aim-save

# Save Memory - Manual Session Summary Storage Save the current session context to the AI Memory system's `discussions` collection. This creates a `type=session` entry that can be retrieved by SessionStart on future session resume or by `/aim-search`. ## When to Use - Before ending a session to preserve important context - After making significant decisions you want remembered - When you want to bookmark a particular conversation state - As a manual complement to the automatic PreCompact session save ## Usage The save-memory command runs the manual_save_memory.py script using the project's configured AI Memory installation. ```bash # Save with a description (recommended) "$AI_MEMORY_INSTALL_DIR/.venv/bin/python" "$AI_MEMORY_INSTALL_DIR/.claude/hooks/scripts/manual_save_memory.py" \ "Completed authentication refactor, decided on JWT approach" # Save without description "$AI_MEMORY_INSTALL_DIR/.venv/bin/python" "$AI_MEMORY_INSTALL_DIR/.claude/hooks/scripts/manual_save_memory.py" ``` ## What Gets Stored The script stores to the discussions collection with: - type: session (default); agent_memory or agent_insight (when --type is used) - source_hook: ManualSave - group_id: Auto-detected from current project directory - session_id: Current Claude session ID (from CLAUDE_SESSION_ID env var) - content: Structured summary with timestamp and user description - embedding: 768-dim Jina v2 vector for semantic retrieval - content_hash: SHA-256 for deduplication ## Agent Memory Support When `--type agent_memory` or `--type agent_insight` is used, the memory is stored via `store_agent_memory()` to the Parzival namespace with `agent_id=parzival`. This requires Parzival to be enabled (`parzival_enabled=true` in config). If Parzival is not enabled, the command returns an error. ### Activation ```text # Default behavior (unchanged) /aim-save "Completed authentication refactor" # Save as agent memory (general project knowledge) /aim-save "The decay formula uses 0.7/0.3 weighting" --type agent_memory # Save as agent insight (key learning or pattern) /aim-save "PyYAML not in test deps caused CI failures" --type agent_insight ``` ## Environment Variables (Auto-Configured) These are set automatically in settings.json by the installer: - AI_MEMORY_INSTALL_DIR — Path to ~/.ai-memory installation - AI_MEMORY_PROJECT_ID — Current project name - QDRANT_HOST / QDRANT_PORT — Qdrant connection (localhost:26350) - QDRANT_API_KEY — Qdrant authentication - EMBEDDING_HOST / EMBEDDING_PORT — Embedding service (127.0.0.1:28080) ## Error Handling - If Qdrant is unavailable: queues to ~/.ai-memory/queue/ for background processing - If embedding fails: stores with zero vector (backfilled later) - Exit code 0 on success OR successful queue fallback - Exit code 1 only on hard errors (missing installation) ## Prerequisites - AI Memory services running (docker compose up -d from ~/.ai-memory/docker/) - Installation directory exists at $AI_MEMORY_INSTALL_DIR - Python venv at $AI_MEMORY_INSTALL_DIR/.venv/ ## Activation Examples ```text # Save decision context /aim-save "Decided to use Qdrant for vector storage over Pinecone due to self-hosting requirement" # Save progress checkpoint /aim-save "Completed 3 of 5 API endpoints, auth middleware working" # Quick save without description /aim-save # Save as agent memory (general project knowledge) /aim-save "The decay formula uses 0.7/0.3 weighting" --type agent_memory # Save as agent insight (key learning or pattern) /aim-save "PyYAML not in test deps caused CI failures" --type agent_insight ``` ## Technical Details - Script: .claude/hooks/scripts/manual_save_memory.py - Collection: discussions (COLLECTION_DISCUSSIONS) - Type: session (same as PreCompact auto-saves) - Embedding: jina-embeddings-v2-base-en (768 dimensions) - Fallback: File queue at ~/.ai-memory/queue/ via queue_operation() - Logging: Activity logged via log_manual_save() to ~/.ai-memory/logs/activity.log