🤖 Why just GPT is not enough
Connect databases, PDF, API
Remember context
Manage agents (the model searches, writes code, and analyzes)
Build chains of actions
Use your data through vector repositories (RAG)
Without frameworks, you'll have to write all this manually. With frameworks, you just assemble the "constructor".
🧩 What AI frameworks do
Opportunity | Why is it necessary? |
|---|---|
RAG (access to external data) | Chat with PDF, websites, knowledge base |
Agent approach | GPT itself decides what to do and in what order |
Integrations (API, SQL, files) | GPT connects to real data |
Caching, logging | For debugging and acceleration |
Easy assembly of pipelines | Less code — faster result |
🚀 Popular frameworks
LangChain is the most famous
🧱 Builder of everything: chains, agents, data processing, integrations. Supports OpenAI, Claude, Ollama, HuggingFace, and others.
Suitable if:
Need to build complex pipelines
You want to use different models and sources
Ready for a little more complexity for flexibility
LlamaIndex (ex-GPT Index)
📚 Specializes in RAG — connecting local documents, vector databases, working with texts.
Suitable if:
Need a "chat with PDF" or search your knowledge base
Want to quickly tweak semantic search?
You work with documents and files
Haystack
🔍 An open alternative for advanced search and chat tasks. Supports UI, connectors, pipelines.
Suitable if:
Do you want open-source and customization
Build a QA platform or chat on your data
You love Python and transparency
Semantic Kernel
🧠 From Microsoft. Focused on creating smart agents and skills that interact with each other.
Suitable if:
You are a .NET or TypeScript developer
Want a modular agent architecture
Integrate AI into an enterprise application
🔍 How to choose a framework?
Do you want... | Your choice |
|---|---|
Quickly attach a chat with a PDF / knowledge base | LlamaIndex |
Build a complex agent with logic and search | LangChain or Semantic Kernel |
Maximum control and open-source | Haystack |
Work with .NET and TS, enterprise solutions | Semantic Kernel |
Minimum dependencies, only basic logic | Generally without a framework or simple llama.cpp + Python |
💡 Tips from Kodik
Don't overcomplicate it. Often, an MVP can be made in pure Python with
openaiandchromadb.Framework ≠ magic. These are just tools, without understanding the basics they will not help.
Better understand RAG and agentsthan chasing "new tools".
Look at the community — LangChain and LlamaIndex are actively developing.
In the attachment Code you will find courses in Python, JavaScript. Right in the code, with hints, exercises, and your own data!
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