Gists

Abstract

This article explores the integration of Google Workspace with the Antigravity CLI, the high-performance successor to the legacy Gemini CLI. This integration is critical because it bridges the gap between low-latency, local agent execution and cloud-native enterprise productivity platforms. We demonstrate this framework by evaluating five core developer tools—the Google Workspace CLI, gas-fakes, ggsrun, GASADK/GoogleApiApp, and goodls—and mapping their capabilities into distinct local, hybrid, and cloud execution layers. Our analysis reveals how this unified architecture streamlines complex, multi-step agentic workflows while optimizing resource consumption, establishing a blueprint for next-generation workspace automation.

Gists

Abstract

This paper presents a serverless architecture that overcomes the stateless nature and 6-minute execution limit of Google Apps Script (GAS). By configuring a 1-second immediate timeout in UrlFetchApp loopback calls, an orchestrator dispatches background tasks and terminates immediately. This design frees up the caller’s execution quota while the target Web App runs to completion in an isolated container. Combined with a transactional Google Sheets state machine, this design supports self-perpetuating parallel MapReduce runs and multi-turn, state-hydrated generative AI agent networks without external compute infrastructure.

GASADK was updated to v1.3.0

• v1.3.0 (June 3, 2026)
  • Added Custom Server Name Routing to specify user-defined server names as aliases in MCP and A2A configurations.
  • Supports string URLs or custom JSON objects (with custom server names as keys and URL configurations as values) in mcpServers and a2aServerAgentCardURLs.

You can see the detail information here https://github.com/tanaikech/adk-gas

Gists

Abstract

Configuring complex time-driven triggers in Google Apps Script—such as executing tasks exclusively on weekday mornings—is notoriously intractable programmatically and strictly impossible via the standard UI. TriggerApp mitigates this architectural friction through a declarative JSON engine, allowing developers to completely bypass granular date-math logic. Now, by embedding a native Model Context Protocol (MCP) server, we cross into a definitive paradigm shift. Developers can orchestrate complex, continuously looping GAS schedules using natural language via Generative AI (Vibe Coding), preserve the hard 20-trigger quota limit through an elegant recursive daisy-chain architecture, and bypass the strict 6-minute execution timeout by dynamically queuing future execution batches.

• v5.0.0 (May 2026) - The Omnibus Architecture Rewrite Engine fundamentally rewritten for Go 1.26.3+. Implemented channel-based concurrency (errgroup), freeze-proof TUI (mpb/v8), SIMD JSON parsing (goccy/go-json), native MCP server (ggsrun mcp), Shared Drives full-support, auto MIME-mapping, isolated fault tolerance, and OAuth2 loopback automation.

You can check ggsrun at https://github.com/tanaikech/ggsrun.

• v3.2.2 (May 27, 2026)

  1. Critical Progress Bar Lifecycle Fix: Fixed a bug where indeterminate file sizes (such as Google Docs exports returning 0 size from the Drive API) caused the progress bar spinners to hang infinitely. Bar completion is now strictly enforced (SetTotal(-1, true)) upon IO completion.
  2. Resource Leak Patches: Hardened HTTP body and local file descriptor management using strictly placed defer statements, preventing memory leaks during connection errors.

• v3.2.1 (May 27, 2026)

Gists

Abstract

Integrating autonomous AI agents into enterprise architectures exposes critical security and latency vulnerabilities. The Autonomous Google API Agent (AGAA) solves this by enforcing a deterministic, zero-trust execution framework directly within Google Apps Script (GAS). By merging GASADK, dynamic REST endpoint resolution via GoogleApiApp, and the Developer Knowledge API through the Model Context Protocol (MCP), AGAA executes complex cross-domain workflows exclusively via natural language. It autonomously researches API schemas, mitigates server-side formula latencies, handles recursive pagination, and mathematically enforces local Role-Based Access Control (RBAC). AGAA enables true “Vibe Coding” across all Google APIs—including Workspace, Analytics, and YouTube—without bloated client libraries.

Overview

GASADK is a groundbreaking Agent Development Kit tailored specifically for the resource-constrained Google Apps Script environment. Driven by a deterministic Planner-Executor-Synthesizer (PES) architecture, it effectively bypasses traditional async Node.js assumptions to overcome the absolute 6-minute execution limit and synchronous blocking networking (UrlFetchApp). Featuring dynamic DAG planning, temporal context anchoring, and seamless integrations with MCP, A2A, and Google Drive-based Agent Skills, GASADK transforms GAS into a bulletproof, high-performance runtime for autonomous AI agents. Install via the dedicated library key to orchestrate robust, zero-latency multi-agent systems today.

Gists

Abstract

Google’s Agent Development Kit (ADK) revolutionizes autonomous AI agents, yet its standard Node.js-based asynchronous ReAct architecture is fundamentally incompatible with the restrictive, synchronous, and time-bound execution environment of Google Apps Script (GAS). To unlock enterprise-grade AI natively within Google Workspace, this paper introduces GASADK. By abandoning the cyclical ReAct loop in favor of a deterministic Planner-Executor-Synthesizer (PES) architecture, GASADK proactively manages execution constraints, synchronous network blocking, and payload limits. This framework successfully implements multi-agent orchestration, the Model Context Protocol (MCP), and Agent-to-Agent (A2A) communication directly within GAS, empowering developers to build highly resilient, serverless AI workflows that seamlessly manipulate Workspace applications.

GeminiWithFiles was updated to v2.0.16

• v2.0.16 (May 7, 2026)
  1. Introduced invoke_agent functionality to Agent Skills.
  2. Enabled subagent orchestration, allowing parent agents to spawn independent executing contexts (Subagents) for specialized tasks without polluting the main conversation history.

You can see the detail information here https://github.com/tanaikech/GeminiWithFiles