Google ADK 2.13 promotes 12 features to stable and adds MongoDB sessions and memory

What Google released

Google published ADK for Python 2.13.0 on GitHub on 23 October 2026; the changelog entry is dated 22 October. The Agent Development Kit is Google's open-source, code-first framework for building, evaluating and deploying agents. It is optimized for Gemini but works with other models.

The release promotes 12 mature experimental features to stable status, listed in the project's feature registry guide. It adds a MongoDbSessionService that stores sessions in MongoDB and shares them across runner replicas, and a MongoDbMemoryService that gives agents long-term memory by keyword match, with no embedding model or vector index required. Both services are marked experimental. The MongoDB toolset can now be deployed to Agent Engine without packaging the connection string, and MongoDB Atlas can embed search queries on the server side.

Routing, voice and speed

A new ClassifierNode routes workflow steps with a classifier instead of an LLM call. The first integration, TypesafeClassifier, uses TypeSafe's Jev models, which the release notes say return calibrated probabilities in a single forward pass, much faster than an LLM. An LlmClassifier is also included.

For voice agents, the CascadeLive connection gains barge-in, so a user can interrupt the agent, plus Gemini speech-to-text and text-to-speech. Google says execution turns are up to 8x faster for agents that name their model with a string, and event loading has less overhead. Evaluation now splits each invocation's duration into model, tool and framework time. Other additions include a delete_memory API on the base memory service and an OpenSandbox code environment.

What breaks

Telemetry now defaults to the OpenTelemetry GenAI semantic conventions v2 (otel_semconv_1_44), and the call_llm span is deprecated. Teams can keep the old v1 schema by setting ADK_TELEMETRY_SCHEMA_VERSION_OPT_IN=otel_semconv_1_36. Dashboards or alerts that query call_llm spans should be checked before upgrading.

The previous release, 2.12.0 on 13 October, cut time to the first model request by 40% in long sessions, added after_tool_callback for screening tool output, let workflow nodes require human confirmation, and added Python 3.15 support.

Why it matters

Two releases in ten days show ADK 2 moving from feature work to hardening. Promoting a dozen features to stable and adding a shared session store are the kind of changes teams need before running agents on several replicas in production. MongoDB support also gives ADK users a persistence option outside Google Cloud. Other framework releases, including Strands harness from AWS, are collected under AI Agents.

Questions people ask

What breaks when upgrading to Google ADK 2.13?

Telemetry switches to OpenTelemetry GenAI v2 by default and removes the call_llm span. Setting ADK_TELEMETRY_SCHEMA_VERSION_OPT_IN=otel_semconv_1_36 keeps the old schema.

Does ADK's MongoDB memory need a vector database?

No. Google says MongoDbMemoryService recalls past turns by keyword match on any MongoDB deployment, with no embedding model or vector index. It is experimental.

How much does Google ADK cost?

ADK is open source under the Apache 2.0 license. Model calls and hosting, for example on Agent Engine, are billed separately.

Sources

1. ADK for Python v2.13.0 (Google, 23 Oct 2026)
2. ADK for Python v2.12.0 (Google, 13 Oct 2026)
3. Agent Development Kit README (Google, 28 Oct 2026)