LangChain vs CrewAI vs AutoGen: Which Agent Framework Fits Your Stack

Building AI agents in 2025 means choosing a framework. The three leading options - LangChain, CrewAI, and AutoGen - take fundamentally different approaches to agent orchestration. We built the same research assistant application with all three to provide an informed comparison.

Quick verdict

Our recommendation: Start with LangChain for production applications requiring flexibility and extensive ecosystem support. Use CrewAI when your problem maps naturally to specialized agent roles. Consider AutoGen for research and experimental applications.

The test application

To provide a fair comparison, we built the same application three times: a research assistant that:

1. Takes a research question
2. Searches multiple sources (web, academic papers, internal knowledge base)
3. Synthesizes findings into a structured report
4. Cites sources with relevance scores

This tests core agent capabilities: tool use, multi-step reasoning, and output formatting.

LangChain

Overview

LangChain is the most mature and feature-complete framework. Originally focused on chains (sequential LLM calls), it has evolved into a comprehensive platform for building LLM applications with the LangGraph extension for agent orchestration.

Architecture approach

LangChain uses a component-based architecture where you compose applications from modular pieces:

import { ChatOpenAI } from '@langchain/openai';
import { createReactAgent } from '@langchain/langgraph/prebuilt';
import { TavilySearchResults } from '@langchain/community/tools/tavily_search';
import { tool } from '@langchain/core/tools';
import { z } from 'zod';

// Define custom tool
const searchKnowledgeBase = tool(
  async ({ query }) => {
    // Implementation
    return results;
  },
  {
    name: 'search_knowledge_base',
    description: 'Search internal knowledge base',
    schema: z.object({ query: z.string() })
  }
);

// Create agent
const agent = createReactAgent({
  llm: new ChatOpenAI({ model: 'gpt-4o' }),
  tools: [new TavilySearchResults(), searchKnowledgeBase]
});

// Execute
const result = await agent.invoke({
  messages: [{ role: 'user', content: 'Research quantum computing advances' }]
});

For complex workflows, LangGraph provides graph-based orchestration:

import { StateGraph, END } from '@langchain/langgraph';

const workflow = new StateGraph({
  channels: {
    query: { value: null },
    sources: { value: [] },
    synthesis: { value: null }
  }
})
  .addNode('search', searchNode)
  .addNode('synthesize', synthesizeNode)
  .addNode('format', formatNode)
  .addEdge('search', 'synthesize')
  .addEdge('synthesize', 'format')
  .addEdge('format', END);

const app = workflow.compile();

Strengths

Ecosystem depth: 500+ integrations covering vector stores, LLM providers, tools, and retrievers. Whatever you need, there's probably a LangChain integration.

Flexibility: Build anything from simple chains to complex multi-agent systems. The abstraction levels let you work at the right granularity.

Observability: LangSmith provides production-grade tracing, evaluation, and monitoring out of the box.

Documentation: Extensive docs, tutorials, and examples. Active community support.

Weaknesses

Complexity: The learning curve is steep. Simple tasks can require understanding multiple abstractions.

Boilerplate: Even straightforward agents need significant setup code.

Breaking changes: The framework evolves rapidly. Upgrades can require significant refactoring.

Overhead: Abstraction layers add latency and memory usage compared to direct API calls.

Test application results

Implementation time: 6 hours
Lines of code: 420
Response latency: 8.2s average
Success rate: 94%
Token efficiency: 12,400 avg tokens/query

CrewAI

Overview

CrewAI takes a role-based approach inspired by human team dynamics. You define agents with specific roles, goals, and tools, then let them collaborate on tasks.

Architecture approach

CrewAI organizes work around crews of specialized agents:

from crewai import Agent, Task, Crew, Process

# Define specialized agents
researcher = Agent(
    role='Research Analyst',
    goal='Find comprehensive, accurate information on topics',
    backstory='Expert researcher with access to multiple data sources',
    tools=[web_search, academic_search, knowledge_base],
    llm='gpt-4o'
)

synthesizer = Agent(
    role='Content Synthesizer',
    goal='Create clear, well-structured reports from research',
    backstory='Skilled writer who excels at organizing complex information',
    tools=[],
    llm='gpt-4o'
)

# Define tasks
research_task = Task(
    description='Research {topic} using all available sources',
    expected_output='Comprehensive research notes with citations',
    agent=researcher
)

synthesis_task = Task(
    description='Synthesize research into a structured report',
    expected_output='Well-formatted report with executive summary',
    agent=synthesizer,
    context=[research_task]  # Depends on research
)

# Create crew
crew = Crew(
    agents=[researcher, synthesizer],
    tasks=[research_task, synthesis_task],
    process=Process.sequential
)

# Execute
result = crew.kickoff(inputs={'topic': 'quantum computing advances'})

Strengths

Intuitive mental model: The team/role metaphor maps naturally to how humans think about dividing work.

Built-in collaboration: Agents can delegate to each other, share context, and build on each other's work.

Rapid prototyping: Get multi-agent systems running quickly with minimal boilerplate.

Clear responsibility: Each agent has defined scope, making debugging easier.

Weaknesses

Python only: No JavaScript/TypeScript SDK limits adoption for web-focused teams.

Limited tool ecosystem: Fewer pre-built integrations than LangChain. More custom tool development required.

Overhead for simple tasks: The role-based structure adds unnecessary complexity for single-agent applications.

Less control: The framework handles agent coordination, which can be frustrating when you need specific behaviour.

Test application results

Implementation time: 3 hours
Lines of code: 180
Response latency: 11.4s average
Success rate: 89%
Token efficiency: 18,600 avg tokens/query

CrewAI was fastest to implement but used more tokens due to inter-agent communication overhead.

AutoGen

Overview

AutoGen, developed by Microsoft Research, focuses on multi-agent conversations. Agents communicate through messages, enabling complex collaborative behaviours.

Architecture approach

AutoGen models agents as participants in conversations:

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

# Define agents
researcher = AssistantAgent(
    name='Researcher',
    system_message='''You are a research specialist. Search for information
    and share findings with the team.''',
    llm_config={'model': 'gpt-4o'}
)

analyst = AssistantAgent(
    name='Analyst',
    system_message='''You analyze research findings and identify key insights.
    Ask clarifying questions when needed.''',
    llm_config={'model': 'gpt-4o'}
)

writer = AssistantAgent(
    name='Writer',
    system_message='''You synthesize analysis into clear, structured reports.
    Request additional research if gaps exist.''',
    llm_config={'model': 'gpt-4o'}
)

# User proxy for tool execution
user_proxy = UserProxyAgent(
    name='User',
    human_input_mode='NEVER',
    code_execution_config={'work_dir': 'workspace'}
)

# Group chat for collaboration
group_chat = GroupChat(
    agents=[user_proxy, researcher, analyst, writer],
    messages=[],
    max_round=20
)

manager = GroupChatManager(groupchat=group_chat)

# Execute
user_proxy.initiate_chat(
    manager,
    message='Research quantum computing advances and produce a report'
)

Strengths

Natural collaboration: The conversation model enables emergent collaborative behaviours.

Code execution: Built-in sandboxed code execution for agents that need to write and run code.

Flexibility: Highly configurable agent behaviours and conversation patterns.

Research-grade: Designed for experimentation and novel agent architectures.

Weaknesses

Production readiness: Less mature tooling for deployment, monitoring, and reliability.

Unpredictable execution: Conversation-based coordination can lead to meandering or stuck conversations.

Token consumption: Multi-agent conversations use significantly more tokens than directed approaches.

Limited integrations: Fewer pre-built tools and connectors than LangChain.

Test application results

Implementation time: 5 hours
Lines of code: 240
Response latency: 14.8s average
Success rate: 82%
Token efficiency: 24,200 avg tokens/query

AutoGen produced interesting emergent behaviours but was less reliable and more expensive per query.

Head-to-head comparison

Feature matrix

Performance benchmarks

Running 100 identical research queries:

LangChain was most efficient. CrewAI balanced simplicity with reasonable performance. AutoGen was most expensive but produced the most thorough outputs when it succeeded.

Developer experience

LangChain: Requires understanding multiple abstraction layers but provides the most control. Excellent for developers who want to optimize every aspect.

CrewAI: Fastest time to working prototype. The role-based model is intuitive but can feel constraining for complex requirements.

AutoGen: Most experimental-friendly. Great for research but requires more work to productionize.

Use case recommendations

Choose LangChain when:

• You need extensive integrations with existing tools and data sources
• Production reliability and observability are critical
• You're building complex pipelines with multiple LLM calls
• You want JavaScript/TypeScript support
• You need fine-grained control over agent behaviour

Example use cases: Customer support automation, document processing pipelines, RAG applications, enterprise integrations.

Choose CrewAI when:

• Your problem naturally decomposes into specialized roles
• You want rapid prototyping of multi-agent systems
• Agent collaboration is central to your application
• You're comfortable with Python-only development
• Team metaphors make your system easier to reason about

Example use cases: Content creation workflows, research teams, project management automation, creative collaboration.

Choose AutoGen when:

• You're researching novel agent architectures
• Agents need to write and execute code
• Emergent collaborative behaviour is desirable
• You're building experimental or academic applications
• Conversation dynamics are important

Example use cases: Research assistants, code generation systems, educational simulations, agent behaviour research.

Migration considerations

From LangChain to CrewAI

If LangChain feels over-engineered for your needs:

# LangChain pattern
chain = prompt | llm | parser

# CrewAI equivalent
agent = Agent(
    role='Your Role',
    goal='Your Goal',
    tools=[your_tools]
)

CrewAI provides simpler abstractions but fewer customization options.

From CrewAI to LangChain

If CrewAI's role model doesn't fit your architecture:

// CrewAI pattern (agents collaborate automatically)
crew = Crew(agents=[a, b, c])

// LangGraph equivalent (explicit control)
graph = new StateGraph()
  .addNode('a', agentA)
  .addNode('b', agentB)
  .addConditionalEdges('a', router)

LangGraph provides explicit control over agent coordination.

Emerging alternatives

The framework landscape is evolving rapidly:

OpenAI Agents SDK: First-party SDK with native tool calling and handoffs. Worth evaluating for OpenAI-centric architectures.

Anthropic Claude Agents: Tight integration with Claude models. MCP support built-in.

Pydantic AI: Type-safe agent framework with excellent DX. Gaining traction for Python shops.

Vercel AI SDK: Strong choice for Next.js applications with streaming-first design.

Monitor these alternatives as the ecosystem matures.

Our verdict

For most production applications, LangChain remains the safest choice. The ecosystem depth, observability tooling, and multi-language support make it the most versatile foundation.

Use CrewAI when the role-based model genuinely simplifies your architecture. Don't force-fit problems into the crew metaphor - use it where it naturally applies.

Reserve AutoGen for research and experimentation. The conversation-based model produces interesting results but requires more work to make reliable.

The best framework is the one your team can be productive with. Start with a proof of concept in your top choice, then validate before committing to production.

Further reading:

• /blog/langchain-vs-llamaindex-vs-haystack-agent-framework-comparison
• /blog/multi-agent-orchestration-implementation-guide
• LangChain Documentation
• CrewAI Documentation
• AutoGen Documentation