Google ADK vs LangChain vs CrewAI vs AutoGen
A comprehensive comparison of the major AI agent frameworks.
Feature Comparison
| Feature | Google ADK | LangChain | CrewAI | AutoGen |
|---|
| Creator | Google | LangChain Inc | CrewAI Inc | Microsoft |
| License | Apache 2.0 | MIT | MIT | MIT |
| Primary Model | Gemini | Any | OpenAI-focused | OpenAI + Azure |
| Model Support | 100+ via LiteLLM | 50+ integrations | Via LangChain | OpenAI, Azure, local |
| Languages | Python, Java, Go, TS | Python, JS | Python | Python, .NET |
| Multi-Agent | Native (Seq, Par, Loop) | Via LangGraph | Native (Crew, Tasks) | Native (GroupChat) |
| Tools | 60+ + MCP + OpenAPI | 100+ | LangChain tools | Custom functions |
| Evaluation | Built-in (7 metrics) | Via LangSmith ($) | Limited | Limited |
| Deployment | Vertex AI, Cloud Run, GKE | Self-managed | Self-managed | Self-managed |
| Streaming | Gemini Live (voice/video) | LLM streaming | No | No |
| A2A Protocol | Native | No | No | No |
| No-Code | Agent Config (YAML) | No | No | No |
| Sessions | Built-in (scoped state) | Via memory classes | Task-based | GroupChat memory |
| HITL | Built-in | Custom | Human input task | Human proxy |
Strengths & Weaknesses
Google ADK
| Strengths | Weaknesses |
|---|
| Best Gemini integration | Newer, smaller community |
| Built-in evaluation (7 metrics) | Some features Gemini-only |
| Multi-language (Python, Java, Go, TS) | Google Cloud-centric deployment |
| A2A protocol for inter-agent comms | Less third-party model polish |
| Managed deployment (Vertex AI) | Fewer tutorials/courses |
LangChain
| Strengths | Weaknesses |
|---|
| Largest ecosystem (100+ integrations) | Complex, steep learning curve |
| Most community resources | Frequent breaking changes |
| LangSmith for observability | LangSmith is paid |
| LangGraph for complex agents | Over-abstracted for simple tasks |
| Broadest model support | Heavy dependency chain |
CrewAI
| Strengths | Weaknesses |
|---|
| Simplest multi-agent API | Smaller tool ecosystem |
| Role-based agent design | OpenAI-focused |
| Easy to learn | Limited evaluation |
| Good for team simulations | Less deployment support |
AutoGen
| Strengths | Weaknesses |
|---|
| Microsoft/Azure integration | Azure-centric |
| GroupChat for multi-agent | Less flexible orchestration |
| Code execution built-in | Smaller community |
| .NET support | Fewer integrations |
When to Use What
| Use Case | Recommended |
|---|
| Gemini-first development | Google ADK |
| Rapid prototyping with any model | LangChain |
| Simple multi-agent teams | CrewAI |
| Microsoft/Azure ecosystem | AutoGen |
| Production evaluation needed | Google ADK |
| Maximum tool ecosystem | LangChain |
| Enterprise with managed deployment | Google ADK |
Code Comparison
Google ADK
from google.adk.agents import Agent
agent = Agent(
name="assistant",
model="gemini-2.5-flash",
instruction="Help users with tasks.",
tools=[search, calculate],
)
LangChain
from langchain.agents import create_tool_calling_agent
from langchain_openai import ChatOpenAI
agent = create_tool_calling_agent(
ChatOpenAI(model="gpt-4o"),
tools=[search, calculate],
prompt=prompt_template,
)
CrewAI
from crewai import Agent, Task, Crew
agent = Agent(
role="Research Analyst",
goal="Find information",
tools=[search, calculate],
)
crew = Crew(agents=[agent], tasks=[task])
AutoGen
from autogen import AssistantAgent
agent = AssistantAgent(
name="assistant",
llm_config={"model": "gpt-4o"},
)
Learn more at Google ADK, LangChain, CrewAI, and AutoGen.