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Mastra 1.75.0: Span Query API, Kosten-Analytics und Self-Embedding-Vektorspeicher
Mastra 1.75.0 bringt eine Span Query API zum Abfragen abgeschlossener Spans über Traces hinweg, token- und kostenbasierte Trace-Aggregation in den Observability-Stores ClickHouse, DuckDB und Postgres, Semantic Recall mit selbst einbettenden Vektorspeichern wie MongoDBVector ohne Client-Embedder sowie neue Funktionen in @mastra/connect 1.0.
Highlights
Span Query API (list completed spans across traces)
New core/server/client support lets you query individual completed spans (with filters, cursors, previews, and model cost) via storage.querySpans() / client.querySpans() / POST /api/observability/spans/query, enabling workflows like “show every failed tool_call in the last hour” without first finding traces.
Trace Aggregation with token + cost analytics (now across stores)
aggregateTraces() now supports token and cost measures (tokens.* sums/avgs and cost.sum/cost.avg with coverage + currency handling), and is implemented in the ClickHouse, DuckDB, and Postgres observability stores—making cost/tokens-first dashboards and “top spenders” queries a first-class capability.
Self-embedding vector stores for Semantic Recall (no client embedder required)
Semantic recall can now run against vector stores that generate embeddings themselves (MastraVector.isSelfEmbedding), including MongoDBVector via autoEmbed, so memory recall and message writes work without configuring a client-side embedder.