MongoDB

MongoDB released an integration with Google ADK that gives agent state durable memory. Previously developers still had to build their own tooling for a data store that could retain agent context over time. The integration uses MongoDbSessionService to persist sessions, events, and application-, user-, and session-scoped state to MongoDB, and MongoDBMemoryService to ingest session events into collections and recall memories through keyword matching; retrieval reuses MongoDB vector search combined with full-text search. It supports MongoDB Atlas and self-managed MongoDB 8.0+ deployments.

MongoDB released an integration with Google ADK that gives agent state durable memory. Previously developers still had to build their own tooling for a data store that could retain agent context over time. The integration uses MongoDbSessionService to persist sessions, events, and application-, user-, and session-scoped state to MongoDB, and MongoDBMemoryService to ingest session events into collections and recall memories through keyword matching; retrieval reuses MongoDB vector search combined with full-text search. It supports MongoDB Atlas and self-managed MongoDB 8.0+ deployments.

MongoDB has released Automated Embedding in MongoDB Vector Search as a public preview in Community Edition: a developer chooses an embedding model while creating a vector search index, and text vectors are generated and kept in sync during initial data sync, document inserts and updates, and query processing. Previously developers called external embedding providers from application code before inserting data or running a vector query, and built custom pipelines to keep source data and vectors aligned on every insert, update or delete, plus an extra API call per query. The feature ships Voyage AI's voyage-4-large, voyage-4, voyage-4-lite and voyage-code-3 models and uses asynchronous dynamic batching that adjusts batch size to document shape.

MongoDB has released Automated Embedding in MongoDB Vector Search as a public preview in Community Edition: a developer chooses an embedding model while creating a vector search index, and text vectors are generated and kept in sync during initial data sync, document inserts and updates, and query processing. Previously developers called external embedding providers from application code before inserting data or running a vector query, and built custom pipelines to keep source data and vectors aligned on every insert, update or delete, plus an extra API call per query. The feature ships Voyage AI's voyage-4-large, voyage-4, voyage-4-lite and voyage-code-3 models and uses asynchronous dynamic batching that adjusts batch size to document shape.

MongoDB has released a federated Copilot connector for MongoDB Atlas, now generally available, letting users ask Microsoft 365 Copilot questions about live Atlas data. Previously such answers lived in operational applications built on Atlas while the questions were asked in everyday productivity tools, or relied on a static copy of the data. The connector queries Atlas on demand through MongoDB's managed remote MCP server, with no migration or indexed copy, and each query runs as the signed-in user under existing Atlas permissions.

MongoDB has released a federated Copilot connector for MongoDB Atlas, now generally available, letting users ask Microsoft 365 Copilot questions about live Atlas data. Previously such answers lived in operational applications built on Atlas while the questions were asked in everyday productivity tools, or relied on a static copy of the data. The connector queries Atlas on demand through MongoDB's managed remote MCP server, with no migration or indexed copy, and each query runs as the signed-in user under existing Atlas permissions.

MongoDB Research has released LEAF, a knowledge distillation framework, together with two 23M-parameter text embedding models, mdbr-leaf-ir and mdbr-leaf-mt. State-of-the-art embedding models have grown to hundreds of millions or billions of parameters, typically need GPU servers, and are not compatible with one another, so swapping models means recomputing every document embedding. LEAF aligns only the teacher and student output embeddings; the distilled models retain up to 97% of their teacher's performance while being 5–15× smaller and 7–24× faster, and can run offline on CPU-only servers, IoT and mobile devices.

MongoDB Research has released LEAF, a knowledge distillation framework, together with two 23M-parameter text embedding models, mdbr-leaf-ir and mdbr-leaf-mt. State-of-the-art embedding models have grown to hundreds of millions or billions of parameters, typically need GPU servers, and are not compatible with one another, so swapping models means recomputing every document embedding. LEAF aligns only the teacher and student output embeddings; the distilled models retain up to 97% of their teacher's performance while being 5–15× smaller and 7–24× faster, and can run offline on CPU-only servers, IoT and mobile devices.

MongoDB released Atlas App Connections, which lets AI coding tools connect to an Atlas account with a single click of authorization. Until now the only route was sharing long-lived service account credentials, which slowed builders and left administrators managing a security risk they were reluctant to accept, so some organizations avoided third-party integrations. Built on OAuth 2.1, it lets AI clients act on the user's behalf through the Atlas MCP Server, scoped to the user's existing permissions, revocable at any time, and off by default for existing organizations until an administrator opts in.

MongoDB released Atlas App Connections, which lets AI coding tools connect to an Atlas account with a single click of authorization. Until now the only route was sharing long-lived service account credentials, which slowed builders and left administrators managing a security risk they were reluctant to accept, so some organizations avoided third-party integrations. Built on OAuth 2.1, it lets AI clients act on the user's behalf through the Atlas MCP Server, scoped to the user's existing permissions, revocable at any time, and off by default for existing organizations until an administrator opts in.