AI Agent Frameworks Compared: LangChain vs AutoGen vs CrewAI vs Custom

Building AI agents used to mean writing everything from scratch. Now there are frameworks:

• LangChain: The most popular, jack-of-all-trades
• AutoGen: Microsoft's multi-agent conversation framework
• CrewAI: Role-based agent orchestration
• Custom: Building your own (sometimes the best choice)

Which should you use? It depends on what you're building.

Here's an honest comparison based on building dozens of production agents.

Framework Overview

LangChain

What it is: Comprehensive framework for building LLM applications

Best for:

• RAG (Retrieval-Augmented Generation)
• Chain-of-thought workflows
• Integration with many tools and data sources

Strengths:

• Massive ecosystem
• Tons of pre-built integrations
• Active community
• Good documentation

Weaknesses:

• Can be overly complex for simple use cases
• Frequent breaking changes
• Performance overhead
• Steep learning curve

When to use: You need RAG, many integrations, or complex workflows

AutoGen

What it is: Microsoft's framework for multi-agent conversations

Best for:

• Multi-agent systems
• Agent-to-agent communication
• Code generation and execution

Strengths:

• Excellent for multi-agent orchestration
• Built-in code execution (safe sandboxing)
• Research-backed approach
• Microsoft support

Weaknesses:

• Narrower focus than LangChain
• Fewer integrations out-of-the-box
• Less mature ecosystem

When to use: Building multi-agent systems with complex interactions

CrewAI

What it is: Framework for role-based agent teams

Best for:

• Business process automation
• Task delegation among specialist agents
• Structured workflows

Strengths:

• Simple, intuitive API
• Role-based design maps to business processes
• Built-in task management
• Sequential and parallel execution

Weaknesses:

• Newer, smaller community
• Limited integrations compared to LangChain
• Opinionated structure (can be limiting)

When to use: Modeling business processes with multiple specialist roles

Custom (Build Your Own)

What it is: Direct LLM API calls with custom orchestration

Best for:

• Simple, focused use cases
• Performance-critical applications
• Full control over behavior

Strengths:

• No unnecessary complexity
• Best performance
• Full control
• Easier to debug

Weaknesses:

• More code to write
• No pre-built integrations
• Reinventing common patterns

When to use: Simple agents, performance matters, or frameworks feel like overkill

Real-World Comparison

Let's build the same agent three ways: Customer support agent with RAG

Option 1: LangChain

import { ChatOpenAI } from 'langchain/chat_models/openai'
import { SupabaseVectorStore } from 'langchain/vectorstores/supabase'
import { OpenAIEmbeddings } from 'langchain/embeddings/openai'
import { RetrievalQAChain } from 'langchain/chains'

// Setup vector store
const vectorStore = new SupabaseVectorStore(
  new OpenAIEmbeddings(),
  {
    client: supabase,
    tableName: 'documents',
    queryName: 'match_documents'
  }
)

// Create RAG chain
const chain = RetrievalQAChain.fromLLM(
  new ChatOpenAI({ modelName: 'gpt-4-turbo-preview' }),
  vectorStore.asRetriever()
)

// Use it
const response = await chain.call({
  query: "What's your return policy?"
})

Pros:

• RAG setup in ~10 lines
• Swappable components (vector store, LLM, retriever)
• Handles prompt templating automatically

Cons:

• Abstractions hide what's happening
• Hard to customize prompt structure
• Performance overhead from abstraction layers

Lines of code: ~50 for full implementation

Development time: 2-4 hours (learning curve)

Option 2: Custom

import { ChatOpenAI } from 'openai'
import { createClient } from '@supabase/supabase-js'

class CustomerSupportAgent {
  private llm = new ChatOpenAI({ model: 'gpt-4-turbo-preview' })
  private embeddings = new OpenAIEmbeddings()
  private supabase = createClient(url, key)

  async answer(question: string) {
    // 1. Generate embedding for question
    const embedding = await this.embeddings.create(question)

    // 2. Search vector database
    const { data } = await this.supabase.rpc('match_documents', {
      query_embedding: embedding,
      match_threshold: 0.7,
      match_count: 5
    })

    // 3. Build context
    const context = data.map(d => d.content).join('\n\n')

    // 4. Generate response
    const response = await this.llm.chat.completions.create({
      messages: [
        {
          role: 'system',
          content: `You are a customer service agent. Use this context to answer:

          ${context}`
        },
        {
          role: 'user',
          content: question
        }
      ]
    })

    return response.choices[0].message.content
  }
}

Pros:

• Crystal clear what's happening
• Easy to customize
• No mysterious abstractions
• Best performance

Cons:

• More boilerplate
• Reimplementing common patterns
• No automatic prompt optimization

Lines of code: ~80 for full implementation

Development time: 3-5 hours (if you know what you're doing)

Option 3: CrewAI

import { Agent, Task, Crew } from 'crewai'

// Define specialist agents
const researchAgent = new Agent({
  role: 'Knowledge Base Researcher',
  goal: 'Find relevant information from company docs',
  backstory: 'Expert at searching and synthesizing information',
  tools: [vectorSearchTool]
})

const responseAgent = new Agent({
  role: 'Customer Service Representative',
  goal: 'Provide helpful, accurate answers to customers',
  backstory: 'Friendly, knowledgeable support specialist',
  tools: []
})

// Define tasks
const researchTask = new Task({
  description: 'Find information about: {question}',
  agent: researchAgent
})

const respondTask = new Task({
  description: 'Answer customer question based on research',
  agent: responseAgent
})

// Create crew
const crew = new Crew({
  agents: [researchAgent, responseAgent],
  tasks: [researchTask, respondTask],
  process: 'sequential'
})

// Use it
const result = await crew.kickoff({
  inputs: { question: "What's your return policy?" }
})

Pros:

• Clear separation of concerns (roles)
• Easy to add more specialists
• Built-in task management
• Intuitive for business processes

Cons:

• Overhead of multiple agents for simple tasks
• Less control over individual steps
• Agent-to-agent communication can be unpredictable

Lines of code: ~60 for full implementation

Development time: 2-3 hours

Performance Comparison

Testing the same 100 customer questions:

Framework	Avg Response Time	Cost per 100 queries	Accuracy
LangChain	3.8s	$1.85	87%
Custom	2.1s	$1.20	89%
CrewAI	4.5s	$2.40	88%

Why custom is faster:

• No abstraction layers
• Optimized prompts
• Direct API calls

Why CrewAI is more expensive:

• Multiple agent interactions
• More token usage (agent-to-agent communication)

When to Use Each Framework

Use LangChain if:

✅ You need RAG and don't want to build it from scratch ✅ You're integrating with many data sources ✅ You want pre-built chains (summarization, QA, etc.) ✅ Team is already familiar with it ✅ Prototyping quickly is priority

❌ Avoid if:

• Simple use case (overkill)
• Performance is critical
• You want full control

Use AutoGen if:

✅ Building multi-agent system (3+ agents) ✅ Agents need to negotiate/collaborate ✅ Code generation is part of workflow ✅ You're okay with Microsoft ecosystem

❌ Avoid if:

• Single agent use case
• Need tight control over conversation flow
• Want many pre-built integrations

Use CrewAI if:

✅ Mapping to business processes with clear roles ✅ Task delegation among specialists makes sense ✅ You like role-based design ✅ Sequential/parallel task execution is needed

❌ Avoid if:

• Simple, single-purpose agent
• Need low-level control
• Performance is critical

Use Custom if:

✅ Simple, focused use case ✅ Performance matters ✅ You have specific requirements frameworks don't support ✅ You're comfortable with LLM APIs ✅ Team can maintain custom code

❌ Avoid if:

• Need many integrations quickly
• Team lacks LLM API experience
• Want community solutions for common problems

The Hybrid Approach (Our Recommendation)

Best of both worlds: Use frameworks for what they're good at, custom for the rest.

// Use LangChain for RAG (they solved it well)
import { SupabaseVectorStore } from 'langchain/vectorstores/supabase'

// Custom for business logic
class CustomerSupportAgent {
  private vectorStore = new SupabaseVectorStore(/* ... */)

  async answer(question: string) {
    // Use LangChain for retrieval
    const docs = await this.vectorStore.similaritySearch(question, 5)

    // Custom logic for response generation
    return this.generateResponse(question, docs)
  }

  private async generateResponse(question: string, context: any[]) {
    // Your custom prompt engineering
    // Your custom error handling
    // Your custom business logic
    return response
  }
}

Benefits:

• Leverage framework strengths (RAG, integrations)
• Keep control where it matters (prompts, business logic)
• Best performance
• Easier to debug

Framework Maturity & Ecosystem

LangChain

• Maturity: High (2+ years, widely adopted)
• Community: Very large
• Integrations: 100+ out of box
• Updates: Frequent (can be breaking)
• Production-ready: Yes

AutoGen

• Maturity: Medium (1+ year, growing)
• Community: Medium, research-focused
• Integrations: Limited, but growing
• Updates: Moderate pace
• Production-ready: Yes, for multi-agent use cases

CrewAI

• Maturity: Lower (<1 year publicly)
• Community: Small but active
• Integrations: Limited
• Updates: Rapid development
• Production-ready: Emerging

Custom

• Maturity: N/A (you control it)
• Community: LLM API communities (OpenAI, Anthropic)
• Integrations: Whatever you build
• Updates: On your schedule
• Production-ready: Depends on your implementation

Migration Difficulty

Easiest to hardest to migrate away from:

1. Custom → Easy (you own the code)
2. CrewAI → Moderate (role-based structure is portable)
3. AutoGen → Moderate (agent patterns are somewhat standard)
4. LangChain → Hard (deeply integrated abstractions)

Recommendation: Start simple, add framework complexity only when needed.

Real Production Examples

Example 1: E-Commerce Support (Custom)

Why custom: Simple use case, performance critical

// Direct OpenAI API calls
// Custom RAG with Supabase
// ~200 lines total
// Response time: < 2s
// Cost: $0.01 per interaction

Result: Fast, cheap, exactly what we need

Example 2: Legal Research (LangChain)

Why LangChain: Complex document retrieval, need many integrations

// LangChain for multi-step retrieval
// Custom business logic on top
// ~500 lines total
// Response time: ~5s (acceptable for this use case)
// Cost: $0.15 per research query

Result: Saved weeks of development time

Example 3: Multi-Agent Workflow (AutoGen)

Why AutoGen: 5 specialist agents need to collaborate

// AutoGen for agent coordination
// Custom tools for each agent
// ~800 lines total
// Response time: ~10s (complex workflow)
// Cost: $0.40 per workflow

Result: Only framework that made multi-agent coordination manageable

The Decision Tree

Is your use case simple and focused?
├─ YES → Start with Custom (upgrade to framework if needed)
└─ NO → Is it primarily about RAG/retrieval?
    ├─ YES → LangChain
    └─ NO → Do you need multi-agent collaboration?
        ├─ YES → AutoGen or CrewAI
        └─ NO → Custom or LangChain based on integrations needed

Cost of Complexity

Framework complexity tax:

Approach	Initial Dev Time	Maintenance	Performance	Cost
Custom	Higher	Low	Best	Lowest
LangChain	Medium	Medium	Good	Medium
AutoGen	Medium	Medium	Good	Medium
CrewAI	Lower	Medium	Fair	Higher

Long-term: Custom wins if you can afford initial development

The Bottom Line

For most small businesses starting with AI agents:

Start custom, then add frameworks as needed:

1. Build v1 with direct API calls (1-2 weeks)
2. Identify pain points (retrieval? orchestration? integrations?)
3. Add framework components for those specific needs
4. Keep business logic custom

Why this works:

• Fastest time to value
• Learn what you actually need
• Avoid over-engineering
• Keep costs low
• Maintain control

When to use frameworks from day 1:

• Team already knows the framework
• Use case perfectly matches framework strength
• Prototyping/validation phase

Remember: Frameworks are tools, not requirements. The best agent is the one that ships and solves real problems—whether that's 50 lines of custom code or 500 lines of LangChain.

Questions about which approach is right for your use case? Schedule a consultation - we can review your requirements and recommend the best path.