agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
- 21.8k estrellas
- 2.9k forks
- 5 issues abiertos
- Lenguaje: Jupyter Notebook
- Licencia: NOASSERTION
- actualizado
Instalación
git clone https://github.com/NirDiamant/agents-towards-production && cp agents-towards-production/*.md ~/.claude/agents/- Clone the repository and copy the agent .md definitions into ~/.claude/agents (or .claude/agents inside a project).
- Start a new Claude Code session to load the agents.
- Delegate work to them with the Task/Agent tool or by name.
Nota editorial
Agents Towards Production is a collection of 28 Jupyter Notebook tutorials teaching developers how to build and ship GenAI agents in real-world environments. The tutorials cover LangGraph stateful workflows, Redis-backed vector memory, real-time web search API integration, Docker and FastAPI deployment, security guardrails, GPU scaling, browser automation, fine-tuning, multi-agent coordination, LLM observability, evaluation pipelines, and UI development. Claude and other LLMs connect through standard APIs and MCP, with several tutorials addressing MCP-based agent tooling directly. Sponsor-contributed notebooks from LangChain, Redis, and Contextual AI provide worked examples that go beyond toy demos, including a dedicated RAG tutorial using Contextual AI's retrieval stack. The repository targets ML engineers and backend developers who need to move agents past the prototype stage into containerized, monitored, production deployments. A notable detail is that each tutorial is code-first and self-contained inside a notebook, making the progression from local experimentation to enterprise infrastructure traceable step by step.
Resumen de Subagents
Agents Towards Production
The open-source playbook for turning AI agents into real-world products.
Agents Towards Production is your go‑to resource for building production‑ready GenAI agents that scale from prototype to enterprise. Tutorials cover stateful workflows, vector memory, real‑time web search APIs, Docker deployment, FastAPI endpoints, security guardrails, GPU scaling, browser automation, fine‑tuning, multi‑agent coordination, observability, evaluation, and UI development.
⭐ If you find value in this project, PLEASE STAR IT to help others discover these tutorials!
📖 The companion book
AI Agents Made Simple - the decision guide to building agents that work: 28 chapters on what each pattern does, what breaks, what it costs, and when it is overkill. No code.
28 production-grade tutorials covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.
💎 Tutorial Sponsors
Companies that have contributed step-by-step tutorials to this repository. Click a logo to open the tutorial. Use Ctrl‑/⌘‑click to keep this page open.
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srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_langchain_white.png"> height="44" style="max-width:180px;" alt="LangChain - AI agent framework and workflow orchestration platform for building production-ready language model applications"> Agent Framework & Workflows
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_Redis_white.svg"> height="44" style="max-width:180px;" alt="Redis - In-memory database and vector storage for AI agent memory, caching, and real-time data processing"> Memory & Vector Database
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_contextual_white.png"> height="44" style="max-width:180px;" alt="Contextual AI - Production-ready RAG platform for building enterprise-grade retrieval augmented generation systems"> RAG & Knowledge Management
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_brightdata_white.svg"> height="44" style="max-width:180px;" alt="Bright Data - Web scraping and data collection platform for AI training and agent data gathering"> Web Data Platform
style="table-layout:fixed; width:100%; margin-top:16px; border-collapse:collapse;">
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_tavily_white.png"> height="44" style="max-width:180px;" alt="Tavily - Real-time web search API for AI agents with intelligent content extraction and summarization"> Real‑time Web Search API
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_arcade_white_tight.png"> height="44" style="max-width:180px;" alt="Arcade - Multi-user tool calling platform for secure OAuth2 authentication and human-in-the-loop safety controls"> MCP Runtime
srcset="assets/repos_images/sponsors_logos/trimmed_padded/trimmed_padded_jetbrains_white.svg"> height="44" style="max-width:180px;" alt="JetBrains - Creator of Kotlin and the Koog AI agent framework for building intelligent applications on the JVM"> Kotlin AI Agent Framework
Lo que la gente pregunta sobre agents-towards-production
¿Qué es NirDiamant/agents-towards-production?
NirDiamant/agents-towards-production es subagents para el ecosistema de Claude AI. End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment. Tiene 21.8k estrellas en GitHub y su última actualización registrada es del 2026-10-22.
¿Cómo se instala agents-towards-production?
Puedes instalar agents-towards-production clonando el repositorio (https://github.com/NirDiamant/agents-towards-production) o siguiendo las instrucciones del README en GitHub. ClaudeWave también te ofrece bloques de instalación rápida en esta misma página.
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¿Quién mantiene NirDiamant/agents-towards-production?
NirDiamant/agents-towards-production es mantenido por NirDiamant. La última actividad registrada en GitHub es del 2026-10-22, con 5 issues abiertos.
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