Google ADK vs LangChain: Which Agent Framework to Use?

Paradigm

ADK is opinionated about agent topology: you build a tree of LlmAgent nodes with sub_agents, then orchestrate them with SequentialAgent, ParallelAgent, and LoopAgent workflow primitives. LangChain is opinionated about composition: AgentExecutor runs a single ReAct-style loop over a @tool registry, and LangGraph adds typed state channels when you need branching.

Where ADK assumes hierarchical delegation as the default, LangChain assumes a flat agent with pluggable parts — PromptTemplate, OutputParser, ConversationBufferMemory — that you wire together yourself.

Ecosystem

LangChain has the larger surface area: 132k stars since 2022, thousands of integrations (document loaders, vector stores, embeddings), and LangSmith for tracing. Provider-agnostic by design — swapping OpenAI for Anthropic is a class change.

ADK is younger (April 2025, 18k stars) and leans toward Gemini and Vertex AI. Other providers work, but the deployment story (Vertex AI Agent Engine, Cloud Run, bidirectional audio/video, Session/State services) is built for Google Cloud customers. The integration catalog is thinner; the cloud integration is deeper.

Use case

Reach for ADK when the system is multi-agent by design — a root coordinator delegating to specialized children, parallel fan-out via ParallelAgent, and managed deployment on Vertex AI.

Reach for LangChain when the agent itself is simple but the integration surface is wide: RAG over a specific vector store, multi-provider support, and LangSmith observability. ADK optimizes for orchestration topology; LangChain optimizes for connector breadth.

Pick google-adk if your project lives or dies on multi-agent orchestration deployed on Google Cloud.

• Hierarchical delegation is the architecture: You actually need a root LlmAgent routing to specialized sub_agents, not just a single agent with a few tools. ADK's tree model and Runner.run() dispatch are the point.
• Vertex AI is your deployment target: You want managed scaling, Agent Engine, and built-in streaming with bidirectional audio/video. The Gemini and Google Cloud coupling is a feature, not a tax.
• Workflow primitives as first-class units: SequentialAgent, ParallelAgent, and LoopAgent map directly onto how your team thinks about pipelines, and you want them composable rather than encoded as ad-hoc function calls.

Pick langchain if your project lives or dies on stitching many integrations together across providers.

• Provider-agnostic by requirement: You need to swap OpenAI, Anthropic, Cohere, or local models behind one interface, and you don't want each swap to touch business logic. LangChain's class hierarchy pays off here.
• RAG and connector breadth: Document loaders, text splitters, embedding models, and vector stores are core to the product. The integration catalog saves weeks of glue code.
• LangSmith and LangGraph are on the roadmap: You want production tracing, eval datasets, and conditional state-machine workflows with typed reducers — not just a single agent loop.

Both frameworks pull in a substantial dependency tree and a vocabulary your team has to learn before reading any agent code. LlmAgent plus Runner plus SequentialAgent is a different mental model from AgentExecutor plus @tool plus ConversationBufferMemory, but in both cases the actual LLM call sits behind several layers of indirection.

That indirection is the cost: stack traces get longer, debugging means stepping through framework internals, and version upgrades occasionally break public APIs. If your agent is a single loop with a handful of tools, you may be paying for orchestration and integrations you'll never call.