mem0

New Features:

• Embeddings: Add an AWS Bedrock embedding provider (#6185)

Bug Fixes:

• Packaging: Finish the lazy-loading work started in v3.1.0. The remaining LLMs (Anthropic, Google, Groq, LangChain, Mistral, Ollama), embedders (Google, LangChain, Ollama, Vertex AI), vector stores (Azure AI Search, Azure MySQL, Baidu, LangChain, Qdrant, Redis, Supabase, Valkey, Vectorize), and the Supabase history store still imported their SDKs at module load, so importing mem0ai/oss required every provider package to be installed (#6389)
• Vector Stores: Convert Baidu Mochow's raw L2 distance into a similarity score in search() (1 / (1 + distance)), so closer matches rank higher instead of lower. A row the backend returns without a score is now left undefined instead of being treated as the closest match (#6485)
• Memory (OSS): Coerce non-string entity IDs (e.g. a numeric user_id) to strings instead of crashing on .trim() (#6263)
• Memory (OSS): Stop update() metadata from overwriting or injecting user_id, agent_id, run_id, or actor_id (in either snake_case or camelCase). These identity fields are immutable after creation, so passing them in metadata can no longer move a memory into a different tenant's scope (#6343)
• Vector Stores: Scope Pinecone deleteCol()/reset() to the configured namespace instead of deleting the whole index, so resetting a namespaced Pinecone store no longer wipes out the other namespaces sharing that index (#6287)

Changes:

• Client: Remove the unused retrievalCriteria field from PromptUpdatePayload. It was accepted and forwarded but never affected retrieval, so removing it is not a behavior change (#6313)