Configuration

LivingMemory defaults work for most users. The settings you usually need to touch are model providers, recall size, memory isolation, graph retrieval, backup, and cleanup.

Recommended profiles

Scenario	Recommendation
First-time setup	Configure only provider_settings.llm_provider_id and provider_settings.embedding_provider_id; keep the rest at defaults
Private long-term assistant	Keep persona and session filtering enabled; keep summary_trigger_rounds around 8-12
Group companion	Enable session_manager.enable_full_group_capture and consider a larger context_window_size
Low-resource server	Reduce index_rebuild_settings.embedding_batch_size, keep tasks_limit = 1, and increase request delays
Higher recall quality	Keep graph memory and atomization enabled; set recall_engine.top_k to 5-8
Cost-sensitive setup	Lower top_k, disable recent-context expansion, and increase summary trigger rounds

Model providers

Key	Default	Description
provider_settings.embedding_provider_id	empty	Generates memory vectors. Empty means AstrBot's default embedding provider
provider_settings.llm_provider_id	empty	Summarizes conversations and evaluates memory importance. Empty means AstrBot's default LLM

Try to keep the embedding model stable. If you change it and old memories recall poorly, run /lmem rebuild-index.

Session management

Key	Default	Description
session_manager.enable_full_group_capture	true	Captures group messages that do not directly mention the bot
session_manager.context_window_size	50	Historical message window used for summarization and context analysis
session_manager.max_messages_per_session	1000	Maximum stored messages for one session
session_manager.cleanup_batch_size	50	Number of old summarized messages cleaned per batch

For very busy group chats, lower context_window_size or disable full group capture.

Recall and injection

Key	Default	Description
recall_engine.top_k	5	Number of memories automatically recalled each turn
recall_engine.max_k	10	Maximum results returned by active agent recall
recall_engine.importance_weight	1.0	Importance weight in final ranking
recall_engine.fallback_to_vector	true	Falls back to vector search if hybrid retrieval fails
recall_engine.injection_method	extra_user_content	Where or how recalled memories are injected
recall_engine.inject_with_recent_context	false	Expands the query with recent conversation
recall_engine.search_cache_enabled	true	Enables short-term retrieval caching

extra_user_content is the safest default. Gemini providers automatically fall back from fake_tool_call to extra_user_content. DeepSeek V4 thinking mode can now use normal fake_tool_call on recent AstrBot versions; the legacy fake_tool_call_deepseek_v4 option is kept only as a compatibility alias and automatically falls back to fake_tool_call.

Memory isolation

Key	Default	Description
filtering_settings.use_persona_filtering	true	Only recall memories for the current persona
filtering_settings.use_session_filtering	true	Only recall memories for the current session

Disable session filtering only if you intentionally want different chats to share one long-term memory pool. Keep persona filtering enabled when the bot has distinct personas.

Reflection and lifecycle

Key	Default	Description
reflection_engine.summary_trigger_rounds	10	Number of conversation rounds before summarization
importance_decay.decay_rate	0.01	Daily importance decay
importance_decay.access_decay_window_days	30.0	Time window for access reinforcement
importance_decay.access_decay_max_count	10	Maximum access reinforcement count

Lower summary_trigger_rounds if you want the bot to remember faster. Raise it if you want fewer LLM calls.

Agent tools

Key	Default	Description
agent_tools.enable_recall_tool	true	Registers recall_long_term_memory for active recall
agent_tools.enable_memorize_tool	false	Registers memorize_long_term_memory for active writes

The write tool is powerful and depends on model discipline. Start with active recall, then enable active writes after observing stable behavior.

Graph memory and atomization

Key	Default	Description
graph_memory.enabled	true	Enables graph-route retrieval
graph_memory.document_route_weight	0.65	Document-route weight
graph_memory.graph_route_weight	0.35	Graph-route weight
graph_memory.cross_route_bonus	0.08	Bonus when both routes hit the same memory
graph_memory.expansion_hops	1	Graph neighbor expansion hops
graph_memory.dynamic_route_weighting	true	Adjusts route weights based on query intent
graph_memory.atom_enabled	true	Enables memory atomization

For relationship-heavy use, increase graph-route weight or set expansion_hops to 2. If the database is large, second-hop expansion adds query cost, so use the WebUI recall debugger to inspect results first.

Backup, migration, and cleanup

Key	Default	Description
migration_settings.auto_migrate	true	Migrates old databases at startup
migration_settings.create_backup	true	Creates a backup before migration
backup_settings.enabled	true	Daily database backup
backup_settings.keep_days	7	Backup retention days
forgetting_agent.auto_cleanup_enabled	true	Daily cleanup for old low-importance memories
forgetting_agent.cleanup_days_threshold	30	Age threshold for cleanup candidates
forgetting_agent.cleanup_importance_threshold	0.3	Importance threshold for cleanup candidates

For production use, keep backups and migration backups enabled. To make cleanup more conservative, raise the day threshold or lower the importance threshold.

Index rebuild tuning

Key	Default	Description
index_rebuild_settings.batch_size	50	Memories read per batch
index_rebuild_settings.embedding_batch_size	8	Texts per embedding request
index_rebuild_settings.tasks_limit	1	Embedding concurrency limit
index_rebuild_settings.max_retries	5	Retry count for a failed batch
index_rebuild_settings.request_delay	5.0	Delay between embedding requests
index_rebuild_settings.max_failure_ratio	0.02	Allowed failure ratio

If you hit API rate limits, increase request_delay first, then lower embedding_batch_size. Avoid raising concurrency blindly; index rebuilds are more about finishing reliably than finishing aggressively.