Google Agent Development Kit vs LangGraph
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Google Agent Development Kit | LangGraph |
|---|---|---|
| Pricing | Free (open-source) | Free (MIT open-source) |
| Primary Use Case | Enterprise multi-agent systems with deterministic orchestration & Google Cloud integration | Stateful, low-level agent workflows with human-in-the-loop & custom architectures |
| Language Support | Python, TypeScript, Go, Java, Kotlin (multi-language SDKs) | Python (primary); JavaScript supported via LangChain |
| Key Differentiator | Graph-based deterministic workflows + native Google Cloud/Gemini integration | Fine-grained control with human-in-the-loop, memory, and prompt caching |
| Latest News | ADK 2.0 GA (Nov 2025) – graph workflows, collaborative agents, Kotlin support | Prompt caching for Deep Agents (Jun 2026); memory guidance; Box AI case study |
| Best For | Enterprise teams using Google Cloud, needing multi-language agents & deterministic orchestration | Developers wanting full control over agent state, loops, and human oversight |
For enterprise teams already on Google Cloud needing deterministic multi-agent orchestration with multi-language SDKs, Google ADK is the clear pick. LangGraph wins when you need deep control over state, loops, and human-in-the-loop workflows. If you value low-level primitives and prompt caching (per latest updates), LangGraph edges ahead. Both are free, so choose based on required control vs. integrated cloud tooling.
Open-source framework to build, debug, and deploy production-grade AI agents.
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Open-source orchestration framework for building reliable, stateful AI agents with low-level control.
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Google ADK 2.0 (GA as of Nov 2025) introduces graph-based deterministic workflows and collaborative multi-agent orchestration. It uniquely offers multi-language SDKs (Python, TypeScript, Go, Java, Kotlin) and native integration with Gemini, Gemma, and Claude via Apigee AI Gateway. ADK provides built-in enterprise observability (logging, metrics, traces) and CLI tools for deployment to Cloud Run or GKE. LangGraph (MIT license) focuses on fine-grained control with low-level graph primitives (state, nodes, edges). Its key features include human-in-the-loop checks, built-in cross-session memory, token-by-token streaming, and fault tolerance (retries, timeouts). LangGraph's latest news (June 2026) highlights prompt caching for Deep Agents, reducing cost and latency, and guidance on adding memory. Unlike ADK's high-level orchestration, LangGraph lets you design custom loops and hierarchical architectures. ADK supports MCP and A2A protocols, while LangGraph leverages LangSmith for observability and deployment. Both are model-agnostic, but ADK has tighter Google model integration; LangGraph supports any LLM via providers. ADK’s deterministic graphs suit predictable workflows; LangGraph excels in dynamic, stateful applications.
Pricing compared
Both Google ADK and LangGraph are free and open-source. Google ADK is released under an open-source license (no specific license mentioned, but free to use) with no usage limits. LangGraph is MIT-licensed, also free. There are no premium tiers or paid add-ons for either framework. However, both require third-party services that may incur costs: ADK can leverage Google Cloud (Cloud Run, GKE, Apigee) which are pay-per-use; LangGraph can integrate with LangSmith (which has a free tier and paid plans for observability) and LLM APIs. The latest news does not mention any pricing changes. Therefore, the 'cost' is indirect, tied to infrastructure and model usage. For teams already on Google Cloud, ADK's deployment costs can be managed within existing budgets. LangGraph's integration with LangSmith may add cost for enterprise features. Both frameworks are affordable for startups and enterprises alike, but total cost of ownership depends on scale and chosen cloud providers.
Who should pick which
- Enterprise developer building a multi-agent system on Google CloudPick: Google Agent Development Kit
ADK offers deterministic graph workflows, multi-language SDKs, and native GCP integration (Cloud Run, GKE, Apigee), plus support for Google models and enterprise monitoring.
- Developer needing custom, stateful agent loops with human oversightPick: LangGraph
LangGraph provides low-level primitives for custom state management, human-in-the-loop checks, and memory, with recently added prompt caching for cost savings.
- Solo developer prototyping a multi-agent appPick: Google Agent Development Kit
ADK's multi-language support and CLI tools allow rapid prototyping, and its collaborative agents feature (new in ADK 2.0) helps build complex agents quickly.
- Team building a production agent with heavy human reviewPick: LangGraph
LangGraph's built-in human-in-the-loop checks and fault tolerance are ideal for workflows requiring moderation and retry logic.
- Startup wanting to integrate with multiple LLM providersPick: LangGraph
LangGraph is model-agnostic and works with any LLM provider, while ADK is more tied to Google's ecosystem (though it supports third-party models via LiteLLM).
Frequently Asked Questions
What is the main difference between Google ADK and LangGraph?
ADK focuses on high-level deterministic orchestration with multi-language SDKs and deep Google Cloud integration. LangGraph provides low-level graph primitives for custom stateful agents with human-in-the-loop and memory.
Which framework is better for multi-agent systems?
ADK 2.0 GA includes native multi-agent orchestration and collaborative agents, making it a strong choice. LangGraph also supports multi-agent but requires more manual setup.
Can I use LangGraph with Google models?
Yes, LangGraph supports Google models via LangChain integrations, but ADK has first-class support for Gemini/Gemma.
Is Google ADK production-ready?
Yes, ADK 2.0 GA (November 2025) includes enterprise features like observability, deployment to Cloud Run/GKE, and graph workflows for deterministic logic.
Does LangGraph have prompt caching?
Yes, as of June 2026, Deep Agents in LangGraph support prompt caching to reduce costs and latency.
Which framework is easier for beginners?
ADK's higher-level abstraction and CLI tools make it easier for beginners, especially those familiar with Google Cloud. LangGraph requires more understanding of graph concepts.
Are there any costs beyond the framework itself?
Both are free open-source. Costs come from LLM API usage and optional infrastructure (Google Cloud for ADK, LangSmith for LangGraph).
Which framework has better human-in-the-loop support?
LangGraph has built-in human-in-the-loop checks as a key feature, while ADK does not emphasize this in its features list.
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