How to choose an LLM framework

Wrong LLM framework choice costs 3-4 months in migration time.

Framework quick picks:

• LangChain – Rapid prototyping, 400+ integrations, but slower performance
• Haystack – Production RAG specialist for document-heavy apps
• AutoGen – Multi-agent collaboration workflows
• CrewAI – Lightweight, fast, custom applications

Key trade-offs:

• Cloud = latest models but scaling costs; Local = control but needs hardware
• Real-time apps need local models; batch processing can use cloud APIs

Decision shortcut: Fast development + integrations → LangChain | Production RAG → Haystack | Multi-agent → AutoGen | Performance + control → CrewAI

Bottom line: Start with constraints, prototype 2-3 options with real data, plan for future migration.

Your LLM framework choice will make or break your product’s success.

With over 50 new frameworks launched in 2023-2024 alone, developers face an overwhelming array of options with overlapping capabilities and bold marketing claims.

After building LLM products with all major frameworks, we’ve learned that most teams choose based on hype rather than requirements. The result? Technical debt, performance bottlenecks, and costly migrations that could have been avoided with proper evaluation.

This guide cuts through the noise to show you exactly how to choose the right LLM framework comparison for your specific needs, complete with real-world case studies and a practical decision framework you can use today.

The cost of choosing wrong

Framework selection isn’t just a technical decision—it’s a business one. Choose poorly and you’ll face architecture limitations that emerge at scale, developer productivity impacts that slow feature development by 40-60%, and vendor lock-in that makes switching frameworks require rewriting 70-80% of your application logic.

GitHub’s analysis shows that teams spend an average of 3-4 months migrating between LLM frameworks, not including the opportunity cost of delayed features. The switching costs are real and avoidable with proper initial evaluation.

Framework comparison: The big four

LangChain: The comprehensive ecosystem

LangChain dominates mindshare with the most comprehensive ecosystem for LLM applications. It offers 400+ pre-built tools, mature agent capabilities, and extensive documentation that makes rapid prototyping straightforward.

However, this comprehensiveness comes with trade-offs. The multiple abstraction layers create performance overhead that can impact latency, while the framework’s complexity can lead to over-engineering simple applications. Debugging becomes challenging due to nested abstractions, and frequent API changes in earlier versions created stability concerns.

from langchain.agents import create_openai_functions_agent
from langchain.tools import Tool
from langchain.schema import SystemMessage

# Define custom tools
def calculate_tax(income: float) -> str:
    return f"Tax on £{income}: £{income * 0.2}"

tools = [Tool(
    name="tax_calculator",
    func=calculate_tax,
    description="Calculate UK income tax"
)]

# Create agent with system message
agent = create_openai_functions_agent(
    llm=ChatOpenAI(temperature=0),
    tools=tools,
    messages=[SystemMessage(content="You are a helpful tax advisor")]
)

LangChain excels for teams prioritising development speed over performance optimisation, complex agent workflows requiring multiple tool integrations, and scenarios where the extensive ecosystem justifies the overhead.

Haystack: Production-ready RAG specialist

Deepset’s Haystack takes a different approach, focusing specifically on production RAG applications with enterprise-grade reliability. Its production-focused architecture handles scale and reliability from day one, with superior RAG capabilities and modular pipeline design that promotes clean architecture.

The trade-off is a steeper learning curve and limited agent capabilities for complex multi-agent workflows. The smaller ecosystem means fewer third-party integrations, and the higher upfront complexity requires more architectural planning.

from haystack import Pipeline
from haystack.nodes import DensePassageRetriever, FARMReader
from haystack.document_stores import ElasticsearchDocumentStore

# Setup document store and retriever
document_store = ElasticsearchDocumentStore(host="localhost", port=9200)
retriever = DensePassageRetriever(
    document_store=document_store,
    query_embedding_model="facebook/dpr-question_encoder-single-nq-base"
)

# Build pipeline
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipeline.add_node(component=FARMReader(model_name_or_path="deepset/roberta-base-squad2"), 
                  name="Reader", inputs=["Retriever"])

Haystack is ideal for production RAG systems handling large document collections, enterprise deployments with compliance requirements, and teams prioritising performance and reliability over rapid prototyping.

AutoGen: Multi-agent orchestration

Microsoft’s AutoGen excels at complex multi-agent scenarios where different AI personalities need to collaborate effectively. It provides sophisticated multi-agent patterns with built-in conversation management and excellent observability for debugging complex agent interactions.

The downside is that it’s overkill for single-agent applications, with risk of infinite loops in poorly designed multi-agent conversations. The smaller community compared to LangChain means fewer resources, and there’s a learning curve for understanding multi-agent design patterns.

import autogen

config_list = [{"model": "gpt-4", "api_key": "your-api-key"}]

# Define agents with different roles
user_proxy = autogen.UserProxyAgent(
    name="user_proxy",
    human_input_mode="TERMINATE",
    code_execution_config={"work_dir": "coding"}
)

assistant = autogen.AssistantAgent(
    name="assistant", 
    llm_config={"config_list": config_list}
)

# Multi-agent conversation
user_proxy.initiate_chat(
    assistant, 
    message="Create a Python script to analyse CSV sales data"
)

AutoGen suits applications requiring sophisticated agent collaboration, complex workflows where different agents have specialised roles, and teams building advanced AI assistants with multiple capabilities.

CrewAI: Lightweight simplicity

CrewAI offers a minimalist approach to framework for multi-agent LLM applications without the overhead of larger frameworks. Its intuitive Python-based API is easy to learn, with lightweight architecture and clear crew/agent/task abstractions that map to business logic.

The constraints are a smaller community and ecosystem, limited enterprise features, and fewer integrations with external tools. As a newer framework, the API may still evolve and introduce breaking changes.

from crewai import Crew, Agent, Task

# Define specialised agents
researcher = Agent(
    role='Researcher',
    goal='Find accurate information about the topic',
    backstory='Expert researcher with attention to detail'
)

writer = Agent(
    role='Writer', 
    goal='Create engaging content from research',
    backstory='Skilled writer who makes complex topics accessible'
)

# Define tasks
research_task = Task(
    description='Research the latest trends in AI development',
    agent=researcher
)

writing_task = Task(
    description='Write a blog post based on the research findings',
    agent=writer
)

# Create and run crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, writing_task],
    verbose=True
)

result = crew.kickoff()

CrewAI works best for teams wanting simplicity without complexity, performance-sensitive applications where overhead matters, and custom agent workflows requiring tight architectural control.

Key trade-offs in framework selection

Cloud vs local deployment

Local vs cloud LLM framework trade-offs significantly impact your architecture and costs. Cloud deployment provides immediate access to state-of-the-art models with automatic updates and reduced deployment complexity, but comes with API costs that scale with usage and potential data privacy concerns.

Local deployment offers cost predictability and complete data control with lower latency, but requires hardware investment, model management complexity, and limits you to open-source models. The choice often depends on your data sensitivity, volume, and technical capabilities.

Performance vs convenience

Real-time applications like chatbots need latency optimisation through local models, streaming responses, and intelligent caching. Batch processing applications can prioritise cost efficiency with smaller models, request batching, and off-peak processing.

High-control scenarios requiring custom model implementations or compliance features need maximum flexibility, while convenience-focused approaches prioritise rapid MVP development and standard use cases with proven patterns.

A practical decision framework

Essential questions for framework selection

Start by assessing your application complexity. Single-agent query-response applications need different capabilities than complex collaborative workflows. Consider your performance and scale requirements, including expected volume, latency needs, and context requirements.

Evaluate your deployment constraints, including environment restrictions, resource availability, and compliance needs. Factor in your team’s current expertise, timeline pressure, and maintenance capacity. Finally, consider your integration requirements with third-party services and existing infrastructure.

Decision matrix approach

Create a weighted scoring system across key dimensions:

Weight each category based on your specific requirements to find the best fit.

Real-world applications

Customer support chatbot case

An e-commerce company needed 24/7 customer support handling 10,000+ daily interactions with sub-2-second response times. They required integration with order management systems and cost control under €0.10 per interaction.

Haystack proved ideal for this scenario, providing production reliability with strong RAG capabilities for product knowledge. The result was 40% faster response times, 60% better answer accuracy, and €0.07 per interaction costs including infrastructure.

Multi-agent research assistant

A legal firm needed automated research across case law and regulations with specialised agents for research, analysis, and citation checking. They required complete audit trails for regulatory compliance.

AutoGen’s sophisticated multi-agent orchestration provided the necessary conversation management and observability. The result was 70% reduction in research time with improved citation accuracy and complete compliance audit trails.

Private document analysis

A healthcare organisation needed to process patient records entirely on-premises with strict data privacy requirements and limited GPU resources.

CrewAI’s lightweight architecture maximised available compute resources while providing necessary flexibility. They successfully deployed with Llama 2, processing 1,000+ documents daily within resource constraints.

Implementation best practices

Start with constraints, not features. Your budget, timeline, team expertise, and performance requirements will eliminate most options immediately. Prototype quickly with your top 2-3 candidates using real data and representative use cases, as theoretical comparisons only go so far.

Avoid common pitfalls like choosing based on popularity rather than fit, under-estimating learning curves, and ignoring performance considerations early in development. Plan for change by building abstraction layers that isolate framework-specific code from business logic.

Consider expert guidance. The cost of framework consultation is typically far less than the cost of choosing wrong and migrating later. Our AI development team has hands-on experience with all major frameworks and can help you navigate this decision.

Making your choice

How to choose LLM framework for product development ultimately comes down to honest assessment of your requirements versus framework capabilities. No single framework excels at everything, so focus on fundamentals: performance, reliability, and developer productivity over flashy features you might never use.

The LLM framework landscape will continue evolving rapidly. The key is choosing something that meets your current needs while positioning you well for future developments. Focus on your specific use case, test with real data, and don’t be afraid to start simple and evolve as your requirements become clearer.

Ready to make the right framework choice for your LLM application? Get in touch to discuss your specific requirements and get expert guidance on your framework selection.