The six autonomous features
What Gemma 4 unlocks.
memory_auto_tagSmart tier+
LLM looks at a memory's title + content and proposes tags. New tags merge into the existing tag set (no overwrites). Operators use this to tag bulk-imported memories without writing rules.
// MCP — memory_auto_tag
{"id": "550e8400-e29b-41d4-a716-446655440000"}
→ {"id": "…", "new_tags": ["okr", "q3", "engineering"], "all_tags": ["draft", "okr", "q3", "engineering"]}
// Gemma 4 reads title+content, returns 3-5 relevant tags. The new tags are
// merged with whatever was already there.
memory_consolidateSmart tier+
Bulk-collapses N memories (up to 100) into 1 derived summary. Source memories are linked to the consolidated output via derived_from KG relation, so provenance survives. The biological-memory analog of sleep-driven episodic-to-long-term consolidation.
// MCP — memory_consolidate
{
"ids": ["id-1", "id-2", "id-3", …], // 2-100 ids
"title": "Q3 OKR — consolidated retrospective",
"namespace": "alphaone/eng"
}
→ {"consolidated_id": "…", "summary": "",
"source_count": 12, "links_created": 12} // each source → derived_from edge
memory_expand_querySmart tier+
Takes a short user query and expands it into a richer set of related terms. Used to widen recall when the literal query doesn't match enough rows. Especially useful for vague natural-language queries against a corpus that uses precise jargon.
// MCP — memory_expand_query
{"query": "how do we deploy"}
→ {"original": "how do we deploy",
"expanded_terms": ["deploy", "deployment", "release", "ship", "rollout",
"kubernetes", "ci pipeline", "container registry"]}
// Caller can then run memory_search across the expanded set.
memory_detect_contradictionSmart tier+
Compares two memories and tells you if they contradict. Powers the v0.6.3 KG contradicts relation: when the LLM flags a contradiction, the system can auto-link the pair so future recall surfaces the conflict.
// MCP — memory_detect_contradiction
{
"id_a": "id of the older memory",
"id_b": "id of the newer memory"
}
→ {"contradicts": true, "memory_a": {"id": "…", "title": "We use Postgres"},
"memory_b": {"id": "…", "title": "We migrated to MySQL"}}
cross_encoder_rerankingAutonomous tier
Cross-encoder reranker scores top-K recall results against the query, reordering for precision. Where keyword + vector recall return a candidate set, the cross-encoder is the final pass that puts the best match first. Adds ~50ms to a recall but materially improves top-1 quality.
// Implicit — automatically applied during memory_recall when:
// 1. Autonomous tier is configured, AND
// 2. cross-encoder model loaded successfully at startup
//
// Recall pipeline becomes:
// FTS5 70% ⊕ HNSW 30% → candidate set (top-100 typical) →
// Cross-encoder rerank → final top-K (default 10)
memory_session_startAutonomous tier (LLM-driven)
Run at the start of an agent session. Recalls the most relevant memories given the session's stated context, optionally LLM-summarized into a session brief. The agent's "morning briefing" — pulls in the right context without explicit recall calls peppered through the prompt.
// MCP — memory_session_start
{
"context": "continuing the q3 OKR review thread from yesterday",
"namespace": "alphaone/eng/leadership",
"as_agent": "alphaone/eng/leadership/alice",
"summarize": true // LLM-generate a brief from the recall hits
}
→ {"recalled": [12 top memories, ranked, with budget_tokens cap respected],
"session_brief": "Yesterday's discussion focused on..." // Gemma 4 summary
}