GASADK (Agent Development Kit for Google Apps Script)

GASADK Diagram

Welcome to GASADK, the ultimate Agent Development Kit (ADK) designed specifically for the Google Apps Script (GAS) environment.

Operating within the strict constraints of Google Apps Script—specifically the absolute 6-minute execution limit and synchronous blocking networking—demands an architecture that discards the optimistic assumptions of traditional Node.js environments. GASADK is a highly engineered survival architecture. Inspired by the @google/adk, this initial release of GASADK introduces the heavily optimized LlmAgent. It replaces unbounded, recursive ReAct loops with a deterministic, phase-separated orchestration model, fundamentally solving context bloat, execution latency, and API quota exhaustion.

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🌟 Architecture & Key Innovations

At the core of GASADK is the LlmAgent, powered by the Planner-Executor-Synthesizer (PES) architecture. It utilizes Directed Acyclic Graphs (DAGs) to orchestrate complex task delegations across external tools, Model Context Protocol (MCP) servers, Agent-to-Agent (A2A) networks, and file-based Agent Skills.

Core Safeguards & Optimizations

1. One-Pass Fast-Track (Zero-Latency Bypass)

If the LLM Planner determines that external capabilities are unnecessary for a given prompt (e.g., standard conversational queries), the execution and synthesis phases are completely bypassed. The agent returns a direct response, aggressively slashing API latency and token consumption by avoiding redundant tool queueing.

1. Schema Interception

Even when the Fast-Track attempts to bypass execution, if developers enforce an outputSchema (strict JSON formatting), GASADK intelligently intercepts the bypass. It routes the output through a dummy task directly into the Synthesizer to mathematically guarantee adherence to the requested JSON schema under all circumstances.

1. Temporal Context Anchoring

LLMs suffer from temporal blindness—they cannot natively resolve relative time like "tomorrow" or "last week". GASADK intercepts the system prompt and injects a hardcoded new Date() absolute anchor. The Planner autonomously converts relative requests into absolute ISO 8601 timestamps before pinging external tools (MCP/A2A), completely eliminating date-resolution errors on remote servers.

1. Payload Bulletproofing (Pessimistic Memory Management)

When external servers or massive Google Drive files return tens of thousands of characters, feeding them directly into the context window triggers a fatal 400 Payload Too Large error. GASADK enforces a strict maxResultLength threshold (default 20,000 chars), automatically truncating overflow data. It favors partial data over catastrophic runtime crashes.

1. Dynamic Re-Planning (Targeted ReAct)

Unlike standard ADKs that rely on a continuous ReAct loop for every step, GASADK plans an entire DAG upfront. Only if a node in the DAG execution fails does the system trigger a Re-Plan. It discards the unexecuted queue, analyzes the failure report, and generates an alternative DAG utilizing different tools.

1. Clean History Optimization (A2AApp v2.6.0)

Maintains and propagates conversation history dynamically to sub-agents, MCP servers, and remote A2A servers without polluting the core logic history. Massive internal intermediate LLM reasoning steps (function calls, planning thoughts) are filtered out, constructing a clean user/model role-based chat history to prevent token bloat and quota exhaustion.

1. Fast-Track Halt Optimization

Allows server functions to forcefully bypass the server-side LLM synthesis loop (by returning _gemini_halt: true). This prevents endless generative loops, eliminates unnecessary token usage, and guarantees instant response times for purely algorithmic/computational tool executions.

1. Direct JSON-RPC Bypass & Direct Routing (v1.3.1)

Bypasses the entire multi-phase LLM mock orchestration when directRouting is flagged and a single target card is assigned, dispatching the JSON-RPC request natively to slash network latency. It also supports local pre-fetched Agent Cards through a2aServerAgentCardJSONs to completely bypass remote HTTP fetches.

1. Multi-Channel Log Propagation (v1.3.3)

Supports explicit log propagation from the orchestrator down to sub-clients (MCPApp and A2AApp), storing logs inside dedicated, isolated Sheets (raw, MCP, A2A, MCPA2Aserver_log) dynamically. It guarantees thread-safe writes using script lock protection under high-concurrency environments.

1. Global Scope Initialization Fix (v1.3.4)

Resolves compilation ReferenceError during global script initialization in Google Apps Script by removing unbound variables (accessKey, webAppsUrl) from the global context of agentCard_ToolsForMCPServer.js and instead using runtime shadow cloning for context safety.

GASADK vs. TS ADK (@google/adk) Paradigm Shift

⚙️ GASADK Workflow Architecture

The execution lifecycle of LlmAgent is rigorously compartmentalized. The diagram below details the exact chronological flow from the moment agent.run() is invoked to the final synthesized response.

graph TD
    %% Styling Definitions
    classDef userReq fill:#f9f,stroke:#333,stroke-width:2px,color:#000;
    classDef core fill:#bbf,stroke:#333,stroke-width:2px,color:#000;
    classDef llm fill:#fbb,stroke:#333,stroke-width:2px,color:#000;
    classDef external fill:#bfb,stroke:#333,stroke-width:2px,color:#000;
    classDef decision fill:#ff9,stroke:#333,stroke-width:2px,color:#000;
    classDef safeguard fill:#f66,stroke:#333,stroke-width:2px,color:#000;

    %% Nodes
    Start(["User: agent.run(prompt)"])
    InitCaps["Initialize Capabilities<br>(Tools, MCP, A2A, Skills)"]
    InjectTime["Inject Temporal Context<br>System Time Anchor"]
    Planner["LLM: Planner Phase<br>Generate DAG & JSON Schema"]

    FastTrackDec{"requires_<br>capabilities<br>== false?"}
    SchemaDec{"outputSchema<br>defined?"}
    ReturnDirect(["Return direct_answer<br>Execution Bypassed"])

    PopTask["Pop Task from planQueue"]
    CheckTime{"Elapsed Time ><br>timeoutMs<br>(280s)?"}
    TimeoutAbort["Trigger Safe Abort<br>Stop Queue"]

    InjectContext["Inject 'depends_on'<br>Context to Prompt"]
    ExecRouter{"Capability<br>Type?"}

    ExecMCP["MCP Server Client"]
    ExecA2A["A2A Server Client"]
    ExecSkill["Agent Skill LLM Call"]
    ExecNative["Native Function Calling"]

    CheckErr{"Execution<br>Error?"}
    Truncate["Payload Truncation<br>> maxResultLength"]
    SaveResult["Save to taskResults"]

    CheckEmpty{"planQueue<br>Empty?"}
    CheckReplan{"replanCount <<br>maxReplans?"}
    DropQueue["Discard Remaining DAG"]
    Replanner["LLM: Dynamic Re-Planner<br>Avoid Failed Method"]

    SynthPhase["LLM: Final Synthesis<br>Analyze Gathered Data"]
    End(["Return Final Answer"])

    A2A_Registry[("A2A App")]
    MCP_Servers[("MCP Servers")]
    GDrive[("Google Drive")]

    %% Edges
    Start --> InitCaps
    InitCaps -. "Fetch Agent Cards" .-> A2A_Registry
    InitCaps -. "tools/list Request" .-> MCP_Servers
    InitCaps -. "Read .md Skills" .-> GDrive

    InitCaps --> InjectTime --> Planner
    Planner --> FastTrackDec

    FastTrackDec -- "Yes (Fast-Track)" --> SchemaDec
    SchemaDec -- "No (Raw output fine)" --> ReturnDirect
    SchemaDec -- "Yes (Intercept)" --> SynthPhase

    FastTrackDec -- "No (Capabilities required)" --> PopTask

    PopTask --> CheckTime
    CheckTime -- "Timeout Exceeded" --> TimeoutAbort
    TimeoutAbort --> SynthPhase

    CheckTime -- "Safe" --> InjectContext
    InjectContext --> ExecRouter

    ExecRouter -- "MCP Server" --> ExecMCP
    ExecRouter -- "A2A Server" --> ExecA2A
    ExecRouter -- "Agent Skill" --> ExecSkill
    ExecRouter -- "Native/Built-in" --> ExecNative

    ExecMCP --> CheckErr
    ExecA2A --> CheckErr
    ExecSkill --> CheckErr
    ExecNative --> CheckErr

    CheckErr -- "Success" --> Truncate --> SaveResult
    SaveResult --> CheckEmpty
    CheckEmpty -- "No" --> PopTask
    CheckEmpty -- "Yes" --> SynthPhase

    CheckErr -- "Failed" --> CheckReplan
    CheckReplan -- "Yes (Can Replan)" --> DropQueue --> Replanner
    Replanner --> PopTask
    CheckReplan -- "No (Max Replans)" --> SaveResult

    SynthPhase --> End

    %% Assign Classes
    class Start,ReturnDirect,End userReq;
    class InitCaps,InjectTime,PopTask,InjectContext,ExecNative,SaveResult core;
    class Planner,ExecSkill,Replanner,SynthPhase llm;
    class ExecMCP,ExecA2A,A2A_Registry,MCP_Servers,GDrive external;
    class FastTrackDec,SchemaDec,CheckTime,ExecRouter,CheckErr,CheckEmpty,CheckReplan decision;
    class TimeoutAbort,Truncate,DropQueue safeguard;

🧩 Supported Capabilities

GASADK acts as a universal adapter, normalizing disparate protocols into a unified schema for the Planner.

• Native Tools: Wrap standard Google Apps Script functions directly into the capability schema.
• MCP Servers: Native integration to dynamically discover (tools/list) and invoke tools on external servers using the Model Context Protocol.
• A2A Servers: Cross-agent communication. Fetch remote Agent Cards and utilize other autonomous agents as local tools via the Agent-to-Agent protocol.
• Sub-Agents: Nest instances of LlmAgent locally. Delegate complex cognitive sub-tasks without corrupting the main orchestrator's context.
• Agent Skills: Dynamically load behavioral skills stored as Markdown (.md) files inside Google Drive. A native GAS hack for distributed, RAG-like prompt injection.
• Built-in Tools: Comes equipped with a Python CodeExecutor and native GoogleSearch capabilities.

📥 Installation & Core Dependencies

GASADK integrates multiple high-performance GAS libraries under the hood. You can use it as a standalone library or copy the source code directly.

GASADK is constructed by the following scripts.

• GADADK
• GeminiWithFiles
• A2AApp
• MCPApp
• MCPA2Aserver
• FileSearchApp

Option 1: Use as a GAS Library (Recommended)

1. Open your GAS project.
2. Navigate to Libraries on the left panel and click "+".
3. Enter the Project Key: 1w2mwhWQd4_6rom-UBRPD8gayBoqGH_87awSBVqGI8DdaQI_pOeSuGYDu
4. Select the latest version and set the identifier to GASADK.
5. Click Add.

After GASADK was installed, you can use it as follows.

const { LlmAgent, MCPA2Aserver, FileSearch } = GASADK;

All objects are the class objects.

You can also directly use GeminiWithFiles, A2AApp, and MCPApp like const { LlmAgent, GeminiWithFiles, MCPA2Aserver, FileSearch, GeminiWithFiles, A2AApp, MCPApp } = GASADK.

Option 2: Copy & Paste Directly

If you want to directly use GASADK by including all scripts in a Google Apps Script project, please copy and paste the following script. The following script includes all required scripts for using GASADK.

https://github.com/tanaikech/adk-gas/blob/master/dist/GASADK.js

In this case, you can directly use the class objects. So, you are not required to set const { LlmAgent, GeminiWithFiles, MCPA2Aserver, FileSearch, GeminiWithFiles, A2AApp, MCPApp } = GASADK.

🛠 LlmAgent Configuration API

The new LlmAgent(config) constructor accepts an extensive configuration object to dictate agent behavior and safety parameters.

Custom Server Name Routing (v1.3.0+)

From v1.3.0, you can specify custom user-defined server names in mcpServers and a2aServerAgentCardURLs by using a JSON object instead of a simple string URL. This custom name is injected into the LLM context to ensure accurate routing when the user references specific servers by their aliases.

#### Format:

• String URL (Standard): "https://example.com/mcp"
• JSON Object (Custom Name): { "custom-server-alias": { "httpUrl": "https://example.com/mcp" } }

#### Example:

const agent = new LlmAgent({
  apiKey: API_KEY,
  mcpServers: [
    "https://basic.mcp.example.com", // Standard string URL
    {
      "server-trigger-test-project1": { // Custom server name
        httpUrl: "https://script.google.com/macros/s/{deploymentID}/exec?accessKey=sample"
      }
    }
  ],
  a2aServerAgentCardURLs: [
    {
      "my-custom-a2a-agent": { // Custom A2A server name
        httpUrl: "https://script.google.com/macros/s/{deploymentID}/exec/.well-known/agent-card.json?accessKey=sample"
      }
    }
  ]
});

Local JSON Bypass & Direct Routing (v1.3.1+)

From v1.3.1, you can pass pre-fetched Agent Card JSON objects directly to a2aServerAgentCardJSONs. This completely bypasses the HTTP card retrieval process, resolving the network latency overhead. When combined with directRouting, GASADK automatically bypasses the internal multi-step LLM planning proxy layers, executing direct JSON-RPC dispatch to the remote agent.

#### Example:

const agent = new LlmAgent({
  apiKey: API_KEY,
  a2aServerAgentCardJSONs: [
    {
      "local-cached-agent": { // Custom server name
        name: "CachedAgent",
        url: "https://script.google.com/macros/s/{deploymentID}/exec",
        description: "Directly loaded JSON agent card.",
        skills: [
          {
            id: "get_exchange_rate",
            name: "Currency Exchange Rates Tool",
            description: "Helps with exchange values",
            inputModes: ["text/plain"],
            outputModes: ["text/plain"]
          }
        ]
      }
    }
  ]
});

Multi-Channel Log Propagation & Auto-Sheet Initialization (v1.3.3+)

From v1.3.3, you can activate thread-safe, multi-channel logging simply by providing a logSpreadsheetId in the LlmAgent configuration. When configured, this property is automatically propagated down to downstream MCPApp and A2AApp instances.

The library validates and automatically creates the following isolated sheets inside the designated spreadsheet:

• `raw`: Automatically records serialized raw HTTP event payloads (e.g. GET/POST objects) using safe JSON serialization (serializeEvent_) to prevent cyclical structure reference crashes.
• `MCP`: Records Model Context Protocol client-side and server-side RPC log history.
• `A2A`: Records Agent-to-Agent client-side and server-side transaction logging.
• `MCPA2Aserver_log`: Records internal dispatcher execution trails.

All sheet creation and row insertions are protected by thread-safe LockService structures, preventing data loss or layout errors during high-concurrency parallel executions.

#### Example:

const agent = new LlmAgent({
  apiKey: API_KEY,
  logSpreadsheetId: "YOUR_LOG_SPREADSHEET_ID_HERE", // Set the ID here
  mcpServers: ["https://example.com/mcp-server"],
  a2aServerAgentCardURLs: ["https://example.com/a2a-server/.well-known/agent-card.json"]
});

Core Methods

• setServices({ lock, properties })

Mandatory for safe execution. Binds LockService.getScriptLock() and PropertiesService to the agent. Prevents catastrophic state corruption and race conditions when multiple webhooks execute concurrently.

• run(prompt, logCallback)

Executes the orchestrator. Takes the user prompt and an optional logCallback function to emit real-time telemetry on DAG planning and execution. Returns the final synthesized string, or a strictly formatted JSON object if outputSchema was defined.

• setHistory(history)

Sets the conversation history for the agent, enabling multi-turn context retention across runs. The history must be an array of objects compatible with GeminiWithFiles.

• getHistory()

Retrieves the current conversation history.

• getAgentInf()

An introspection tool that returns an array of all internally normalized capabilities (from Native tools, MCP, A2A, and Skills) bound to the agent.

💻 Quick Start & Usage Examples

When you try to test the following sample script, please set your API key for using Gemini API to the property GEMINI_API_KEY of Google Apps Script project.

1. Minimal Quickstart (Fast-Track Routing)

A basic conversational initialization.

// Remove destructuring if you copied the scripts directly.
const { LlmAgent } = GASADK;

function test_quickstart() {
  const properties = PropertiesService.getScriptProperties();
  const API_KEY = properties.getProperty("GEMINI_API_KEY");

  const agent = new LlmAgent({
    apiKey: API_KEY,
    name: "HelperAgent",
    model: "models/gemini-3.1-flash-lite",
    instruction: "You are a helpful AI assistant.",
  });

  agent.setServices({
    lock: LockService.getScriptLock(),
    properties: properties,
  });

  // Because no tools are required, the Fast-Track bypass routes this directly.
  const response = agent.run("Hello, who are you?", (logEntry) => {
    console.log(`[Log ${logEntry.timestamp}] ${logEntry.message}`);
  });

  console.log(response);
}

In the following script, the second argument receives the detailed real-time log as a callback function.

const response = agent.run("Hello, who are you?", (logEntry) => {
  console.log(`[Log ${logEntry.timestamp}] ${logEntry.message}`);
});

2. Use subagents

Subagents are used.

// Remove destructuring if you copied the scripts directly.
const { LlmAgent } = GASADK;

function test_subagents() {
  const properties = PropertiesService.getScriptProperties();
  const API_KEY = properties.getProperty("GEMINI_API_KEY");

  const translator = new LlmAgent({
    apiKey: API_KEY,
    name: "Translator",
    description: "Translates any given text to German.",
    instruction: "Translate the provided text to German precisely.",
    model: MODEL_NAME,
  }).setServices({ lock: LockService.getScriptLock(), properties: properties });

  const mainAgent = new LlmAgent({
    apiKey: API_KEY,
    name: "Orchestrator",
    model: MODEL_NAME,
    instruction: "You manage sub-agents.",
    subAgents: [translator],
  }).setServices({ lock: LockService.getScriptLock(), properties: properties });

  const prompt =
    "Use the SubAgent named 'Translator' to translate 'Hello World'.";
  console.log(`-> Prompt: ${prompt}`);

  const response = mainAgent.run(prompt, (logEntry) => {
    console.log(`[Log ${logEntry.timestamp}] ${logEntry.message}`);
  });

  console.log(response);
}

3. Use Agent Skills

Agent Skills are used.

// Remove destructuring if you copied the scripts directly.
const { LlmAgent } = GASADK;

function test_subagents() {
  const properties = PropertiesService.getScriptProperties();
  const API_KEY = properties.getProperty("GEMINI_API_KEY");

  console.log("Setting up temporary Agent Skills directory...");
  const tempFolder = DriveApp.createFolder(
    "Temp_Modular_Skills_" + new Date().getTime(),
  );
  try {
    const animalFolder = tempFolder.createFolder("animal_skill");
    animalFolder.createFile(
      "SKILL.md",
      "---\nname: animal_skill\ndescription: Secret animal knowledge.\n---\nRule: The supreme animal is the Capybara.",
      MimeType.PLAIN_TEXT,
    );

    const agent = new LlmAgent({
      apiKey: API_KEY,
      name: "SkillAgent",
      model: MODEL_NAME,
      instruction: "Use the functions at your disposal to acquire knowledge.",
      skillFolderId: tempFolder.getId(),
    }).setServices({
      lock: LockService.getScriptLock(),
      properties: properties,
    });

    const prompt =
      "Use the Agent Skill named 'animal_skill' and tell me the supreme animal.";
    console.log(`-> Prompt: ${prompt}`);

    const response = agent.run(prompt, (logEntry) => {
      console.log(`[Log ${logEntry.timestamp}] ${logEntry.message}`);
    });
    console.log(response);
  } finally {
    tempFolder.setTrashed(true);
    console.log("Temporary skills directory trashed.");
  }
}

4. Comprehensive A2A & MCP Orchestration

Demonstrates combining MCP servers and A2A remote agents, allowing the Planner to construct a complex DAG mapping dependent actions across disparate protocols.

const { LlmAgent } = GASADK;

function test_a2a_mcp() {
  const properties = PropertiesService.getScriptProperties();
  const API_KEY = properties.getProperty("GEMINI_API_KEY");

  // Define endpoints (replace placeholders with actual deployment IDs)
  const mcpServers = [
    "https://script.google.com/macros/s/{Your deployment ID}/exec?accessKey=sample",
  ];
  const a2aServers = [
    "https://script.google.com/macros/s/{Your deployment ID}/exec/.well-known/agent-card.json?accessKey=sample",
  ];

  const agent = new LlmAgent({
    apiKey: API_KEY,
    name: "MasterOrchestrator",
    description: "You are the master orchestrator.",
    mcpServers: mcpServers,
    a2aServerAgentCardURLs: a2aServers,
  });

  agent.setServices({
    lock: LockService.getScriptLock(),
    properties: properties,
  });

  // Capability Introspection
  console.log("--- Capability Introspection ---");
  console.log(JSON.stringify(agent.getAgentInf(), null, 2));

  // The relative time ("tomorrow") triggers the Temporal Context Anchoring logic.
  const prompt =
    "Please use the MCP Server to check the current exchange rate between USD and GBP, and then use the A2A Server to get the weather in Tokyo for tomorrow's lunchtime.";

  const result = agent.run(prompt, (logEntry) => {
    console.log(`[Log ${logEntry.timestamp}] ${logEntry.message}`);
    if (logEntry.data && logEntry.data.plan) {
      console.log("=== Execution Plan (DAG) ===");
      console.log(JSON.stringify(logEntry.data.plan, null, 2));
    }
  });

  console.log("--- Final Synthesized Result ---");
  console.log(result);
}

5. Deploying a Consolidated A2A & MCP Server

GASADK includes MCPA2Aserver, allowing you to expose your own native GAS functions as both an MCP Server and an A2A Server simultaneously from a single web app deployment.

In this case, after you deploy Web Apps, set `WEB_APPS_URL` by replacing with your Web Apps URL and deploy Web Apps to reflect the latest script.

const { MCPA2Aserver } = GASADK;

const API_KEY =
  PropertiesService.getScriptProperties().getProperty("GEMINI_API_KEY");
const WEB_APPS_URL =
  "https://script.google.com/macros/s/{Your deployment ID}/exec";

const object = {
  apiKey: API_KEY,
  model: "models/gemini-3.1-flash-lite",
  accessKey: "sample",
};

const doGet = (e) => main(e);
const doPost = (e) => main(e);

function main(e) {
  const lock = LockService.getScriptLock();
  const context = createServerContext_(); // Defines functions and agentCard
  const m = new MCPA2Aserver();

  m.setServices({ lock: lock });
  m.apiKey = object.apiKey;
  m.model = object.model;

  // Pre-configure server-side injected history (System Context/Persona)
  m.setHistory([
    {
      role: "user",
      parts: [{ text: "System Context Override: You are an elite financial API node named OMEGA-SERVER, located securely in Tokyo." }]
    },
    {
      role: "model",
      parts: [{ text: "Understood. My secret access code is OMEGA-99." }]
    }
  ]);

  // Force enablement of both server protocols
  m.a2a = true;
  m.mcp = true;
  m.accessKey = object.accessKey;

  const res = m.main(e, context, (log) => {
    console.log(`[${log.level}] ${log.timestamp} - ${log.message}`);
  });

  return res;
}

function createServerContext_() {
  const functions = {
    params_: {
      get_exchange_rate: {
        description: "Get current exchange rate.",
        parameters: {
          type: "object",
          properties: {
            currency_from: { type: "string" },
            currency_to: { type: "string" },
            currency_date: { type: "string" },
          },
          required: ["currency_from", "currency_to", "currency_date"],
        },
      },
      chat_and_identity: {
        description: "Answer general conversation, identity, location, and secret code questions based on the chat history.",
        parameters: {
          type: "object",
          properties: {
            message: { type: "string", description: "The complete, detailed response message." }
          },
          required: ["message"]
        }
      }
    },
    get_exchange_rate: (args) => {
      // Internal execution logic here...
      const res = `Rate from ${args.currency_from} to ${args.currency_to} is 0.82.`;

      const returnObj = {
        mcp: {
          jsonrpc: "2.0",
          result: { content: [{ type: "text", text: res }], isError: false },
        },
        a2a: { result: res },
      };

      // [Fast-Track Halt Optimization]: Return immediately to bypass secondary LLM synthesis loops
      return {
        ...returnObj,
        _gemini_halt: true,
        items: { functionResponse: returnObj }
      };
    },
    chat_and_identity: (args) => {
      const res = args.message || "I have processed your chat request.";
      const returnObj = {
        mcp: {
          jsonrpc: "2.0",
          result: { content: [{ type: "text", text: res }], isError: false },
        },
        a2a: { result: res },
      };
      return {
        ...returnObj,
        _gemini_halt: true,
        items: { functionResponse: returnObj }
      };
    }
  };

  const agentCard = {
    name: "API Manager",
    description: "Provide management for using various APIs and handle conversational queries.",
    url: WEB_APPS_URL + `?accessKey=${object.accessKey}`,
    skills: [
      { id: "get_exchange_rate", name: "Exchange Rate Tool" },
      { id: "chat_and_identity", name: "Chat and Identity" }
    ],
  };

  return { functions, agentCard };
}

6. Chat History & Context Retention

GASADK supports seamless multi-turn conversation tracking across the orchestrator, remote agents, and Consolidated servers.

const { LlmAgent } = GASADK;

function test_chat_history() {
  const properties = PropertiesService.getScriptProperties();
  const API_KEY = properties.getProperty("GEMINI_API_KEY");

  const agent = new LlmAgent({
    apiKey: API_KEY,
    name: "ChattyAgent",
    model: "models/gemini-3.1-flash-lite",
  }).setServices({ lock: LockService.getScriptLock(), properties: properties });

  // 1. Manually seed/restore conversation history
  agent.setHistory([
    { role: "user", parts: [{ text: "Hello, my project code is XRAY-7." }] },
    { role: "model", parts: [{ text: "Acknowledged. I have recorded your project code as XRAY-7." }] }
  ]);

  // 2. Run query referencing the context
  const response = agent.run("What is my project code?");
  console.log("Response:", response); // Output will resolve using the history

  // 3. Inspect updated history
  console.log("Updated History:", JSON.stringify(agent.getHistory(), null, 2));
}

7. Exposing Google APIs as a Dynamic MCP/A2A Server (via GoogleApiApp)

By combining GASADK with the GoogleApiApp library, you can build a stateless proxy that dynamically exposes Google Workspace services (Sheets, Drive, Docs, Calendar, Gmail, Slides, etc.) to any LLM-powered agent as local MCP tools.

For the full implementation script and a beginner-friendly setup guide, see: 👉 [Google API MCP Server Sample & Setup Guide](https://github.com/tanaikech/adk-gas/tree/master/samples/googleapiapp-mcp-server)

License

MIT

🛡 Hooks System & Workspace Safety Guardrails (v1.3.5+)

GASADK introduces a comprehensive Hooks system fully compliant with the Gemini CLI specifications. Developers can intercept the execution lifecycle of the agent, apply validations, inject context, mock responses, or enforce safety guardrails.

Lifecycle Hook Events

The system supports the following lifecycle events:

• SessionStart: Triggered at session initiation (useful for context load or initialization).
• SessionEnd: Triggered at session termination (exit or error).
• BeforeAgent: Fired after user input but before planning. Can block execution or inject prompt context.
• AfterAgent: Fired after the final synthesis. Can trigger context clearing or execute conditional retries.
• BeforeModel: Fired before LLM request transmission. Allows config override or returning mock responses.
• AfterModel: Fired upon receiving LLM response. Allows response rewriting or blocking.
• BeforeToolSelection: Fired before capability selection. Allows toolMode and function white-listing.
• BeforeTool: Fired before a tool is executed. Blocks execution or overrides arguments.
• AfterTool: Fired after tool execution. Allows result editing, masking, or error handling.
• PreCompress: Fired before history context compaction occurs.
• Notification: Fired when system logs or alerts occur.

Workspace Safety Guardrails Example

Below is a practical implementation of BeforeTool hooks to construct safety guardrails. It prevents an autonomous agent from executing dangerous operations (e.g., sending emails to external addresses or deleting critical folders) by validating model-generated arguments at the tool execution boundary.

#### 1. Defining the Guardrail Hook This GAS function intercepts Gmail transmission and Drive folder deletion:

function checkWorkspaceSafetyGuardrail(input) {
  const toolInput = input.tool_input || {};
  
  // Gmail Safety Check: Block external domains
  if (input.tool_name === "gmail_send_email") {
    const toAddress = toolInput.to || "";
    if (!toAddress || !toAddress.endsWith("@mycompany.com")) {
      return {
        decision: "deny",
        reason: "Blocked: Sending mail to external domains (" + toAddress + ") is restricted."
      };
    }
  }
  
  // Google Drive Safety Check: Prevent critical asset deletion
  if (input.tool_name === "drive_delete_file") {
    const fileId = toolInput.fileId || "";
    if (fileId === "protected_root_id") {
      return {
        decision: "deny",
        reason: "Blocked: Deletion of the protected root resource is strictly prohibited."
      };
    }
  }
  
  return { decision: "allow" };
}

#### 2. Registering Hooks in the Agent Config Instantiate the LlmAgent with the safety hook registered under the BeforeTool event:

const agent = new LlmAgent({
  apiKey: API_KEY,
  name: "SecureWorkspaceAgent",
  hooks: {
    "BeforeTool": [
      {
        "matcher": "gmail_send_email|drive_delete_file", // RegExp tool matching
        "type": "gas_function",
        "functionName": "checkWorkspaceSafetyGuardrail"
      }
    ]
  },
  tools: [
    {
      name: "gmail_send_email",
      description: "Send emails to targets. Argument 'to' must contain the email address.",
      parameters: {
        type: "object",
        properties: { to: { type: "string" }, body: { type: "string" } },
        required: ["to", "body"]
      },
      function: (args) => {
        // Actual Gmail transmission code
        return "Email sent successfully to " + args.to;
      }
    },
    {
      name: "drive_delete_file",
      description: "Delete files or folders. Argument 'fileId' must contain the ID.",
      parameters: {
        type: "object",
        properties: { fileId: { type: "string" } },
        required: ["fileId"]
      },
      function: (args) => {
        // Actual Drive deletion code
        return "File " + args.fileId + " deleted successfully.";
      }
    }
  ]
}).setServices({
  lock: LockService.getScriptLock(),
  properties: PropertiesService.getScriptProperties(),
  globalContext: this // Exposes the global checkWorkspaceSafetyGuardrail function
});

When the agent runs, any attempt to call gmail_send_email with an external domain, or drive_delete_file with the protected ID will be immediately intercepted and blocked.

🔄 Human-in-the-Loop (HITL) & Resumption (v2.0.0+)

GASADK v2.0.0 introduces Human-in-the-Loop (HITL) capability. When a hook function returns a decision of "suspend", the agent immediately saves its execution state (including prompt, plan, task results, history, and log traces) to script properties under HITL_STATE_[sessionId], then aborts the loop by throwing a "SUSPENDED" exception.

This allows developers to pause execution, solicit user confirmation (e.g., via Email, Slack, or a Web App form), and resume execution from the exact task that was suspended.

#### 1. Defining a Suspension Hook Return decision: "suspend" inside a BeforeTool hook to request human approval:

function checkGmailApproval(input) {
  if (input.tool_name === "gmail_send_email") {
    // Suspend execution for human review
    return {
      decision: "suspend",
      reason: "Requires human review before sending email to " + (input.tool_input?.to || "recipient")
    };
  }
  return { decision: "allow" };
}

#### 2. Catching the Suspension and Resuming Run the agent, catch the suspension, and resume later using the resume() method:

const agent = new LlmAgent({
  apiKey: API_KEY,
  name: "ApprovalAgent",
  hooks: {
    "BeforeTool": [
      {
        "matcher": "gmail_send_email",
        "type": "gas_function",
        "functionName": "checkGmailApproval"
      }
    ]
  },
  tools: [...]
}).setServices({
  lock: LockService.getScriptLock(),
  properties: PropertiesService.getScriptProperties(),
  globalContext: this // Exposes checkGmailApproval
});

const sessionId = agent.sessionId;

try {
  agent.run("Use gmail_send_email to email [email protected]");
} catch (err) {
  if (err.message.includes("SUSPENDED")) {
    console.log("Execution suspended. Session ID: " + sessionId);
    // Send approval link/email to human
  } else {
    throw err;
  }
}

// --- Later (e.g. from a Web App doGet/doPost approval trigger) ---
function approveSession(sessionId) {
  const resumeAgent = new LlmAgent({
    apiKey: API_KEY,
    name: "ApprovalAgent",
    tools: [...]
  }).setServices({
    lock: LockService.getScriptLock(),
    properties: PropertiesService.getScriptProperties()
  });

  // Resume execution with 'allow' decision
  const result = resumeAgent.resume(sessionId, "allow");
  console.log("Resumed Final Result: " + result);
}

📊 API Quota Safeguards & Token Accumulation (v2.0.0+)

To prevent runaway loops or budget overrun, GASADK automatically tracks token usage per session. You can enforce a strict token limit using the maxTokensPerSession option.

const agent = new LlmAgent({
  apiKey: API_KEY,
  maxTokensPerSession: 50000 // Limit session to 50k tokens
});

Author

Tanaike

Update History

• v1.0.0 (May 18, 2026)
• Initial release.

• v1.0.2 (May 18, 2026)
• Modified. Updated .gitignore

• v1.1.0 (May 19, 2026)
• MCPApp and A2AApp were updated to support custom headers for authenticated connections.
• LlmAgent was updated to integrate these changes, allowing seamless interaction with secure MCP servers and remote agents.
• Added support for structured object input (URL and headers) in mcpServers and agentCardUrls.

• v1.2.0 (May 21, 2026)
• Added full Chat History context retention across LlmAgent, A2AApp, and MCPA2Aserver.
• Introduced Clean History Optimization in A2AApp (v2.6.0) to filter intermediate LLM reasoning steps from conversation history, preventing token bloat.
• Implemented server-side History Injection and Event Object cloning in MCPA2Aserver.
• Introduced Fast-Track Halt Optimization (_gemini_halt) to bypass synthesis loops on explicit server functions.

• 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.

• v1.3.1 (June 7, 2026)
• Added a2aServerAgentCardJSONs support to bypass HTTP fetching of Agent Cards, allowing direct JSON injection.
• Implemented Direct JSON-RPC Bypass (directRouting optimization in A2AApp v2.7.0) to route requests directly to remote agents, slashing network and orchestration latency when specific target agents are known.
• Added strict ban on synthesis tasks in system prompt instructions to prevent redundant LLM compile phases.

• v1.3.2 (June 9, 2026)
• Updated GeminiWithFiles to v2.0.30.

• v1.3.3 (June 10, 2026)
• Added Multi-Channel Log Propagation down to MCPApp and A2AApp via logSpreadsheetId.
• Automatically creates and isolates logs into dedicated sheets (raw, MCP, A2A, and MCPA2Aserver_log) inside the specified log spreadsheet.
• Implemented thread-safe _getOrCreateSheet helper with LockService validation across MCPApp, A2AApp, and MCPA2Aserver to prevent concurrent sheet insertion conflicts.
• Added raw event serialization (serializeEvent_) to bypass cyclical structure errors when writing to the raw sheet.
• Enhanced error traceability by prefixing log and exception messages (e.g. [MCP Server Error], [A2A Client Error]).

• v1.3.4 (June 10, 2026)
• Resolved ReferenceError during global script compilation in Google Apps Script.
• Removed unbound variables accessKey and webAppsUrl from the global namespace in agentCard_ToolsForMCPServer.js.
• Implemented runtime shadow cloning in MCPA2Aserver.js to dynamically inject the resolved Web App URL context at execution time, ensuring context safety.

• v1.3.5 (June 24, 2026)
• Fully integrated Gemini CLI compliant Hooks system.
• Added support for all 11 lifecycle hook events (including PreCompress and Notification).
• Added automatic injection of session metadata (session_id, transcript_path, cwd, hook_event_name, timestamp) into hook payloads.
• Implemented Native Tool execution wrappers to enable BeforeTool/AfterTool hooks on model-generated arguments, realizing strict security guardrails.
• Enhanced RegExp matcher logic in GasHookManager to properly parse pipe-delimited and wildcard capabilities.

• v2.0.0 (June 24, 2026)
• Exposed Google Workspace APIs as a Dynamic MCP/A2A Server.
• Added a standalone deployment script (DeployMcpServer.js) under samples/googleapiapp-mcp-server to expose the GoogleApiApp library.
• Created a detailed beginner-friendly guide for configuring and deploying Google Workspace services (Sheets, Drive, Docs, Calendar, Gmail, etc.) as dynamic MCP tools.
• Added API Quota Safeguard: Track model token consumption per session and enforce session-level token usage limit check via maxTokensPerSession configuration.
• Added Hook & Session Propagation: Automatically propagate parent hooks security context (hookManager and sessionId) and token constraints down to child sub-agents during hierarchical delegation.
• Added Human-in-the-Loop (HITL) Integration: Supported decision: "suspend" decisions in BeforeTool hooks. Seamlessly serializes and stores task state to PropertiesService (under HITL_STATE_[sessionId]) and throws a "SUSPENDED" exception. Added saveState(), loadState(sessionId), and resume(sessionId, decision, approvedArgs) methods to recover and run suspended agent execution loops.
• Synchronized `src/lib/` Core Libraries: Upgraded bundled core components (GeminiWithFiles.js, A2AApp.js, MCPApp.js, MCPA2Aserver.js, and FileSearchApp.js) to support the GasHookManager hooks system natively (allowing interceptors like BeforeModel, AfterModel, BeforeTool, and AfterTool).
• Ecosystem Modernization: Upgraded default model configurations to models/gemini-3.1-flash-lite across all workspace libraries, tests, and examples to provide modern, cost-effective defaults.
• Modular Standalone Twin Synchronization: Propagated and fully published these updates to all five standalone local repositories (GeminiWithFiles_GAS_lib, A2AApp_GAS_lib, MCPApp_GAS_lib, MCPA2Aserver_GAS_lib, FileSearchApp_GAS_lib) with next-version tags.

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