NirDiamant/GenAI_Agents

50+ tutorials and implementations for Generative AI Agent techniques, from basic conversational bots to complex multi-agent systems

Summary

GenAI_Agents is a notebook-based collection of 64 generative-AI agent tutorials and implementations, ranging from conversational bots and question-answering agents to LangGraph workflows, tool use, evaluation, and multi-agent systems. The collection serves as both a learning path and a set of implementation references for choosing and prototyping agent patterns.

What it is useful for

The repository is most useful when an engineer needs to understand how an agent design works before committing to one. Its “2026 core patterns” section provides a practical sequence: start with the One-Step Decision Router to determine whether an agent is needed, study the while-loop agent, prefer a gated workflow where appropriate, then move into tool design, context engineering, trace-based evaluation, human approval, plan-then-execute, reflection, and thinking budgets.

It also contains domain examples that can serve as design references. These include a Document Intake Agent that turns office documents into LLM-ready Markdown and grounds answers, a customer-support agent with query categorization and sentiment analysis, a contract-analysis assistant, an E2E testing agent, and a self-healing-codebase example. The newer pattern notebooks include planted-answer scoring and report what a pattern does not fix, making them more useful as engineering references than as isolated demonstrations.

This is a collection of independent tutorials, not one cohesive agent framework. Examples span LangChain, LangGraph, PydanticAI, MCP, AutoGen, and OpenAI Swarm, so engineers should treat each notebook as a separate implementation with its own assumptions rather than expecting a single API across the repository.

How engineers can use it

Begin with the README’s “Start here” table and select the smallest pattern that matches the problem. For example, a deterministic contract-review process can start with the gated workflow notebook, while an iterative research flow can move from the agent loop to tool design, context management, planning, and reflection. Replace the sample domain with your own data, tools, and success cases.

For production-oriented work, use Trace-Based Agent Evaluation to examine execution traces and the Human-in-the-Loop Approval Agent as a reference for introducing an approval checkpoint. Validate the adapted implementation with your own test cases rather than assuming the notebook’s example output transfers to your environment.

Setup is not centrally documented in the supplied README, so follow the selected notebook’s instructions and provide the API keys it requires; there is no documented one-command setup to reproduce here. The README says most use-case notebooks now import with LangChain 1.x and LangGraph 1.x and use current model IDs, but each notebook still needs its own keys and not every notebook was run end to end against live services. The supplied GitHub metadata also reports the license as NOASSERTION, so confirm reuse terms before incorporating code into a product.