NirDiamant/agents-towards-production resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

21334 stars · 2835 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of code-first Jupyter notebook tutorials for building production-grade generative AI agents, covering topics like stateful workflows, memory, guardrails, multi-agent coordination, observability, and deployment. It serves as an open-source playbook for taking GenAI agent prototypes to enterprise-scale production.

Use cases

• learn how to build production-ready AI agents
• deploy LLM agents with Docker and FastAPI
• add guardrails and security to GenAI agents
• implement multi-agent coordination with LangGraph
• add observability and evaluation to AI agent systems
• scale GenAI agents on GPUs
• build RAG agents with vector memory and web search tools

When to choose

• you want hands-on, code-first tutorials for shipping GenAI agents to production
• you need practical patterns for deployment, observability, and guardrails for LLM agents
• you are learning LangGraph, MCP, or multi-agent system design in Python

When to avoid

• you need a ready-made production agent framework or library rather than educational notebooks
• you are not working in Python or the LLM/GenAI ecosystem
• you need a maintained software dependency with a standard open-source license

Facets

learning-resource · maturity active

agent-framework rag llm-inference mcp monitoring deployment developer-tools artificial-intelligence large-language-models tutorials developer-tools python cross-platform genai tutorials jupyter-notebooks langgraph multi-agent-systems production-deployment guardrails observability mlops ai-agents retrieval-augmented-generation devops docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NirDiamant/agents-towards-production")