Google ADK vs LangGraph

Detailed side-by-side comparison to help you choose the right tool

Google ADK

πŸ”΄Developer

Agent framework

Google’s development kit for constructing, evaluating, and deploying modular AI agents.

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Starting Price

Free

LangGraph

πŸ”΄Developer

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

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Starting Price

Free

Feature Comparison

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FeatureGoogle ADKLangGraph
CategoryAgent frameworkAI agent framework
Pricing Plans4 tiers8 tiers
Starting PriceFreeFree
Key Features
  • β€’ Multi-language SDKs: Python 2.0 Beta, TypeScript 1.0, Go, and Java
  • β€’ LLM agents, sequential, parallel, loop, and custom workflow agents
  • β€’ Built-in evaluation framework with criteria, user simulation, and environment simulation
  • β€’ Graph-based workflow orchestration
  • β€’ Deterministic state machine execution
  • β€’ Human-in-the-loop workflows

πŸ’‘ Our Take

Choose Google ADK if you prefer opinionated workflow primitives (sequential, parallel, loop), built-in evaluation, and a local debugging UI shipped out of the box. Choose LangGraph if you need maximum flexibility, a larger ecosystem of integrations, and prefer graph-based agent orchestration with LangChain compatibility.

Google ADK - Pros & Cons

Pros

  • βœ“Code-first abstractions support versioning and testable agent behavior
  • βœ“Evaluation and deployment workflows address more than prompt prototyping
  • βœ“Natural fit for teams already using Gemini and Google Cloud

Cons

  • βœ—Requires software engineering skills and operational ownership
  • βœ—End-to-end cost depends on separate model and cloud services
  • βœ—Deep Google integration may reduce portability to other stacks

LangGraph - Pros & Cons

Pros

  • βœ“Open-source library is MIT-licensed and runs anywhere without platform lock-in
  • βœ“Native checkpointing makes durable, resumable, human-in-the-loop agents straightforward
  • βœ“First-class multi-agent patterns: supervisor, hierarchical, sequential, parallel branches
  • βœ“Tight integration with LangSmith for production observability, evaluations, and replays
  • βœ“Active maintenance from the LangChain team with frequent releases and strong community

Cons

  • βœ—More verbose than LangChain for simple agents β€” explicit state schemas and edge functions add overhead
  • βœ—LangSmith trace pricing ($2.50/1k base traces) is a real cost at production scale
  • βœ—LCU + deployment-minute billing makes pricing harder to predict than seat-only competitors
  • βœ—Steeper learning curve than role-based frameworks like CrewAI for newcomers
  • βœ—Best documented in Python; JavaScript SDK exists but lags in features

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πŸ”’ Security & Compliance Comparison

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Security FeatureGoogle ADKLangGraph
SOC2β€”βœ… Yes
GDPRβ€”βœ… Yes
HIPAAβ€”β€”
SSOβ€”βœ… Yes
Self-Hostedβ€”πŸ”€ Hybrid
On-Premβ€”βœ… Yes
RBACβ€”βœ… Yes
Audit Logβ€”βœ… Yes
Open Sourceβ€”βœ… Yes
API Key Authβ€”βœ… Yes
Encryption at Restβ€”βœ… Yes
Encryption in Transitβ€”βœ… Yes
Data Residencyβ€”β€”
Data Retentionβ€”configurable
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