Agent and LLM Frameworks Compared

LangChain

Python / TypeScript

Core Focus

Generic orchestration framework for modular LLM chains, memory management, and tool integrations.

Best Use Case

Rapid prototyping and building versatile applications that require connecting multiple LLM providers and external APIs.

Python / TypeScript

Core Focus

Specialized data ingestion, indexing, vector search, and advanced RAG architectures.

Best Use Case

Applications where structured or unstructured proprietary documents (such as PDFs or knowledge bases) need to be searched accurately.

Python

Core Focus

Modular, production-ready search and NLP pipelines developed by deepset.

Best Use Case

Enterprise RAG systems and large-scale search applications where strict control over dataflow and scalability is required.

C# / Python / Java

Core Focus

Enterprise integration of AI functions via a native Microsoft SDK with plugins and connectors.

Best Use Case

Existing enterprise software and Microsoft ecosystems (.NET/C#) where LLM functionality needs to be securely integrated.

Python / .NET

Core Focus

Multi-agent orchestration where multiple autonomous agents communicate with each other to solve tasks.

Best Use Case

Advanced, autonomous workflows such as automated code generation, debugging, and complex decision-making processes.

Python

Core Focus

Role-based multi-agent collaboration with a clear division of tasks and processes.

Best Use Case

Business processes that require a fixed division of roles, such as market research, content creation, and multi-source data analysis.

Selecting the right framework depends on your existing tech stack and the specific requirements of your project:

For RAG & Documents: Choose LlamaIndex if your priority is advanced indexing and search quality on proprietary data.
For General LLM Apps: Choose LangChain when you need the broadest support for integrations and tools.
For Multi-Agent Automation: Choose CrewAI for a clear division of roles or AutoGen for more dynamic conversations between agents.
For Enterprise .NET Environments: Choose Semantic Kernel for seamless integration with C# codebases and Microsoft Azure infrastructure.