Persist a memory (fact, decision, context) for an agent. importance defaults to 0.5; use 0.8-1.0 for memories that must survive decay.

Semantic recall: the top_k (default 5) memories closest to a query, optionally narrowed by tags, memory_type or session_id. include_associated adds KG neighbours one hop away (dakera_recall_associated goes deeper).

Permanently delete memories by ID or tag. Provide memory_ids for exact removal or tags to bulk-delete all memories sharing those tags. Deletion is immediate and irreversible — prefer dakera_memory_importance to suppress without deleting.

List memories by filters (tags, importance, time window, type, session) without a query; at most limit (default 100). Use instead of dakera_recall when no semantic search is needed.

Bulk-delete memories matching filters (tags, importance range, time window, type, session); at least one is required. Permanent: to demote instead, use dakera_memory_importance.

Semantic search with optional tag and memory-type pre-filters. Prefer over dakera_recall when results must be constrained by tag or type alongside the semantic match.

Open a new session, returning a session_id that groups stored memories under a shared context. Attach metadata such as task type or trigger source for later retrieval.

Close an active session with an optional summary. Always call at run end (even on error) to avoid orphaned sessions; summary is retrievable via dakera_session_get.

Build a knowledge graph from a seed memory using embedding similarity. Use to explore how a concept connects to stored knowledge. For BFS traversal of an existing linked graph use dakera_graph_traverse.

BM25 keyword search over indexed documents. Use over vector search when exact-term recall matters (error codes, IDs, names). For semantic+keyword combined use dakera_hybrid_search.

BM25 + vector ANN hybrid search in a single pass. Omit vector for BM25-only mode. Use for RAG when pure semantic or keyword search alone is insufficient. vector_weight: 0.0=BM25, 1.0=vector (default 0.5).

Extract entities, topics, key phrases and a summary from text with the provider chain: extractor_override, then the namespace default, then the server default (GLiNER local). For ad-hoc GLiNER types use dakera_auto_tag. Needs a write key for the namespace (for all namespaces when neither namespace nor agent_id is given).

Search the Dakera tool catalog by keyword or tier (core/power/admin/meta) and return names and one-line summaries without loading full schemas. Call this first to find relevant tools, then use dakera_load_tools to fetch only the schemas you need — avoids loading the full catalog upfront.

Fetch the full inputSchema for one or more named tools. Use after dakera_discover_tools. Returns schemas for found tools and a not_found list for unrecognized names.