# ADK 2.14.0 graduates execution environments, A2A, and evaluation from experimental

> ADK v2.14.0 cuts long multi-turn workflow runtimes by 23% with 9% lower peak memory and graduates execution environments, evaluation, and A2A modules to stable.

- Type: AI news
- Page: https://completeaitraining.com/news/adk-2140-graduates-execution-environments-a2a-and/
- Published: 2026-11-08
- Updated: 2026-11-08
- Author: Jeroen Erne
- Jobs: Creatives, Customer support, Education, Healthcare, Hospitality and events, IT and development, Management, Product development, Sales, Science and research, Writers
- Reading time: 2 min

The ADK v2.14.0 release, published on November 7, graduates execution environments, A2A, evaluation, and core runtime modules from experimental to stable. It adds multi-run evaluation, semantic tool argument matching, and integrations for Microsoft Teams and OpenAI Decisions, while cutting long multi-turn workflow runtimes by 23%.

#### Execution environments and sandboxes reach stability

**BaseEnvironment**, **LocalEnvironment**, and platform-specific environments for Daytona and E2B are now stable APIs. The update introduces streaming file reads so that `ReadFileTool` can grab line ranges without loading entire files into memory. For large-scale operations, `AgentEngineEnvironment` reads and writes large files in chunks and reports commands running longer than 570 seconds as timed out before hitting the service deadline.

`ContainerCodeExecutor` now supports input and output files. A related security change closes a hole where a prompt-injected model could run arbitrary shell commands: `RunSkillScriptTool` no longer accepts free-form shell commands in environment mode. The model must now pass structured arguments, and only `.py`, `.sh`, and `.bash` scripts are allowed.

#### Evaluation gets multi-run and semantic matching

Evaluators can now run each test case multiple times concurrently using `MultiRunConfig` and `--parallelism`, combining scores or pass rates across runs for steadier verdicts. The new **ToolArgsMatcher** compares tool call arguments using exact, case-insensitive, numeric-tolerance, or LLM-backed semantic checks. `AgentEvaluator` can evaluate agents built at runtime, and `--eval_storage_uri` lets teams store evaluation files outside a read-only agents directory.

#### New integrations and workflow performance

A stable A2A integration lets users build agent cards from the CLI with `adk generate_agent_card`. The release adds a **TeamsRunner** to connect ADK agents to Microsoft Teams chats and channels, and an **OpenAIDecisionsClassifier** that classifies workflow and tool-gating questions using the OpenAI Decisions API. **MongoDbMemoryService** gains vector search retrieval and TTL expiration.

On the performance side, a 30-node workflow's 200-turn run is now 23% faster with 9% lower peak memory. Token-threshold compaction now counts tool outputs since the last model call, triggering earlier for an unchanged threshold.

#### Breaking and behavior changes to note

Enum tool arguments now arrive as members rather than raw strings. A plain `Enum` argument will no longer work with string comparisons, dict lookups, or `json.dumps` \- teams should compare against members, use `.value`, or switch to `StrEnum`. `VertexAiSessionService.create_session` now accepts only session config options; extra keyword arguments raise `ValueError`.

`DatabaseSessionService` creates new PostgreSQL JSON columns as `json` instead of `jsonb`. Existing `jsonb` tables keep working, but queries relying on jsonb-only operators or GIN indexes won't function on new tables. Sixteen toolset and integration features promoted to stable - including Bigtable, Computer Use, and MCP agent server - are now always available and can no longer be blocked by feature flags.

#### Why this matters for IT and development professionals

For teams building agent workflows, the 23% speed improvement on complex multi-turn runs and the lower memory footprint translate directly to reduced infrastructure costs. The shift to structured tool arguments and the closure of the shell command injection vector in `RunSkillScriptTool` address a concrete security risk for anyone running models that process untrusted input. If your team relies on PostgreSQL session storage, plan for the `json` versus `jsonb` change before spinning up new tables - existing deployments won't break, but new schemas will lack jsonb indexing unless you migrate manually. Professionals looking to build these skills can explore [AI for Developers Courses](https://completeaitraining.com/tag/it-and-development/) that cover agent architecture and deployment patterns.

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