RAG & Agent Frameworks
🔗 LangChain
Meaning: A framework for building LLM-powered apps with memory, tool use, and RAG.
Example: Building a customer-support chatbot that looks up your company's knowledge base before answering.
from langchain.chat_models import ChatOpenAI from langchain.chains import RetrievalQA # Initialize LLM and retrieval chain llm = ChatOpenAI() qa_chain = RetrievalQA.from_chain_type(llm=llm) # Query with context response = qa_chain.run("What is our refund policy?") print(response)
📊 LlamaIndex
Meaning: Specializes in connecting LLMs to your structured/unstructured data.
Example: A law firm loads thousands of PDFs (contracts) → chatbot answers legal queries from those docs.
Key Features:
- Advanced document indexing
- Query routing and optimization
- Structured data integration
- Multi-modal support (text, tables, images)
🌾 Haystack
Meaning: An open-source RAG framework for production-ready search + QA.
Example: News company builds a search system where users can ask "Summarize all articles about inflation in 2025" → Haystack + LLM answers.
Production Features:
- Pipeline-based architecture
- Built-in evaluation tools
- Scalable document stores
- REST API ready
Infrastructure & Operations
🔌 MCP (Model Context Protocol)
Meaning: A standard by Anthropic for LLMs to talk to tools, data, and each other.
Example: Instead of writing custom APIs for every agent, MCP defines a "language" → easier to plug models into company systems.
Benefits:
- Standardized tool interfaces
- Interoperability between different LLMs
- Simplified integration with enterprise systems
- Built-in security and access controls
🎯 AgentOps
Meaning: A framework to observe, debug, and track AI agents (like DevOps but for AI agents).
Example: Team deploys multiple AI assistants (data cleaner, recruiter bot) → AgentOps monitors errors, costs, and tool usage.
Key Capabilities:
- Agent performance monitoring
- Cost tracking per agent/task
- Error detection and alerting
- Tool usage analytics
- A/B testing for prompts
Understanding Agents vs LLMs
🧠 LLM (Large Language Model)
Meaning: A model trained to generate text (predicts the next word).
Example: Ask "What's 2+2?" → it answers "4."
Characteristics:
- Stateless (no memory between calls)
- Text in, text out
- No external actions
- Single-turn interactions
🤖 Agent
Meaning: An LLM wrapped with memory, tools, and goals → can reason and act.
Example: Agent gets task: "Book me a flight and add it to my calendar."
It:
It:
- Talks to flight API
- Confirms booking
- Adds event to Google Calendar
Design Patterns:
- ReAct: Reason + Act loop
- Planner-Executor: One agent plans, another executes
- Multi-agent teams: Recruiter bot + reviewer bot + scheduler bot work together