What Sets Them Apart
The important 2026 distinction is no longer that LangGraph has graphs and Google ADK does not. Google ADK 2.0 now documents a graph-based Workflow Runtime with fan-out and fan-in, loops, retry, state management, dynamic nodes, human-in-the-loop, nested workflows, and task agents as workflow nodes. LangGraph still wins this comparison because graph orchestration is the core product rather than one runtime surface: its README positions it as a low-level framework for long-running, stateful agents with durable execution, human oversight, comprehensive memory, streaming, and deployment support. That makes LangGraph the safer default when the architecture must survive provider changes, partial failures, long-running state, and review gates across many workflows. ADK is compelling, but its strongest story is Google-native agent development rather than neutral orchestration ownership.
LangGraph and Google ADK at a Glance
LangGraph is the more mature orchestration layer for teams that already know they need explicit state. The current `langchain-ai/langgraph` repository shows 35,819 GitHub stars, 5,990 forks, an MIT license, and a fresh push on 2026-06-25. More important than the star count is the product shape: LangGraph exposes graph nodes, edges, checkpoints, interrupts, memory, streaming, time travel, and deployment patterns for agents that cannot be treated as a single request-response chain. If your agent needs approval before sending an email, needs to resume after a worker crash, or needs to preserve a thread-level memory record, LangGraph gives those controls as first-class design primitives instead of hiding them behind a managed agent loop.
Google ADK is the faster choice when the team wants to build inside Google's agent stack. The current `google/adk-python` repository shows 20,298 GitHub stars, 3,618 forks, an Apache-2.0 license, and a fresh push on 2026-06-26. Its README describes an open-source, code-first Python framework for building, evaluating, and deploying AI agents, and ADK 2.0 adds a graph-based Workflow Runtime plus a Task API for structured agent-to-agent delegation. ADK also gives Google-native surfaces around sessions, state, memory, evaluation, deployment, web interface, command line, API server, Cloud Run, GKE, and Vertex Gen AI evaluation. For a Gemini or Vertex-first organization, that integration can be more valuable than framework neutrality.
The winner depends on who owns the operating model. Pick LangGraph when platform teams want to own orchestration semantics across OpenAI, Anthropic, Gemini, local models, LangChain integrations, and custom runtimes. Pick Google ADK when the same team wants Google to provide the agent development kit, runtime shape, evaluation hooks, and deployment path around Gemini and Vertex services. In practical buyer terms, LangGraph is the stronger architecture decision for heterogeneous AI estates, while ADK is the stronger platform decision for teams already treating Google Cloud as the agent control plane. This is why LangGraph is the overall recommendation here, with a clear ADK exception for Google-native shops.
Runtime, State, and Human Control
Runtime control is where LangGraph earns the recommendation. Its durable execution model is built for workflows that persist through failures and resume from the exact point where they stopped. That matters for customer-support escalations, compliance reviews, research agents, coding agents, and procurement flows where a single long-running state machine may span multiple tool calls, people, and external systems. LangGraph interrupts let developers pause the graph, expose JSON-serializable state to a reviewer, collect a decision, and continue with a thread ID and checkpointer. ADK has sessions, state, memory, callbacks, and human-in-the-loop workflow support, but LangGraph's design makes review points and restart behavior feel like part of the core runtime contract.
ADK has narrowed the architecture gap with Workflow Runtime and Task API. The Workflow Runtime can compose agents, deterministic functions, branching, loops, retries, state management, and human input, while the Task API gives a structured way for agents to delegate multi-turn work to other agents. That is a strong answer to older critiques that ADK was only a simpler toolkit or a Gemini wrapper. The trade-off is that ADK's strongest abstractions are tied to Google's agent platform direction: Gemini examples, Agent Platform hosted models, Apigee AI Gateway, Cloud Run, GKE, Vertex evaluation, and Google-hosted runtime pages all sit close to the happy path. That is a benefit if those are your defaults and a constraint if neutrality is strategic.
For production governance, LangGraph is easier to reason about when auditability is the priority. A graph with explicit transitions, persisted state, and interrupt points gives reviewers a concrete place to ask why an agent moved from retrieval to tool execution to approval. LangSmith integration then adds tracing, evaluation, prompt/version visibility, and deployment choices around that graph. ADK's evaluation documentation and Vertex Gen AI Evaluation Service integration are valuable, especially for Google Cloud teams, but the governance story is more platform-shaped. If the organization already uses Vertex for evaluation and Cloud Run or GKE for deployment, ADK can be cleaner. If governance must survive a future move away from Google-hosted services, LangGraph is the safer foundation.
Deployment, Evaluation, and Ecosystem Fit
Deployment is the clearest platform split. LangGraph can be run as open-source Python or JavaScript/TypeScript libraries and then paired with LangSmith and LangGraph deployment options when a managed or self-hosted operational layer is needed. That gives teams a path from local graph code to observability, evaluation, and production APIs without forcing one model vendor. ADK points teams toward Google-run surfaces: ADK web interface, API server, Agent Runtime, Cloud Run, GKE, observability, logging, metrics, traces, evaluation criteria, custom metrics, and Vertex-backed evaluation. Both can reach production, but LangGraph optimizes for framework portability while ADK optimizes for Google Cloud alignment.
Ecosystem fit should decide the tie-breaker. LangGraph benefits from LangChain's broad integration map, LangGraph.js for TypeScript teams, LangSmith tracing across common providers, and a larger repository footprint at write time. ADK benefits from Google's velocity, Apache-2.0 licensing, a clear 2.0 roadmap around workflows and tasks, and a docs surface that covers sessions, memory, A2A protocol, MCP, models, runtime config, and deployment. A Python team building regulated, multi-provider workflows should start with LangGraph even if it later calls Gemini. A Google Cloud team building Gemini agents with Vertex evaluation, Cloud Run deployment, and ADK-native workflow tools should start with ADK rather than recreating Google integration glue in LangGraph.
The Bottom Line
LangGraph wins overall because the comparison is about long-term agent architecture, not just first-week SDK ergonomics. It has the stronger neutral orchestration story, the larger current GitHub footprint, explicit durable execution, first-class interrupts, memory, streaming, and a production path that does not require committing the agent runtime to one cloud provider. Google ADK is not a weak alternative: ADK 2.0's graph workflow runtime, Task API, sessions, memory, evaluation, and Google deployment surfaces make it the right answer for Gemini-first and Vertex-first teams. The practical recommendation is simple: choose LangGraph if you need portable stateful orchestration across providers; choose Google ADK if your agent roadmap is intentionally Google-native and the value of ADK runtime, evaluation, and deployment outweighs framework neutrality.