Google ADK, AWS Strands Agents and LangGraph are now the three most common shortlists for enterprise agent projects. All three are open source, model-agnostic in principle and support both MCP and A2A.
They differ in where they come from, how they model control flow, which languages they ship and which managed runtime they pull you toward. The goal is not the trendiest framework. It is a choice your team will not need to reverse in 12 months.
None wins everywhere. Start from your cloud, your production language and your compliance needs.
Choose it if you run on Google Cloud, use Gemini, or need agents in Go, Java or Kotlin. It ships five native SDKs with MCP and A2A in each, and is the only one of the three with first-party Android on-device support. Expect some churn from the 2.0 migration.
Choose it if you are on AWS and want Bedrock models with IAM-native auth. Its model-driven loop keeps code minimal: a model, a prompt and tools. Pair it with AgentCore for per-session microVM isolation and sessions up to 8 hours.
Choose it if you need explicit, auditable control flow, durable checkpoints and replay, or the widest model catalog. It is the most cloud-neutral of the three, with LangSmith providing the deepest first-party observability and evaluation.
See how ADK, Strands and LangGraph differ on control flow, models, orchestration, protocols, runtimes, evals and licensing.
Use MCP for tools and A2A for agent boundaries so a framework change does not mean a rewrite.
Match Agent Runtime, Bedrock AgentCore, LangSmith Deployment or Kubernetes to your cloud, identity and compliance needs.
Build the same agent in two or three frameworks, run trajectory evals in CI and score against eight weighted criteria.
Google's Agent Development Kit is an open-source, Apache 2.0 framework for building, evaluating and deploying AI agents. It ships native SDKs for Python, Go, TypeScript, Java and Kotlin, each with an MCP client and A2A support. ADK 2.0 added a graph-based Workflow Runtime.
No. ADK is optimized for Gemini, but the Python SDK includes adapters for Anthropic, OpenAI and LiteLLM. Coverage is narrower in other languages, so test tool calling, streaming and structured output with your exact model first.
ADK is a higher-level, code-first framework tuned for Google Cloud, with five language SDKs. LangGraph is a lower-level, cloud-neutral state-graph runtime in Python and JavaScript, centered on explicit state, checkpoints and replay.
AWS's open-source, Apache 2.0 agent SDK for Python and TypeScript. You supply a model, a prompt and tools, and the model plans the steps. Version 1.0 added agents-as-tools, swarms, graphs and A2A. Bedrock is the default provider, with Anthropic, OpenAI, Google and others supported.
MCP connects an agent to tools, data sources and APIs through a standard interface. A2A lets agents discover each other, delegate tasks and stream results across frameworks. Both are Linux Foundation projects, and most production systems use both.
Yes. AWS documents AgentCore as framework-agnostic and names LangGraph, Google ADK, CrewAI, LlamaIndex, OpenAI Agents SDK and Strands as supported.
None wins everywhere. ADK fits Google Cloud and polyglot teams, Strands fits AWS, LangGraph fits teams that need auditable control flow across clouds, and Microsoft Agent Framework fits .NET and Azure. Run a two-week bake-off with evals and tracing before deciding.
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