v2.2.1

mem0ai/mem0v2.2.1Sep 25, 2026by blazingretinol

Upgrading?

AI Summary

The Python SDK release addresses critical core functionality issues, including fixing memory insertion reporting and restoring context manager protocols, alongside various vector store and LLM provider fixes.

Key Highlights

  • The `add()` method now strictly reports only successfully inserted records and raises `VectorStoreError` if none are inserted.
  • Restored context-manager protocol (`with Memory() as m:`) for proper resource cleanup.
  • Fixed AWS Bedrock Anthropic path to handle `reasoningContent` blocks emitted by Claude reasoning models.
  • Turbopuffer filters now apply all operators, and search scores respect `distance_metric`.

Full Release Notes

**Bug Fixes:**
- **Core:** `add()` (`Memory` and `AsyncMemory`) no longer reports records the vector store rejected as successful `ADD` events. Only records that were actually inserted are written to history, entity-linked, and returned. If none of the extracted memories could be inserted, `add()` now raises `VectorStoreError` instead of returning memories that were never stored ([#7066](https://github.com/mem0ai/mem0/pull/7066))
- **Core:** Restore the context-manager protocol on `Memory` (`with Memory() as m:`) and `AsyncMemory` (`async with AsyncMemory() as m:`), which closes the instance on exit ([#7354](https://github.com/mem0ai/mem0/pull/7354))
- **LLMs:** The AWS Bedrock Anthropic path now returns the first Converse content block that carries text instead of always reading `content[0]`. Claude reasoning models can emit a `reasoningContent` block before the text block, which previously made the call fail with a `KeyError` ([#6369](https://github.com/mem0ai/mem0/pull/6369))
- **Vector Stores:** Turbopuffer filters now apply every operator (`eq`, `ne`, `gt`, `gte`, `lt`, `lte`, `in`, `nin`). Only `gte` and `lte` were read before, so any other operator was silently dropped and the query returned unfiltered results. An unsupported operator now raises `ValueError` ([#6564](https://github.com/mem0ai/mem0/pull/6564))
- **Vector Stores:** Turbopuffer `search()` scores now respect `distance_metric`. With `euclidean_squared`, the unbounded squared distance maps to `1 / (1 + distance)` instead of `1 - distance`, which went negative and inverted ranking for any distance above 1 ([#6559](https://github.com/mem0ai/mem0/pull/6559))
- **Vector Stores:** S3 Vectors `search()` scores are now metric-aware. With `euclidean`, the distance maps to `1 / (1 + distance)` instead of `max(0, 1 - distance)`, which collapsed most scores to 0 ([#6547](https://github.com/mem0ai/mem0/pull/6547))

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