Returns server health metadata for the active project: storage engine (sqlite-wal) and write arbitration mode (MessageQueue). Use this to verify the egc-memory server is running and responsive before calling get_state or update_state.

Persist a single decision to the SQLite store with write-lock arbitration to prevent concurrent conflicts. Provide a short context label and the decision text. Decisions stored here are queryable via query_history and surfaced in get_state.

Return a paginated list of past decisions stored in the SQLite state. Each entry includes the decision text, context label, and timestamp. Use limit and offset for pagination. Useful for auditing what was decided without loading the full project state.

Keyword search over the decision history with BM25 relevance ranking (SQLite FTS5). Each result includes the decision content, context label, timestamp, and a score normalized to [0, 1] where 1 is the best match in the result set. Use this to find past decisions by topic instead of paging through query_history.

Returns the current project memory: decisions made, preferences established, things to avoid, and what to pick up next. State is scoped to the current git branch when the project is a git repository, falling back to the default branch state and then to the legacy flat state file. Call this at the START of every session to restore context.

Updates the project memory with decisions made this session. Writes to the state file of the current git branch when the project is a git repository. Call this at the END of every session. Merges with existing state and does not erase previous memory.

Store a transient key-value entry scoped to the current project. Expires automatically after ttl_seconds (default 86400s). Overwrites any existing entry with the same key without error. Use for debug flags, temporary task context, or values that must not pollute long-term state in update_state. Do not use for data that must survive a session restart — use update_state instead.

Retrieve a single transient entry by key for the current project. Returns null if the key does not exist or has expired — does not throw. Does not modify the entry or extend its TTL. Use working_memory_list when the key is unknown or to audit all active entries.

List all live transient key-value entries for the current project, ordered by key. Expired entries are excluded automatically. Returns an empty array when no live entries exist. Each entry includes key, value, and expires_at. Use to audit active transient state or to check whether a key exists before calling working_memory_get.

Persist a new lesson learned during this session with an initial confidence score (default 0.7). Confidence decays over time when the lesson is not reinforced. Does not deduplicate — call lesson_recall first to check whether a similar lesson already exists. Use to record patterns, heuristics, or observations the AI should carry forward across sessions.

Search active lessons by keyword across content, context, and tags. Only lessons at or above min_confidence (default 0.2) are returned; lower-confidence lessons are archived and hidden. Updates the last_recalled timestamp on matched lessons (decay is driven by last_reinforced, not last_recalled). Returns results ranked by confidence score descending. Call at session start to surface relevant patterns before beginning work on a known problem area.

Reinforce an existing lesson when the same pattern is observed again. Increases confidence by 0.15, capped at 1.0. Unarchives lessons that had decayed below the threshold. Call this when a lesson recalled via lesson_recall proves relevant to the current task, or when the same mistake recurs. Returns the updated lesson with its new confidence score.

Analyze captured runtime hook events and surface recurring behaviors across sessions. Detects repeated shell commands and recurring error signatures using frequency analysis. Patterns are stored with frequency, last_seen, and suggested_automation fields. Returns an array of pattern objects with type, description, occurrence count, and an actionable automation suggestion. Call after several sessions of work to identify tasks worth automating or formalizing as a hook.

Compress recent raw hook observations into structured typed summaries (tool_failure, tool_success, file_edit, generic) using rule-based analysis. Reduces token count when injecting session history into context. Does not delete raw observations — only marks them as compressed. Call before get_state or at session start to ensure hook data is compact before loading project memory. Returns the count of compressed items and a human-readable summary of what was processed.