Persist a new memory for an agent with importance weighting and optional tags. Use to save facts, decisions, or context for future retrieval. importance defaults to 0.5; set 0.8–1.0 for critical memories that must survive decay.

Retrieve top-k memories semantically closest to a query. Prefer over dakera_batch_recall for query-based retrieval. Set include_associated=true to expand results via KG edges (1-3 hops).

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.

Filter-based memory listing by tags, importance range, time window, type, or session. Prefer over dakera_recall when semantic search is not needed. At least one filter required.

Bulk-delete memories matching filter criteria: tags, importance range, time window, or memory type. At least one filter is required to prevent accidental full-agent wipe. Deletion is permanent — use dakera_memory_importance to lower importance scores instead of deleting.

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 structured information (entities, topics, key phrases, summary) from arbitrary text using the configured provider hierarchy: per-request override → namespace default → server default → GLiNER local. Supported providers: gliner (zero-config local ONNX), openai, anthropic, openrouter, ollama, none.

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.