# NirDiamant/agents-towards-production

End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.

Repository: https://github.com/NirDiamant/agents-towards-production
Canonical: https://ross.abutalabs.com/products/agents-towards-production
Homepage: https://diamant-ai.com
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: agent, agent-framework, agents, ai-agents, genai, generative-ai, llm, llms, mlops, production, tutorials, deployment, langgraph, python, agentic-ai, mcp, multi-agent-systems, observability, rag
Last push: 2026-08-15T00:52:10+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 35, longevity 31
- inputs: {"age_days": 443, "days_push": 19, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 21334, forks 2835 (observed 2026-08-28T04:11:31.605654+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: agent-framework, rag, llm-inference, mcp, monitoring, deployment, developer-tools
- domain: artificial-intelligence, large-language-models, tutorials, developer-tools
- platform: python, cross-platform
- tags: genai, tutorials, jupyter-notebooks, langgraph, multi-agent-systems, production-deployment, guardrails, observability, mlops, ai-agents, retrieval-augmented-generation, devops, docker

## Member repositories
- NirDiamant/agents-towards-production (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:31.605654+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:58:08.209058+00:00, confidence not recorded.
  - readme: https://github.com/NirDiamant/agents-towards-production (fetched 2026-08-28T04:11:31.605654+00:00, sha f0c409c5c6c6)
  - homepage: https://diamant-ai.com (fetched 2026-08-29T07:56:47.919301+00:00, sha b674eb24f089)
- Data as of 2026-08-30T08:39:29.467469+00:00.