Remembering

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

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), populates local cache for fast subsequent recall().

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:

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

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.

Forget and Supersede

from scripts import forget, supersede

# Soft delete (sets deleted_at, excluded from queries)
forget("memory-uuid")

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

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
• Credential auto-detection (v3.8.0): Scans env vars, then /mnt/project/turso.env, /mnt/project/muninn.env, ~/.muninn/.env
• FTS5 search: Porter stemmer tokenizer with BM25 ranking
• Local SQLite cache for fast recall (< 5ms vs 150ms+ network)
• Thread-safe for background writes
• Repo defaults fallback: scripts/defaults/ used when Turso and cache are both 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)