Research Report RL1
Agentic AI Frameworks & Languages
A strategic analysis of framework adoption, capability, and vendor lock-in risks for scalable AI architecture.
Adoption Score
Capability Radar
Ecosystem Landscape
Analysis by Language & Deployment Risk
| Language | Frameworks & Role | Lock-in Risk |
|---|---|---|
| Python | LangChain, LlamaIndex, AutoGen, CrewAI. Essential for data science. |
Low (Codebase) |
| C# / .NET |
Primary for Semantic Kernel (SK).
Note: SK also supports Python & Java.
|
High (Ecosystem) |
| Multi-SDK | Vertex AI & LangChain offer SDKs for Node.js, Go, and Java. | Moderate (Platform) |
Critical Tool: Pydantic
Pydantic is essential across Python frameworks (LangChain, AutoGen, Vertex AI SDK) for defining data schemas, guaranteeing structured output, and enabling reliable LLM Function Calling.
Vendor Lock-in Context
Low Lock-in: Open-source frameworks (LangChain, CrewAI) are LLM-agnostic.
High Lock-in: Platforms (Vertex AI, Semantic Kernel) deeply integrated with cloud proprietary services.