# Google Agent Development Kit (ADK) vs. LangGraph: Framework Comparison

Selecting an AI agent orchestration framework involves weighing code-first modular software design against stateful graph structures. In this Google Agent Development Kit (ADK) vs. LangGraph comparison, we examine their architectures, state persistence models, execution safety profiles, and hosting runtimes.

Source: https://fast.io/resources/google-adk-vs-langgraph-agentic-orchestration/
Last reviewed: 2026-08-20

## The Architectural Shift in Agentic Orchestration

Building a multi-agent system often forces developers to choose between two distinct engineering philosophies: treating agents as nodes in a rigid state machine, or writing them as independent, object-oriented software modules. When agents must collaborate on complex workloads, selecting the wrong framework results in brittle state transitions, language lock-in, and severe execution security vulnerabilities.

While LangGraph models agent interactions as stateful directed cyclic graphs (DAGs/graphs) with nodes and conditional edges, Google's Agent Development Kit (ADK) provides a code-first, framework-agnostic programming interface supporting multiple languages including Python, TS, Java, and Go.

These two frameworks address the agentic orchestration challenge from opposite directions. LangGraph, developed as an extension of the LangChain ecosystem, assumes that complex agent behavior is best managed by explicitly mapping the flow of control. LangGraph was introduced in January 2024 as a module on top of LangChain to support cyclical graphs for agent runtimes. It uses a graph structure to define how information moves between LLM nodes, tool calls, and conditional branches. This provides developers with granular control over loops and state mutations, which is essential for deterministic systems.

Google ADK, by contrast, treats agent orchestration as standard software development. Instead of defining a rigid graph envelope, developers write standard object-oriented code, initializing agents as classes and defining their relationships programmatically. This approach makes it easier to connect them to existing enterprise backends. While LangGraph is optimized for Python and TypeScript developers who want deep control over graph traversal, Google ADK is designed for teams building multi-language applications that deploy natively to Vertex AI and require isolated execution environments.

## Why Stateful Graphs Differ from Modular Class-Based Code

The primary difference between LangGraph and Google ADK lies in how they structure agent interactions. LangGraph enforces a strict stateful directed graph model, whereas Google ADK allows developers to build modular, class-based agent architectures.

To orchestrate agents in LangGraph, developers define a centralized state schema, register node functions that modify this state, and establish edges that control execution flow. Nodes represent computing steps, while conditional edges determine the path based on node outputs. When a node function completes execution in LangGraph, it returns a dictionary containing the state keys to update. LangGraph automatically merges these updates into the shared graph state. Developers can customize how updates are merged by defining annotation reducers, such as using Python's operator.add to append values to a list rather than overwriting it. This design ensures that every step in the graph traversal has a clear, deterministic interface. The entry and exit points are marked by the START and END constants. Below is a standard Python implementation of a LangGraph workflow:

```python
from typing import TypedDict
from langgraph.graph import StateGraph, START, END

### Define the state schema
class AgentState(TypedDict):
    query: str
    response: str

### Define a node function that processes the state
def call_model(state: AgentState):
    return {"response": "Model output for " + state["query"]}

### Construct the graph structure
builder = StateGraph(AgentState)
builder.add_node("agent", call_model)
builder.add_edge(START, "agent")
builder.add_edge("agent", END)
graph = builder.compile()
```

In Google ADK, agents are built as standard Python objects without a wrapping graph container. You define agents programmatically, giving them instructions, models, and tools. Communication between agents occurs via standard function calls and delegation, treating agent interactions like standard method calls. This allows specialized agents to delegate work to one another dynamically without a preconfigured edge list. Below is a standard Python implementation of a Google ADK agent:

```python
from google.adk.agents import Agent

### Define a custom tool function
def calculate_metrics(data: str) -> dict:
    """Processes raw data to extract analytical metrics."""
    return {"status": "success", "processed_data": data}

### Initialize the agent
root_agent = Agent(
    model="gemini-2.0-flash",
    name="root_agent",
    description="Processes and analyzes raw metrics.",
    instruction="You are a helpful analyst that processes raw metrics using tools.",
    tools=[calculate_metrics],
)
```

Understanding these differences helps teams match the framework to their development practices:

* **Orchestration Pattern.** LangGraph enforces stateful directed cyclic graphs (DAGs) with nodes and conditional edges. Google ADK operates on a code-first, object-oriented model where agents are defined as classes and function as standard software modules.

* **Supported Languages.** LangGraph officially supports Python and JavaScript/TypeScript. Google ADK provides SDKs for Python, TypeScript, Go, Java, and Kotlin, making it highly polyglot-friendly.

* **State Management.** LangGraph maintains a centralized graph state that changes at each step, persisting via checkpointers. Google ADK relies on distributed state or delegates context persistence to the hosting runtime.

* **Hosting Platforms.** LangGraph runs on LangGraph Cloud or custom infrastructure using a Postgres checkpointer. Google ADK connects to Vertex AI Agent Engine.

* **Execution Security.** LangGraph runs inside your own application process or Docker containers. Google ADK uses GKE Agent Sandbox powered by gVisor for secure, isolated execution of untrusted agent code.

## How LangGraph and Google ADK Manage State and Memory

Maintaining state across multiple turns is essential for complex multi-agent interactions. The two frameworks handle memory and state persistence using very different methodologies.

LangGraph features a built-in checkpointing system designed to preserve graph state at each superstep. This provides developers with granular control over history and state transitions. LangGraph supports several checkpointer backends:

* **MemorySaver.** An in-memory checkpointer used during local testing and development. Data is lost upon container restart.

* **SqliteSaver.** A file-based checkpointer suitable for single-process operations or lightweight deployments.

* **PostgresSaver.** A database checkpointer designed for production-grade, horizontally scalable applications.

* **RedisSaver.** A high-throughput, low-latency checkpointing backend for concurrent sessions.

By checkpointing state at every step, LangGraph enables fault tolerance and time-travel debugging. Time-travel debugging allows developers to query the history of a thread, inspect the state at a specific superstep, and even fork execution from that point by writing new values to the state database. This is valuable during development to reproduce errors by replaying the exact path an agent took. Checkpointing also allows developers to pause execution, wait for manual human verification, and resume the graph run without losing state.

Google ADK does not enforce a centralized state dictionary in its local SDK. Instead, state persistence is handled by the deployment platform. When deployed to Vertex AI Agent Engine, the platform manages session context, conversation history, and variable bindings automatically. Developers can focus on writing agent logic, shifting database scaling and connection pooling to the Google Cloud Platform infrastructure. This reduces boilerplate but limits custom database schemas at the framework level.

## How GKE Sandbox and gVisor Secure Untrusted Agent Code

AI agents frequently generate and execute dynamic code to perform tasks like math, data analysis, and file formatting. Executing untrusted code in a production environment introduces severe security risks, including host system compromise, unauthorized database access, and environment variable exposure.

Google ADK solves this by connecting to GKE Agent Sandbox, which uses gVisor. gVisor implements a kernel-level virtualization model. By running a user-space kernel (called Sentry) that intercepts system calls made by the agent container, it prevents the containerized process from directly accessing the host's operating system kernel. When an ADK agent invokes a tool to run dynamic code, the code executes inside a gVisor sandbox. gVisor intercepts all system calls, preventing untrusted processes from interacting directly with the host node or scanning local networks. This creates a secure, isolated runtime for agents executing dynamic Python scripts. This sandbox execution is native to GKE Agent Sandbox, making it easy to secure enterprise ADK setups.

LangGraph, by comparison, does not provide a built-in execution sandbox. Developers are responsible for securing tool execution. If an agent built with LangGraph needs to execute code, the developer must manually spin up containerized runtimes, configure network access policies, and manage execution lifecycles. This makes Google ADK the more complete solution for applications requiring secure, dynamic code execution out of the box.

## Deploying and Scaling: LangGraph Cloud vs. Vertex AI Agent Engine

Moving from local prototyping to a production deployment requires hosting infrastructure that can scale to handle concurrent requests, manage credentials securely, and monitor agent performance.

LangGraph agents are typically deployed to LangGraph Cloud. LangGraph Cloud is a managed environment built specifically to support graph state persistence. It manages Postgres connection pools, schedules background graph runs, and hosts execution queues. It also provides built-in support for managing thread-level data, handling concurrent user requests, and resolving double-texting scenarios where a user sends new input while the graph is still running.

Google ADK agents are hosted on Vertex AI Agent Engine. This serverless platform automates container building, deployment, and scaling for ADK agents. It connects directly with Google Cloud IAM for access controls, Cloud Logging for tracking agent execution, and Cloud Monitoring for performance analytics. Vertex AI Agent Engine acts as an API gateway, routing requests to the appropriate agent containers while handling security and horizontal scaling. This makes it highly suitable for enterprises already operating on Google Cloud Platform.

## The Workspace Substrate: Multi-Agent Collaboration and Handoff

When multiple agents built with LangGraph or Google ADK collaborate on files, they require a shared storage layer to exchange assets and document states. Traditionally, developers use local directories or standard AWS S3 buckets to store intermediate assets. Some teams rely on general-purpose cloud storage like [Google Drive](/alternatives/google-drive/) or [Dropbox](/alternatives/dropbox/). However, general cloud storage is designed for human file sync and suffers from sync latency and api rate limits under high-frequency agent writes.

An intelligent workspace like Fastio [shared workspaces](/product/workspaces/) resolves these issues by acting as a direct-access, agent-friendly substrate. Fastio runs on cloud infrastructure partners, including Google Cloud Platform and Cloudflare, that are certified to industry-leading security standards. Within the workspace, it protects files using granular permissions at the organization, workspace, folder, and file level, encryption in transit and at rest, expiring share links, and an append-only audit log.

Instead of syncing files through local daemons, agents connect directly using the Fastio MCP Server. The server runs over Streamable HTTP and is available at:

```text
https://mcp.fast.io/mcp/key
```

Developers configure their agents to access this server by passing their API key in the headers. For example, in a Cline workspace configuration, the connection is declared in `cline_mcp_settings.json`:

```json
{
  "mcpServers": {
    "fastio": {
      "url": "https://mcp.fast.io/mcp/key",
      "headers": {
        "Authorization": "Bearer YOUR_FASTIO_API_KEY"
      }
    }
  }
}
```

This shared substrate supports multi-agent coordination through several native capabilities:

* **Intelligence Mode.** Auto-indexes workspace documents on arrival. Agents can perform semantic search and retrieve citation-backed summaries directly through the MCP server.

* **Version History.** Tracks a complete, per-file history. If a researcher agent and a writer agent edit the same file simultaneously, Fastio preserves all edits as distinct versions. This avoids race conditions without requiring application-level locks.

* **Coordination Rooms.** Shareable workspaces where humans and agents post messages and exchange files. Agents can monitor the activity log using the workspace long-poll at `GET /current/activity/poll/{entity_id}` or use webhooks for events like `room.message.created` to react to new inputs.

* **Metadata Views.** Turn raw files into structured data. Unlike unstructured semantic search, Metadata Views serve as the structured extraction layer. Developers or agents describe the fields they need extracted in natural language. The AI designs a typed schema supporting Text, Integer, Decimal, Boolean, URL, JSON, and Date & Time. It matches files (PDFs, images, Word docs, spreadsheets, presentations) and populates a spreadsheet grid. Agents can query these grids via the MCP server to trigger downstream operations. For more details on structured extraction, refer to the [Metadata Views](/product/document-data-extraction/) product documentation.

* **Ownership Transfer.** Allows agents to build workspaces and configure schemas, then transfer ownership to human clients. Every organization starts with a 14-day free trial, which requires a credit card. Starter is $29/mo. Business is $99/mo. Growth is $299/mo. Creating an account is free; doing real work requires an organization on a paid subscription.

## Frequently asked questions

### What is Google ADK?

The Google Agent Development Kit (ADK) is an open-source, code-first framework for building, testing, and deploying AI agents. It supports multiple programming languages, including Python, TypeScript, Go, Java, and Kotlin, and connects natively to Vertex AI Agent Engine.

### How does Google Agent Development Kit compare to LangGraph?

LangGraph organizes agents using stateful directed cyclic graphs (DAGs) with explicit nodes and edges. Google ADK uses a code-first, modular approach where agents communicate programmatically like standard software components without a rigid graph envelope.

### Can I use LangGraph with Gemini models?

Yes, LangGraph can be used with Gemini models by using LangChain's ChatGoogleGenerativeAI or Vertex AI chat model wrappers, allowing you to build graph-based agents powered by Gemini.

## About Fast.io

Fast.io provides shared workspaces where people and AI agents work on the same files, with built-in semantic search and citation-backed chat over what they hold. Agents reach it through a remote MCP server at https://mcp.fast.io/mcp, a REST API at https://api.fast.io/current/, and a command line client published on npm as @vividengine/fastio-cli.