Curation (v5.1.0)

Analyze memory health and identify consolidation opportunities, stale memories, and duplicates:

from scripts import curate

# Preview curation recommendations (dry run)
report = curate(dry_run=True)
for rec in report["recommendations"]:
    print(rec)

# Auto-apply: consolidate clusters and demote stale memories
report = curate(dry_run=False, stale_days=90, consolidation_threshold=3)
print(f"Actions taken: {report['actions_taken']}")

Episodic Relevance Scoring (v5.1.0)

Boost frequently-accessed memories in search results using access-pattern weighting:

from scripts import recall

# Standard recall (BM25 × recency × priority)
results = recall("architecture decisions")

# Episodic recall (adds access-count boost)
results = recall("architecture decisions", episodic=True)

Episodic mode adds ln(1 + access_count) × 0.2 to the composite score, rewarding memories that have been validated as useful through repeated retrieval.

Config Table

Key-value store for profile (behavioral), ops (operational), and journal (temporal) settings.

from scripts import config_get, config_set, config_delete, config_list, profile, ops

# Read
config_get("identity")                    # Single key
profile()                                  # All profile entries
ops()                                      # All ops entries
config_list()                              # Everything

# Write
config_set("new-key", "value", "profile")  # Category: 'profile', 'ops', or 'journal'
config_set("bio", "Short bio here", "profile", char_limit=500)  # Enforce max length
config_set("core-rule", "Never modify this", "ops", read_only=True)  # Mark immutable

# Delete
config_delete("old-key")

For progressive disclosure, priority-based ordering, and dynamic topic categories, see references/advanced-operations.md.

Journal System

Temporal awareness via rolling journal entries in config.

from scripts import journal, journal_recent, journal_prune

# Record what happened this interaction
journal(
    topics=["project-x", "debugging"],
    user_stated="Will review PR tomorrow",
    my_intent="Investigating memory leak"
)

# Boot: load recent entries for context
for entry in journal_recent(10):
    print(f"[{entry['t'][:10]}] {entry.get('topics', [])}: {entry.get('my_intent', '')}")

# Maintenance: keep last 40 entries
pruned = journal_prune(keep=40)

Background Writes

Use remember(..., sync=False) for background writes. Always call flush() before conversation ends to ensure persistence.

from scripts import remember, flush

remember("Derived insight", "experience", sync=False)
remember("Another note", "world", sync=False)

# Before conversation ends:
flush()  # Blocks until all background writes finish

remember_bg() still works as deprecated alias for remember(..., sync=False).

Memory Quality Guidelines

Write complete, searchable summaries that standalone without conversation context:

• Good: "User prefers direct answers with code examples over lengthy conceptual explanations"
• Bad: "User wants code" (lacks context, unsearchable)
• Bad: "User asked question" + "gave code" + "seemed happy" (fragmented)

Edge Cases

• Empty recall results: Returns MemoryResultList([]), not an error
• Tag partial matching: tags=["task"] matches memories with tags ["task", "urgent"]
• Confidence defaults: decision type defaults to 0.8 if not specified
• Invalid type: Raises ValueError with list of valid types
• Tag mode: tag_mode="all" requires all tags present; tag_mode="any" (default) matches any
• Query expansion: When FTS5 returns fewer than expansion_threshold results (default 3), tags from partial matches find related memories. Set expansion_threshold=0 to disable.

Implementation Notes

• Backend: Turso SQLite HTTP API (all queries go directly to Turso)
• Credential auto-detection (v3.8.0): Scans env vars, then /mnt/project/turso.env, /mnt/project/muninn.env, ~/.muninn/.env
• FTS5 search: Server-side FTS5 with Porter stemmer tokenizer, BM25 × recency × priority composite scoring (tags weighted equally with summary since v5.1.0)
• Retry with exponential backoff for transient errors (503, 429, SSL)
• Thread-safe for background writes
• Repo defaults fallback: scripts/defaults/ used when Turso is unavailable

Session Continuity (v4.3.0)

Save and resume session state for cross-session persistence:

from scripts import session_save, session_resume, sessions

# Save a checkpoint before ending session
session_save("Implementing FTS5 search", context={"files": ["cache.py"], "status": "in-progress"})

# In a new session: resume from last checkpoint
checkpoint = session_resume("previous-session-id")
print(checkpoint['summary'])      # What was happening
print(checkpoint['context'])      # Custom context data
print(len(checkpoint['recent_memories']))  # Recent memories from that session

# List available session checkpoints
for s in sessions():
    print(f"{s['session_id']}: {s['summary'][:60]}")

Memory Consolidation (v4.2.0)

Automatically cluster related memories and synthesize summaries, reducing retrieval noise while preserving traceability:

from scripts import consolidate

# Preview what would be consolidated
result = consolidate(dry_run=True)
for c in result['clusters']:
    print(f"Tag '{c['tag']}': {c['count']} memories")

# Actually consolidate (creates summaries, demotes originals to background)
result = consolidate(dry_run=False, min_cluster=3)
print(f"Consolidated {result['consolidated']} clusters, demoted {result['demoted']} memories")

# Scope to specific tags
result = consolidate(tags=["debugging"], dry_run=False)

How it works:

1. Clustering: Groups memories by shared tags (minimum min_cluster memories per group)
2. Synthesis: Creates a type=world summary memory tagged consolidated containing all originals
3. Archival: Demotes original memories to priority=-1 (background)
4. Traceability: Summary's refs field lists all original memory IDs

Cross-Episodic Reflection (v4.4.0)

Phase 1.5 of the therapy workflow: systematically convert clusters of similar experiences into generalized semantic knowledge.

from scripts import therapy_reflect

# Preview discovered patterns without creating memories
result = therapy_reflect(dry_run=True)
for c in result['clusters']:
    print(f"Pattern ({len(c['source_ids'])} episodes): {c['pattern'][:80]}")
    print(f"  Common tags: {c['tags']}")

# Create semantic memories from patterns
result = therapy_reflect(dry_run=False)
print(f"Created {result['created']} pattern memories from {len(result['clusters'])} clusters")

How it works:

1. Sampling: Retrieves recent episodic memories (type=experience)
2. Similarity search: For each experience, finds similar past episodes via recall()
3. Clustering: Groups 3+ similar experiences into pattern clusters
4. Extraction: Creates type=world semantic memories tagged reflection + cross-episodic
5. Traceability: Each pattern memory's refs field lists all source episode IDs

Integrates into the existing therapy workflow between pruning and synthesis phases.

Advanced Topics

For architecture details, see _ARCH.md.

See references/advanced-operations.md for:

• Date-filtered queries (recall_since, recall_between, since/until parameters)
• Priority system and memory consolidation (strengthen, weaken)
• Therapy helpers, cross-episodic reflection, and analysis helpers
• Handoff convention (cross-environment coordination)
• Session scoping and continuity (session_save, session_resume, sessions)
• Retrieval observability and retention management
• Export/import for portability
• Type-safe results (MemoryResult) details
• Proactive memory hints (recall_hints)
• GitHub access detection and unified API
• Progressive disclosure and priority-based ordering
• Decision alternatives (get_alternatives) and memory consolidation (consolidate)
• Reference chain traversal (get_chain)
• Batch APIs (recall_batch, remember_batch) for reducing HTTP round-trips
• Autonomous curation (curate) for memory health management
• Decision traces (decision_trace) for structured architectural documentation
• Episodic relevance scoring (episodic=True in recall) for access-pattern boosting