Framework Comparison

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Overview

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What Makes Nadoo Different

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Feature Comparison Matrix

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Detailed Comparisons

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vs LangChain

from nadoo_flow import LLMNode, FunctionNode

workflow = (
    FunctionNode(lambda x: {"text": x["input"]})
    | LLMNode(model="gpt-4")
    | FunctionNode(lambda x: {"result": x["content"]})
)

result = await workflow.execute({"input": "Hello"})

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

prompt = PromptTemplate(
    template="Process: {input}",
    input_variables=["input"]
)
chain = LLMChain(llm=OpenAI(), prompt=prompt)
result = chain.run(input="Hello")

​

vs CrewAI

from nadoo_flow import ParallelNode, LLMNode

# Parallel task execution
workflow = ParallelNode([
    LLMNode(model="gpt-4", name="analyzer"),
    LLMNode(model="gpt-4", name="summarizer"),
    LLMNode(model="gpt-4", name="translator")
])

results = await workflow.execute({"text": data})

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Researcher",
    goal="Research topics",
    backstory="Expert researcher"
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

​

vs AutoGen

from nadoo_flow import ChainableNode

class CustomAgent(ChainableNode):
    async def execute(self, data):
        # Custom logic
        return {"result": "processed"}

workflow = CustomAgent() | LLMNode()
result = await workflow.execute(input_data)

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user")

user_proxy.initiate_chat(
    assistant,
    message="Solve this task"
)

​

vs n8n

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Architecture Comparison

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Execution Models

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Design Philosophy Differences

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Dependency Footprint

• ~10 core dependencies (Pydantic, httpx, etc.)
• Philosophy: Include only what’s necessary
• Trade-off: Fewer built-in integrations

• 100+ dependencies with full installation
• Philosophy: Batteries-included approach
• Trade-off: Larger installation, more complexity

• 30-40 dependencies
• Philosophy: Focused on specific use cases
• Trade-off: Middle ground approach

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Async Architecture

• Built with async/await from the ground up
• Efficient for I/O-bound operations (API calls, DB queries)
• Better resource utilization in production

• Started synchronous, added async later
• Both sync and async APIs available
• More flexible but potentially inconsistent patterns

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Real-World Considerations

• Network latency to LLM providers
• Complexity of your workflows
• Server infrastructure
• Caching strategies

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When to Choose Each Framework

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Choose Nadoo Flow Core if you:

• Need minimal dependencies for security/compliance reasons
• Are building production APIs with performance requirements
• Want full async support from the ground up
• Prefer simplicity and predictability over extensive features
• Are comfortable building custom integrations

• You need extensive pre-built integrations immediately
• You’re just prototyping and want rapid experimentation
• You require mature multi-agent frameworks today

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Choose LangChain if you:

• Need quick prototyping with many pre-built components
• Want a mature, battle-tested framework
• Require extensive integrations (vector DBs, document loaders, etc.)
• Prefer a large community and abundant tutorials
• Don’t mind additional dependencies

• You need minimal dependencies
• You require native async throughout
• You want simple, predictable APIs

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Choose CrewAI if you:

• Specifically need role-based multi-agent systems
• Want agent collaboration patterns out of the box
• Are building team-based agent scenarios

• You need general-purpose workflows
• You require extensive async support

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Choose AutoGen if you:

• Need conversational agent frameworks
• Want autonomous agent communication
• Are building research-oriented agent systems

• You need simple, predictable workflows
• You want minimal abstractions

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Honest Assessment

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Nadoo’s Current State (2025 Q4)

• Clean, minimal design
• Async-first architecture
• Low dependency count
• Easy to understand and debug

• Early stage (v0.1.0 beta)
• Small community compared to LangChain
• Fewer pre-built integrations
• Less third-party content (tutorials, examples)
• Still proving itself in production

• We’re not trying to replace LangChain for everything
• We focus on production APIs and secure deployments
• We prioritize stability over rapid feature addition
• We value honest comparisons over marketing claims

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Migration Paths

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From LangChain

# LangChain
from langchain.chains import LLMChain
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run(input="test")

# Nadoo Flow Core
from nadoo_flow import LLMNode
node = LLMNode(model="gpt-4")
result = await node.execute({"prompt": "test"})

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From CrewAI

# CrewAI
agent = Agent(role="Researcher", goal="Research")
task = Task(description="Research X")
crew = Crew(agents=[agent], tasks=[task])

# Nadoo Flow Core
class ResearchAgent(ChainableNode):
    async def execute(self, data):
        # Research logic
        return {"research": results}

workflow = ResearchAgent() | LLMNode()

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Ecosystem and Community

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Maturity Levels (as of 2025 Q4)

• Most mature and widely adopted
• 80k+ GitHub stars
• Extensive tutorials and third-party content
• Multiple books and courses available
• Large Discord community

• Established frameworks with growing communities
• 15k-25k GitHub stars
• Active development
• Good documentation
• Focused use cases

• Early stage (v0.1.0 beta)
• Small but growing community
• Active development
• Documentation in progress
• Focus on quality over quantity

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Integration Ecosystem

• Immediate access to 50+ vector databases → LangChain
• Extensive document loaders → LangChain
• Pre-built agent templates → CrewAI / AutoGen
• Custom integrations with full control → Nadoo Flow Core

• We provide clean APIs for building custom integrations
• We’ll add common integrations based on user demand
• We prefer quality integrations over quantity
• Community contributions welcome

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Cost Considerations

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Open Source Costs

• Nadoo: Lower learning curve
• LangChain: Steeper learning curve
• CrewAI: Medium complexity
• AutoGen: Higher complexity

• Nadoo: Lightweight, lower hosting costs
• Others: Higher memory/CPU requirements

• Nadoo: Fewer dependencies, easier updates
• Others: More dependencies to manage

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Making the Right Choice

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Start with Your Constraints

• Tight security requirements → Nadoo (minimal deps) or self-hosted options
• Need to move fast → LangChain (most pre-built components)
• Multi-agent focus → CrewAI or AutoGen
• Production API constraints → Nadoo or AutoGen (native async)

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Consider Your Team

• Experienced Python developers → Any framework works
• New to AI development → LangChain (more tutorials)
• Security-focused → Nadoo (fewer dependencies to audit)
• Research-oriented → LangChain or AutoGen

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Be Honest About Timeline

• Production system in 2 weeks → Use mature framework (LangChain)
• Prototype in 1 day → LangChain
• Long-term maintainable system → Consider Nadoo or build custom
• Multi-agent research → CrewAI or AutoGen

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The Real Question

• What are my actual constraints?
• What’s my timeline?
• What’s my team’s expertise?
• What’s the long-term maintenance plan?

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Our Honest Recommendation

• You need minimal dependencies (security/compliance)
• You’re building production APIs (async-native helps)
• You want to grow with a newer framework
• You value simplicity over extensive features

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Next Steps