Pranav Srivastava

Labs

Build something real, in under an hour.

Short, hands-on labs with code that runs as-is. Each one ends with a working thing you built yourself — not a slideshow. The fastest way to learn a system is to make it do something.

Labs are quick, single-session builds. For the deep, structured path, see the full courses in Learn.

Available now

6 ready to build
45 min·Intermediate

Turn a folder into an OKF bundle, then embed it

Build a tiny Open Knowledge Format bundle (markdown + YAML), then embed it so an AI can find the right file by meaning and answer grounded in its exact contents — the folder-plus-vector architecture, hands-on.

You build:An OKF folder you built, embedded into a vector index, answering questions by finding the right file and grounding its answer in that file — with a citation.

Python 3.10+ sentence-transformers Anthropic API
40 min·Intermediate

Build a ReAct agent in 40 lines

Write a real Reason+Act agent from scratch — Thought, Action, Observation, loop — that reasons, calls tools, and solves a multi-step question while printing its whole trace.

You build:A running ReAct agent that answers a multi-hop question using tools, with its Thought / Action / Observation trace printed live.

Python 3.10+ Anthropic API
45 min·Intermediate

Add memory to an AI agent with LangGraph

Give an agent short- and long-term memory using LangGraph checkpoints, so it remembers across turns and across sessions.

You build:An agent that recalls earlier context within a conversation and persists state between runs.

Python LangGraph SQLite
40 min·Intermediate

Put guardrails on an LLM endpoint

Wrap an LLM call with input/output guardrails — rules it cannot cross — so it stays safe and on-topic in production.

You build:An LLM endpoint that refuses or reroutes unsafe and off-policy requests, with an audit trail.

Python FastAPI Anthropic API
45 min·Intermediate

A minimal RAG system in one file

Ground an LLM in your own documents end to end — chunk, embed, retrieve, and answer with citations — in a single readable Python file.

You build:A working retrieval-augmented answer system over your own docs, with sources.

Python sentence-transformers Anthropic API
30 min·Beginner

Build your first MCP server in 30 minutes

Stand up a working Model Context Protocol server in Python, expose a real tool, and call it live from an MCP client — no prior MCP experience needed.

You build:A running MCP server that exposes a working tool, connected to a client and answering live.

Python 3.10+ FastMCP uv

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