Framework Comparison

Compare popular agentic AI frameworks and find the perfect fit for your project

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Review essential insights about choosing and using agentic AI frameworks. Check off each item as you internalize it.

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No single "best" framework - choose based on your specific needs

Use case, tech stack, team expertise, and priorities all matter

LangChain offers the richest ecosystem and most extensive tooling

Best for RAG, document processing, and general agent workflows

AutoGen excels at multi-agent collaboration and code generation

Native support for agent-to-agent communication and group chats

Semantic Kernel is the go-to for .NET developers and Azure deployments

Enterprise-ready with first-class Azure and Microsoft ecosystem support

Haystack specializes in production-grade search and RAG systems

Optimized for document retrieval, NLP pipelines, and Q&A

Custom frameworks offer maximum control for specialized needs

Best for performance-critical apps, unique requirements, or learning

Consider tech stack compatibility early in decision process

Python dominates, but .NET has Semantic Kernel as strong option

Evaluate production readiness and observability support

LangSmith, Azure App Insights, and custom telemetry matter

Framework ecosystem size affects development speed

More integrations and community support means faster building

Multi-agent support varies significantly across frameworks

AutoGen native, LangGraph partial, others limited

Migration between frameworks requires 2-4 weeks of effort

Tools, memory, prompts all need adaptation to new patterns

Start with established frameworks unless requirements are truly unique

Custom frameworks have high upfront and ongoing maintenance costs

Test agent behavior thoroughly when switching frameworks

Same prompts can produce different results in different frameworks

Documentation quality and community size matter for long-term success

Stuck problems need answers - active communities help

Framework choice is not permanent - you can migrate later

Start with what fits now, re-evaluate as your needs evolve