GraphN vs Google ADK and Agent Platform: SDK composition or integrated workflow plane?
- Published
- August 8, 2026
- Reading time
- 12 min
Decision summary
Choose Google ADK plus Agent Platform when
Gemini and Google Cloud are strategic, IAM and VPC controls should define the agent boundary, and developers want framework code with managed sessions, memory, evaluation, and cloud operations.
Choose GraphN when
one platform should own visual and YAML workflows, workspace tools and secrets, isolated Python functions, retrieval, files, and model serving without organizing the model boundary around one hyperscaler.
Google ADK is an open-source agent-development toolkit. Google's managed production surface—historically searched as Vertex AI Agent Engine—is documented in 2026 under Gemini Enterprise Agent Platform, with Agent Runtime, Sessions, Memory Bank, IAM, and observability. GraphN integrates workflow authoring, managed execution, model infrastructure, knowledge, files, secrets, and isolated functions in one workspace.
Names matter in this comparison. ADK is the framework used to define and run agents. Agent Platform is the Google Cloud service family that deploys, scales, secures, and observes them. Current Google Cloud documentation still uses
/agent-engine/ URLs, so older material and search queries often say “Vertex AI Agent Engine,” while the pages now describe Gemini Enterprise Agent Platform and Agent Runtime.GraphN starts from a different unit: the workflow and all linked resources are managed together. Teams can build the same graph in a visual canvas or YAML DSL, publish it, and invoke it through a workspace endpoint.
Note: This is a primary-documentation comparison, not a latency, quality, or cost benchmark. Google Cloud service names, regional support, and preview labels are version-sensitive.
Compare complete stacks
Scroll horizontally to compare
| Decision layer | Google ADK + Agent Platform | GraphN |
|---|---|---|
| Authoring | Framework code and ADK agent patterns | Canvas and versioned YAML |
| Execution | ADK Runner locally; managed Agent Runtime in Google Cloud | Managed workflow runtime with sync and async modes |
| Session state | SessionService with in-memory, database, or managed backends | Execution state and workspace-scoped run history |
| Long-term memory | MemoryService; managed Memory Bank or RAG memory | Knowledge bases for retrieval; storage for files |
| Tools | ADK tools, MCP, Google services, application code | MCP servers, connectors, isolated functions |
| Models | Gemini-first integrations plus model connectors | Built-in open models, imported endpoints, custom weights |
| Identity | Google Cloud IAM, agent identity, IAM Conditions | Workspace membership and workspace API keys |
| Operations | Cloud Logging, Monitoring, Trace, managed releases | Integrated executions, traces, logs, and resource versions |
ADK by itself is closer to LangGraph or Agent Framework than to the complete GraphN product. Agent Platform closes much of that operational gap, but it does so through Google Cloud services.
Orchestration model
ADK provides agent types and composition patterns such as sequential, parallel, and loop agents, plus a Runner that coordinates model calls, tools, events, and session updates. Because definitions are code, developers can introduce custom agents, callbacks, plugins, and tool logic in the same repository.
GraphN workflows use supported step types: agents, functions, MCP tools, connectors, conditionals, bounded loops, parallel analyzers, handoff routers, and judge loops. Expressions connect typed input and prior outputs. The graph's constrained vocabulary is what makes visual editing and YAML editing operate on the same artifact.
This is a recurring platform trade:
- ADK favors language-level extensibility.
- GraphN favors a resource definition that multiple authoring surfaces and the managed runtime can validate consistently.
A workflow whose control flow is deeply coupled to application classes will fit ADK. A workflow that product teams need to inspect, clone as a blueprint, edit visually, and publish without rebuilding an application container will fit GraphN.
Sessions, state, and memory
Google's documentation is unusually clear about state boundaries. An ADK Session represents one conversation thread and contains events plus temporary state. A
SessionService can be in-memory for development, database-backed for self-managed persistence, or backed by Agent Platform.ADK state guidance recommends updating state through context objects or appended events so deltas are tracked, thread-safe, and persisted by the service. Direct mutation outside that lifecycle can bypass persistence.
ADK MemoryService is separate. It provides searchable knowledge across sessions. Managed options include Memory Bank, which extracts and stores longer-term memories, and a RAG memory service for transcript-style vector retrieval.
GraphN separates execution, retrieval, and files differently:
- an execution has status, output or error, and a trace;
- a knowledge base stores embedded documents and ranked chunks;
- storage holds source and generated files;
- published resources are versioned snapshots.
GraphN does not currently document a direct equivalent to ADK Memory Bank's cross-session extracted memories. If personalized long-term memory is a core requirement, Google has the more explicit managed primitive. If the requirement is governed retrieval over organizational documents and multimodal assets, GraphN's workspace knowledge base is the closer comparison.
Tool execution and code isolation
ADK tools are functions or integrations invoked by an agent. Where custom tool code runs depends on deployment: locally with the application, in an Agent Runtime container, in Cloud Run or another service, or behind a remote protocol such as MCP. Agent Platform supplies managed runtime isolation and can connect Google services through IAM.
GraphN offers MCP tools and workspace Python functions as distinct primitives. Functions use a managed isolated environment and an SDK to access models, storage, knowledge bases, and media services.
That GraphN boundary is useful for short, user-authored workflow logic. ADK's application-container model is useful when the agent needs a broad dependency tree, custom server behavior, or a long-lived process.
Neither eliminates tool security work. Validate:
- whether a tool acts as the end user or as the deployed agent;
- network egress and private-service access;
- per-session filesystem separation;
- credential refresh and revocation;
- idempotency when an event or model step is retried.
Managed runtime and deployment
Gemini Enterprise Agent Platform describes a fully managed Agent Runtime, release management, sessions, Memory Bank, evaluation, and built-in observability. Google Cloud owns serverless scaling while the customer configures the project, region, service accounts, IAM, networking, quotas, and dependencies.
ADK also remains usable outside Agent Platform. That portability is valuable: an application can run locally or on another container platform. It also means teams must decide which ADK services stay in-memory, which become databases, and how operational capabilities are supplied when they leave Google Cloud.
GraphN is not a general container target. Teams publish workflows and linked resources, then use the documented synchronous or asynchronous execution modes. This is more opinionated and less infrastructure-shaped from the workflow author's perspective.
Choose the operating model explicitly:
The diagram compares customer-visible interfaces, not implementation topology.
Observability and evaluation
Agent Platform integrates Cloud Logging, Cloud Monitoring, and Cloud Trace. Google documents built-in and custom metrics, tracing, alerts, and agent evaluation. This is attractive when SRE teams already operate those systems and need agent telemetry beside the rest of the application.
GraphN provides execution traces with model/tool/function spans, timing, token usage, resource references, and per-node results where available. The workflow version and resource snapshot are part of the platform lifecycle, which helps connect an execution to what was published.
Compare the workflows around telemetry:
Scroll horizontally to compare
| Question | Why it matters |
|---|---|
| Can traces be exported through OpenTelemetry? | Central operations may not use the product UI. |
| Are prompts and files redacted independently? | Agent traces can contain customer data. |
| Can an evaluator run against a version before promotion? | Debugging after deployment is too late. |
| Do retries appear as new attempts or duplicate spans? | Operators need the real execution story. |
| Can cost and quota be attributed by workspace, project, model, and tool? | Platform teams need chargeback and abuse controls. |
Google's advantage is integration with a mature cloud observability plane. GraphN's advantage is a product-level trace tied to the same workflow resources the builder edits.
Models and inference
ADK can connect to multiple model providers, but its most integrated path is Gemini through Google Cloud. Agent Platform can use Google model endpoints and surrounding Vertex AI controls. Teams already standardized on Gemini gain one vendor path for model, runtime, data, identity, and operations.
GraphN's models layer is broader in a different direction. Workspaces can select built-in chat and vision models, register external OpenAI-compatible APIs, and import weights from Hugging Face or S3. GraphN manages open-weight inference and supports specialized embedding, reranking, and text-to-speech services.
The decision is not simply “multi-model.” Ask who operates the GPUs and model lifecycle:
- With ADK, model calls can go anywhere, but the team or provider owns those endpoints.
- With Agent Platform, Google Cloud is the natural managed model plane.
- With GraphN, open-weight and custom model serving can be part of the same platform as the workflow.
Knowledge and data boundaries
Google Cloud offers several retrieval paths: Vertex AI Search, RAG services, data stores, BigQuery, Cloud Storage, and Memory Bank. Each service can be governed through projects, IAM, regions, and network controls. That composability is powerful for organizations whose data already lives in Google Cloud.
GraphN provides a narrower integrated abstraction: knowledge bases are searchable collections attached to agents and functions; storage is workspace object storage. A multimodal knowledge base can index text and images and select an embedding model at creation time.
Prefer Google's approach when existing data services and IAM policies are the center of gravity. Prefer GraphN's approach when workflow teams need a direct ingestion-to-retrieval path without designing a collection of cloud resources first.
IAM, secrets, and agent identity
Agent Platform supports IAM agent identity and fine-grained control over Sessions and Memory Bank with IAM Conditions. VPC Service Controls, customer-managed encryption keys, data residency, and other controls vary by service and region; consult the current capability matrix rather than applying one platform-wide label.
GraphN scopes resources and API keys by workspace. Secrets can back agents, MCP servers, imported models, and integrations without placing credentials in workflow instructions. This is simpler for product teams, while Google IAM offers more direct integration with enterprise cloud identities.
For both, trace a request end to end:
- authenticate the caller;
- authorize access to the workflow or agent;
- propagate user or service identity to tools;
- retrieve the correct secret or delegated token;
- record the principal in audit and trace data.
Migration boundaries
ADK to GraphN
- Inventory agents, sub-agents, tools, callbacks, plugins, session state, and MemoryService usage.
- Map explicit sequential, parallel, loop, and handoff behavior to GraphN step types.
- Move bounded Python tools to functions or expose services through MCP.
- Do not map session or Memory Bank records into a knowledge base without a data-model review.
- Register models, secrets, knowledge, and storage as workspace resources.
- Replay representative sessions and compare state updates, tool side effects, and structured output.
GraphN to ADK and Agent Platform
- Export workflow YAML and linked resource specifications.
- Rebuild agents and control flow in ADK code.
- Choose SessionService and MemoryService implementations.
- Replace functions and MCP resources with ADK tools or remote services.
- Map storage and knowledge to Google Cloud data services.
- Deploy to Agent Runtime, configure IAM agent identity, and connect tracing and evaluation.
Choose Google ADK and Agent Platform when
- Google Cloud IAM, networking, logging, monitoring, and data services are established standards.
- Gemini is the primary model family and tight Vertex AI integration matters.
- Developers want framework code and custom agent classes in supported languages.
- Managed Sessions and Memory Bank match the application's conversation and personalization model.
- The team wants a serverless agent runtime but still values the option to run ADK elsewhere.
Choose GraphN when
- A canvas, YAML, CLI, and API must operate on one workflow definition.
- Agents, MCP servers, secrets, functions, models, knowledge, and files should be workspace resources.
- Open-weight inference and custom model deployment are part of the agent-platform requirement.
- Bounded Python tools need managed isolated function execution.
- The platform should not be anchored to a Google Cloud project and its service graph.
A useful proof of concept
Build a customer-research workflow that:
- accepts text and an image;
- retrieves private documents;
- keeps one conversation session;
- invokes a custom Python risk rule;
- pauses for approval;
- calls a delegated external API;
- emits a structured result.
Then test a process restart, concurrent updates to one session, IAM/workspace revocation, memory deletion, model replacement, and trace export. The winning design is the one whose state and ownership boundaries your team can explain before an incident.
Use the GraphN platform lifecycle, concepts guide, models reference, and FAQ to verify GraphN's current public contract.
Related comparisons
- GraphN vs Amazon Bedrock AgentCore and Strands Agents — the analogous AWS framework-and-runtime stack
- GraphN vs Microsoft Agent Framework — Microsoft's successor SDK and Foundry hosting
- GraphN vs LangGraph — checkpointed graph state and modular deployment
Primary sources
- Google ADK session documentation
Defines ADK sessions, events, and the SessionService boundary used in the state comparison.
- Google ADK state documentation
Documents ADK state scopes and mutation semantics discussed in the workflow-state section.
- Google ADK memory documentation
Describes ADK memory services and the distinction between session state and long-term memory.
- Gemini Enterprise Agent Platform overview
Defines the managed Agent Runtime, Sessions, Memory Bank, IAM, and operational product boundary.
- Google Agent Platform tracing
Documents the managed tracing and observability capabilities evaluated in this comparison.
- GraphN core concepts
Defines GraphN workspaces, resources, publishing, and execution concepts.
- GraphN models reference
Documents GraphN's built-in, imported, and custom model options.