
# **Comparison Chart: LangChain vs StratifyAI vs LLMLite**

| Category | **LangChain** | **StratifyAI** | **LLMLite** |
|---------|----------------|----------------|--------------|
| **Primary Purpose** | Full AI application framework with chains, agents, tools, and orchestration | Unified multi‑provider LLM layer with routing, cost control, caching, and large‑file/RAG support | Lightweight wrapper for simple LLM API calls |
| **Complexity Level** | High — large ecosystem, many abstractions | Medium — focused, production‑oriented | Low — minimalistic |
| **Best For** | Complex AI apps, agents, workflows | Teams needing reliability, multi‑provider flexibility, and cost optimization | Simple apps or prototypes |
| **Multi‑Provider Support** | Yes, but inconsistent across integrations | Yes — 9+ providers with unified interface | Limited — usually OpenAI‑compatible only |
| **Routing / Model Selection** | Basic or community‑built | Advanced: cost, quality, latency, hybrid, fallback chains | None |
| **Cost Tracking** | Not built‑in | Built‑in with budgets and usage tracking | None |
| **Caching** | Optional integrations | Built‑in response + provider caching | None |
| **Large File Handling** | Limited | Strong: chunking, extraction, summarization | None |
| **RAG Support** | Yes (via integrations) | Yes (embeddings + ChromaDB) | No |
| **CLI Tools** | Some community tools | Full CLI: chat, routing, caching, analysis | Minimal or none |
| **Learning Curve** | Steep | Moderate | Very easy |
| **Ecosystem Size** | Very large | Focused and growing | Small |
| **Async Support** | **Partial async** — supported but inconsistent across chains/tools | **Full async** — built‑in across providers, routing, streaming | **No async** — sync‑only |
| **Ideal Users** | AI researchers, agent builders, workflow teams | Data engineers, backend teams, enterprises needing reliability | Solo developers, hobbyists |

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## **Summary in Plain English**

### **LangChain**

A big, powerful toolbox for building AI applications. Great for agents, workflows, and experimentation — but can feel heavy and complex.

### **StratifyAI**

A clean, production‑ready layer that helps teams use multiple AI providers intelligently. It focuses on routing, cost control, reliability, and handling real‑world workloads like large documents and RAG.

### **LLMLite**

A tiny, simple library for making LLM calls. Fast to learn, but limited in features.

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