AI Indore Labs
All tutorials
35 free tutorials, in the order we recommend reading them.
AI Agents · Agent Foundations
AI Agents Explained: LLM, Tools, Memory and the Agent Loop
AI agents explained: how an LLM, tools, memory and a loop work together, the ReAct pattern, planning and guardrails, with a toy agent you can run in Python.
AI Agent vs LLM vs RAG: Key Differences with Examples
AI agent vs LLM vs RAG: what each one does, how they differ on knowledge, actions and cost, and how to pick the right one, with runnable Python simulations.
Building Blocks of AI Agents: Role, Tools, Memory, Guardrails
The six building blocks of AI agents: role, task, tools, memory, guardrails and cooperation, how each shapes behaviour, and a Python agent spec you can lint.
5 Agentic AI Design Patterns Explained with Python Examples
The 5 agentic AI design patterns, reflection, tool use, ReAct, planning and multi-agent, explained with when to use each and runnable Python simulations.
5 Levels of Agentic AI Systems: From Responder to Autonomous
The 5 levels of agentic AI explained: basic responder, router, tool calling, multi-agent and autonomous, who controls the flow at each, and how to pick a level.
Memory in AI Agents: Short-Term, Long-Term, Episodic and More
Memory in AI agents explained: short-term context, semantic, episodic and procedural long-term memory, and how agents save, retrieve and forget, in Python.
Agentic AI Glossary: 40 Must-Know Terms Explained Simply
Agentic AI glossary: 40 must-know terms, from agent loop and tool calling to MCP, A2A, guardrails and agentic RAG, each in plain English with examples.
AI Agents · Building Agents
Build a ReAct Agent from Scratch in Python (No Framework)
Build a ReAct agent from scratch in Python: write the prompt, parse Thought and Action text, run tools safely, stop loops, then plug in a real LLM.
Tools and Function Calling for AI Agents: A Practical Guide
How function calling works in AI agents: JSON Schema tool definitions, the call and result cycle, safe validation, and a real API loop, with Python examples.
Multi-Agent System Patterns: 7 Ways AI Agents Work Together
Multi-agent system patterns explained: sequential, parallel, loop, router, aggregator, hierarchical and network, with handoffs, trade-offs and Python demos.
Agentic RAG Explained: Build a Self-Correcting RAG Agent
Agentic RAG explained: how an agent routes questions, grades retrieved passages, rewrites weak queries and falls back to other sources, with Python code.
Guardrails for AI Agents: Input, Tool and Output Checks
Guardrails for AI agents explained: input filters, tool allowlists, human approval, output checks and run budgets, with Python code and an Agents SDK example.
How to Evaluate AI Agents: Metrics, Test Sets and Code
How to evaluate AI agents: outcome, trajectory, efficiency and safety metrics, test sets, pass@k vs pass^k and LLM judges, with a runnable Python harness.
Deploying AI Agents to Production: Architecture and Code
Deploying AI agents to production: API, queue and worker design, streaming, checkpoints, retries, idempotent tools, tracing and costs, with Python examples.
A2A and AG-UI Explained: Agent Protocols with Python Examples
A2A and AG-UI agent protocols explained: agent cards, tasks and states, streamed UI events, how they differ from MCP, and Python code for both protocols.
AI Agents · Agent Projects
Build a Multi-Agent Research Assistant in Python: Full Project
Build a multi-agent research assistant in Python: planner, parallel searchers, cited writer and fact-checker, simulated offline, then a real Agents SDK version.
Build a Voice RAG Agent in Python: Speech In, Answers Out
Build a voice RAG agent in Python: voice activity detection, speech-to-text, retrieval, speech-friendly answers and text-to-speech, simulated, then for real.
Build a Multi-Agent Travel Finder in Python with LangGraph
Build a multi-agent travel finder in Python: parse a trip request, search flights and hotels in parallel, fit a budget, write an itinerary, then use LangGraph.
Build a Financial Analyst AI Agent in Python with MCP Tools
Build a financial analyst AI agent in Python: parse stock questions, compute return, volatility and drawdown in code, serve them over MCP, check every report.
MCP Tutorial · Model Context Protocol
What is MCP? The Model Context Protocol Explained for Beginners
What is MCP? The Model Context Protocol explained: hosts, clients, servers, tools, stateless JSON-RPC in spec 2026-07-28, and a Python tools/call simulation.
Why MCP Was Created: Problems Before Model Context Protocol
Why was MCP created? See the glue-code, drift and discovery problems AI apps faced before 2024, how MCP fixed them, and how the spec changed through 2026.
MCP Architecture: Hosts, Clients, Servers and Transports
MCP architecture in depth: host, client and server roles, the data and transport layers, stateless requests in spec 2026-07-28, stdio vs Streamable HTTP.
MCP Tools, Resources and Prompts: The Three Server Primitives
MCP tools, resources and prompts explained field by field: schemas, annotations, structured output, URI templates, prompt arguments, plus runnable Python demos.
MCP vs REST API: Differences, Examples and When to Use Each
MCP vs REST API compared on discovery, endpoints, errors, auth and caching, with a runnable Python wrapper that turns a REST API into MCP tools.
MCP vs Function Calling: How They Differ and Work Together
MCP vs function calling: function calling is how a model asks for a tool, MCP is how the app reaches it. See the full loop with runnable Python code.
Build an MCP Server in Python with MCPServer (SDK 2.x Guide)
Build an MCP server in Python with the official SDK 2.x: MCPServer, typed tools, ToolError, resources, prompts, progress, stdio and Streamable HTTP, plus tests.
Build an MCP Client in Python: Connect Servers to an LLM
Build an MCP client in Python with SDK 2.x: connect over stdio or HTTP, merge tools from many servers, gate risky calls, and drive an LLM tool loop.
MCP Security Best Practices: Threats, Auth and Safe Defaults
MCP security best practices: tool poisoning, rug pulls, prompt injection, token audience, no passthrough, local server risks, with runnable Python checks.
MCP Tool Overload: How to Keep AI Agents Accurate with Many Tools
Too many MCP tools hurt agent accuracy and cost. Measure the token load, then fix it with task-level tools, tool profiles and dynamic tool retrieval in Python.
AI Trends · Trend Reports
AI Trends 2026: What's Changing Across Data, GenAI and Robotics
The five AI shifts of 2026, from agents and open models to robots and new data rules, with sources, why each matters and what learners should do next.
Generative AI Trends 2026: Agents, MCP, Open Models and More
Sourced 2026 generative AI trends: agentic workflows, MCP and A2A, open-weight models, multimodal input, guardrails and the falling cost of inference.
Data Science and ML Trends 2026: AutoML, MLOps and Regulation
Data science trends for 2026: AutoML agents, tabular foundation models, MLflow 3, pandas 3.0, and India's DPDP Rules, with sources and learner advice.
Data Analytics Trends 2026: AI in BI, Semantic Layers and Trust
What changed in analytics in 2026: Copilot and Genie-style assistants, text-to-SQL, semantic layers, Iceberg, and data quality, with sources for each.
Robotics and Edge AI Trends 2026: Humanoids, VLAs and ROS 2
Robotics trends 2026: record robot installs and India's rank, humanoid pilots, vision-language-action models, edge AI boards and ROS 2, all sourced.