[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-73974":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":41,"readmeContent":42,"aiSummary":43,"trendingCount":16,"starSnapshotCount":16,"syncStatus":44,"lastSyncTime":45,"discoverSource":46},73974,"agents-towards-production","NirDiamant\u002Fagents-towards-production","NirDiamant","End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.","",null,"Jupyter Notebook",20696,2747,232,5,0,44,85,434,146,120,"Other",false,"main",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40],"agent","agent-framework","agents","ai-agents","deployment","genai","generative-ai","langgraph","llm","llms","mlops","production","python","tutorials","2026-06-12 04:01:12","\u003Cdiv align=\"center\">\n\n# Agents Towards Production\n\n### _The open-source playbook for turning AI agents into real-world products._\n\n**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.\n\n### ⭐ **If you find value in this project, PLEASE STAR IT to help others discover these tutorials!** \n\n\u003C!-- SEO Keywords: generative ai agents, production deployment, langgraph, langchain, rag, retrieval augmented generation, memory, observability, guardrails, gpu deployment, orchestration, multi agent, prompt engineering, tutorials, guide -->\n\n[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-Connect-blue)](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)\n[![Twitter](https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002FNirDiamantAI?label=Follow%20@NirDiamantAI&style=social)](https:\u002F\u002Ftwitter.com\u002FNirDiamantAI)\n[![Discord](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDiscord-Join%20our%20community-7289da?style=flat-square&logo=discord&logoColor=white)](https:\u002F\u002Fdiscord.gg\u002FcA6Aa4uyDX)\n[![Sponsor](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=ff69b4)](https:\u002F\u002Fgithub.com\u002Fsponsors\u002FNirDiamant)\n[![DiamantAI Collective is hiring](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%92%BC%20Hiring-DiamantAI%20Collective-7c3aed?style=flat-square)](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=main-readme--hiring-badge&click=top-badge-hiring&target=https%3A%2F%2Fdiamant-ai.com%2Fjobs&text=Hiring%20Badge)\n\n\u003C\u002Fdiv>\n\n---\n\n\u003Cdiv align=\"center\">\n\n## 📖 From the Same Author\n\n\u003Ca href=\"https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=agents-towards-production--readme&click=book-buy-amazon-image&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-atp-20&text=Best%20Seller%20Image\">\u003Cimg src=\"images\u002Frag_book_best_seller.png\" alt=\"#1 Best Seller in Generative AI on Amazon - Click to buy\" width=\"500\">\u003C\u002Fa>\n\n**[RAG Made Simple](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=agents-towards-production--readme&click=book-buy-amazon-title&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-atp-20&text=RAG%20Made%20Simple)** — **#1 Best Seller on Amazon in Generative AI.**\n22 RAG techniques with intuition, comparisons, and illustrations. **Free with Kindle Unlimited** or **$0.99** launch price (goes up soon).\n\n### 👉 [**Get the book on Amazon**](https:\u002F\u002Feurope-west1-rag-techniques-views-tracker.cloudfunctions.net\u002Frag-techniques-tracker?notebook=agents-towards-production--readme&click=book-buy-amazon-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0D76734SZ%3Ftag%3Ddiamantai-atp-20&text=Get%20the%20book%20on%20Amazon)\n\n\u003C\u002Fdiv>\n\n---\n\n\u003Cdiv align=\"center\">\n\n\n> **28 production-grade tutorials** covering stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, and more.\n\n\u003Cdiv align=\"center\">\n\n---\n\n\u003Cimg src=\"images\u002Fcollective-banner.png\" alt=\"DiamantAI Collective - AI engineering jobs\" width=\"600\">\n\n## 💼 Apply for open AI engineering jobs\n\n**AI-first companies are hiring through the DiamantAI Collective.**\n\n[![See open jobs and apply](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%E2%9E%A1%EF%B8%8F%20%20See%20open%20jobs%20and%20apply-7c3aed?style=for-the-badge)](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=main-readme--jobs-panel&click=jobs-panel-see-all-roles&target=https%3A%2F%2Fdiamant-ai.com%2Fjobs&text=See%20open%20jobs%20and%20apply)\n\n---\n\n\u003C\u002Fdiv>\n\n## 💎 Tutorial Sponsors\n\n\u003Cp align=\"center\">\u003Cem>\nCompanies that have contributed step-by-step tutorials to this repository.\u003Cbr>\nClick a logo to open the tutorial. Use Ctrl‑\u002F⌘‑click to keep this page open.\n\u003C\u002Fem>\u003C\u002Fp>\n\n\u003C!-- ─────────── 1st row – 4 sponsors ─────────── -->\n\u003Ctable align=\"center\" cellpadding=\"20\"\n       style=\"table-layout:fixed; width:100%; border-collapse:collapse;\">\n\u003Ctr align=\"center\" valign=\"top\">\n\n  \u003C!-- LangChain -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002FLangGraph-agent\" title=\"Open LangChain tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_langchain_white.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_langchain.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"LangChain - AI agent framework and workflow orchestration platform for building production-ready language model applications\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Agent Framework & Workflows\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Flangchain.com\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit LangChain AI agent framework website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- Redis -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fagent-memory-with-redis\" title=\"Open Redis tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_Redis_white.svg\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_Redis.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Redis - In-memory database and vector storage for AI agent memory, caching, and real-time data processing\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Memory & Vector Database\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fredis.io\u002Ftry-free\u002F?utm_source=nir&utm_medium=cpa&utm_campaign=2025-05-ai_in_production-influencer-nir&utm_content=sd-software_download-7013z000001WaRY\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Redis in-memory database and vector storage website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- Contextual AI -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fagent-RAG-with-Contextual\" title=\"Open Contextual AI tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_contextual_white.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_contextual_black.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Contextual AI - Production-ready RAG platform for building enterprise-grade retrieval augmented generation systems\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">RAG & Knowledge Management\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fapp.contextual.ai\u002F?utm_campaign=agents-towards-production&utm_source=diamantai&utm_medium=github&utm_content=notebook\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Contextual AI RAG platform website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- Bright Data -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fagent-with-brightdata\" title=\"Open Bright Data tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_brightdata_white.svg\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_brightdata.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Bright Data - Web scraping and data collection platform for AI training and agent data gathering\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Web Data Platform\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fbrightdata.com\u002Fai?utm_source=brand&utm_campaign=brnd-mkt_github_nirdiamant_logo\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Bright Data web scraping platform website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n  \n  \n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003C!-- ─────────── 2nd row – 3 sponsors ─────────── -->\n\u003Ctable align=\"center\" cellpadding=\"20\"\n       style=\"table-layout:fixed; width:100%; margin-top:16px; border-collapse:collapse;\">\n\u003Ctr align=\"center\" valign=\"top\">\n\n  \u003C!-- Tavily -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fagent-with-tavily-web-access\" title=\"Open Tavily tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_tavily_white.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_tavily.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Tavily - Real-time web search API for AI agents with intelligent content extraction and summarization\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Real‑time Web Search API\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fapp.tavily.com\u002Fhome\u002F?utm_source=github&utm_medium=referral&utm_campaign=nir_diamant\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Tavily real-time web search API website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- Arcade -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Farcade-secure-tool-calling\" title=\"Open Arcade tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_arcade_white_tight.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_arcade_black.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Arcade - Multi-user tool calling platform for secure OAuth2 authentication and human-in-the-loop safety controls\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">MCP Runtime\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fdocs.arcade.dev\u002Fen\u002Fhome?utm_source=github&utm_medium=paid_sponsorship&utm_campaign=agents_toward_prod&utm_content=readme_placement\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Arcade multi-user tool integration platform website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- JetBrains -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fkotlin-agent-with-koog\" title=\"Open JetBrains Koog tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_jetbrains_white.svg\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_jetbrains.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"JetBrains - Creator of Kotlin and the Koog AI agent framework for building intelligent applications on the JVM\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Kotlin AI Agent Framework\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fkotlinlang.org\u002F?utm_source=github&utm_medium=influencers&utm_campaign=kotlin_nir_supporter_1\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Kotlin website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n\u003C!-- ─────────── 3rd row – 3 sponsors ─────────── -->\n\u003Ctable align=\"center\" cellpadding=\"20\"\n       style=\"table-layout:fixed; width:100%; margin-top:16px; border-collapse:collapse;\">\n\u003Ctr align=\"center\" valign=\"top\">\n\n  \u003C!-- Mem0 -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Fagent-memory-with-mem0\" title=\"Open Mem0 tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002FMem0%20Word%20Logo.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002FMem0 Word Logo Dark.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"Mem0 - Self-improving memory system for AI agents with hybrid vector and graph storage\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">Self-Improving AI Memory\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fmem0.dev\u002Fgithub\u002Fnir\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit Mem0 AI memory platform website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n  \u003C!-- RunPod -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"tutorials\u002Frunpod-gpu-deploy\" title=\"Open RunPod tutorial\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_runpod_white.svg\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Ftrimmed_padded_runpod.svg\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"RunPod - GPU cloud computing platform for training and deploying AI models and agents at scale\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">GPU Cloud Computing\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fget.runpod.io\u002Fnirdiamant\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit RunPod GPU cloud computing website\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n## 💎 General Sponsors\n\n\u003Cp align=\"center\">\u003Cem>\nCompanies that support this project through partnerships and resources.\u003Cbr>\nClick a logo to visit their website.\n\u003C\u002Fem>\u003C\u002Fp>\n\n\u003C!-- ─────────── General sponsors ─────────── -->\n\u003Ctable align=\"center\" cellpadding=\"20\"\n       style=\"table-layout:fixed; width:100%; border-collapse:collapse;\">\n\u003Ctr align=\"center\" valign=\"top\">\n\n  \u003C!-- CodeRabbit -->\n  \u003Ctd width=\"200\" valign=\"bottom\">\n    \u003Ca href=\"https:\u002F\u002Fcoderabbit.link\u002Fnir\" title=\"Visit CodeRabbit\">\n      \u003Cpicture>\n        \u003Csource media=\"(prefers-color-scheme: dark)\"\n                srcset=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Fcoderabbit_Dark_Type_Mark.png\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fsponsors_logos\u002Ftrimmed_padded\u002Fcoderabbit_Light_Type_Mark_Orange.png\"\n             height=\"44\" style=\"max-width:180px;\" alt=\"CodeRabbit - AI-powered code review and automated pull request analysis\">\n      \u003C\u002Fpicture>\n    \u003C\u002Fa>\u003Cbr>\n    \u003Csub>\u003Cspan style=\"white-space:nowrap;\">AI Code Review\u003C\u002Fspan>\u003Cbr>\n      \u003Ca href=\"https:\u002F\u002Fcoderabbit.link\u002Fnir\">\n        \u003Cimg src=\"assets\u002Frepos_images\u002Fvisit-site-badge.svg\" width=\"56\" height=\"16\" alt=\"Visit CodeRabbit AI code review platform\">\n      \u003C\u002Fa>\n    \u003C\u002Fsub>\n  \u003C\u002Ftd>\n\n\u003C\u002Ftable>\n\n\u003Cdiv align=\"center\">\n\n### 💎 Become a Sponsor\n\n**Get in touch:**\n\n[![Website](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FWebsite-DiamantAI.com-green?style=for-the-badge&logo=globe)](https:\u002F\u002Fwww.diamant-ai.com\u002F)\n[![LinkedIn](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-Connect-0077B5?style=for-the-badge&logo=linkedin)](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnir-diamant-759323134\u002F)\n\n\u003C\u002Fdiv>\n\n\u003C\u002Fdiv>\n\n\n\n\u003Cdiv align=\"center\">\n\n## 📫 Stay Updated!\n\n\u003Ctable>\n\u003Ctr>\n\u003Ctd align=\"center\" style=\"padding:15px;background-color:#f8f9fa;border-right:1px solid #eaecef\">🚀\u003Cbr\u002F>\u003Cb>Cutting-edge\u003Cbr\u002F>Updates\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\" style=\"padding:15px;background-color:#f8f9fa;border-right:1px solid #eaecef\">💡\u003Cbr\u002F>\u003Cb>Expert\u003Cbr\u002F>Insights\u003C\u002Fb>\u003C\u002Ftd>\n\u003Ctd align=\"center\" style=\"padding:15px;background-color:#f8f9fa\">🎯\u003Cbr\u002F>\u003Cb>Top 0.1%\u003Cb>Content\u003C\u002Fb>\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftable>\n\n[![Subscribe to DiamantAI Newsletter](assets\u002Frepos_images\u002Fsubscribe-button.svg)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n_Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials!_  \n**_Plus, subscribers get exclusive early access and special 33% discounts to my book and upcoming courses!_**\n\n[![DiamantAI's newsletter](assets\u002Frepos_images\u002Fsubstack_image.png)](https:\u002F\u002Fdiamantai.substack.com\u002F?r=336pe4&utm_campaign=pub-share-checklist)\n\n\u003C\u002Fdiv>\n\n---\n\n\u003Cdiv align=\"center\">\n\n## 💬 Join Our Community\n\nStay connected with the latest in GenAI and agent development:\n\n### r\u002FEducationalAI\n\n[![Reddit](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FReddit-Join%20r\u002FEducationalAI-ff4500?style=for-the-badge&logo=reddit&logoColor=white)](https:\u002F\u002Fwww.reddit.com\u002Fr\u002FEducationalAI\u002F)\n\n_Join our growing community discussing cutting-edge AI research, agent development, and production insights!_\n\n\u003C\u002Fdiv>\n\n---\n\n\n---\n\n## ✨ Introduction\n**Agents Towards Production** is your hands-on guide to every building block of a GenAI agent stack.  \nAll knowledge is delivered through runnable tutorials covering orchestration, memory, observability, deployment, security, and more. Each tutorial lives in its own folder with ready-to-run notebooks or code files, so you can move from concept to working agent in minutes.\n\n---\n\n## 🏗️ AI Agent Architecture\n\n\u003Cdiv align=\"center\">\n\n![AI Agent Architecture - Production-ready AI agent development workflow showing orchestration, memory, tools, security, observability, evaluation, and deployment components](assets\u002Frepos_images\u002Fai_architecture_diagram.svg)\n\n*This diagram shows the flow of building a production-level agent. The tutorials in this repository cover each of these components step-by-step.*\n\n\u003C\u002Fdiv>\n\n---\n\n## 📚 Tutorials\n\n### 🔌 Tool Integration\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Secure Tool Calling (Arcade) \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNEW-brightgreen\" height=\"16\">\u003C\u002Ftd>\n    \u003Ctd>Enable agents to securely call external tools (Gmail, Slack, Notion) with OAuth2 authentication and human-in-the-loop safety controls. Learn production-ready tool integration with user isolation and approval workflows.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"tutorials\u002Farcade-secure-tool-calling\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 📊 Data Processing\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Web Data Collection for AI Agents (Bright Data) \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNEW-brightgreen\" height=\"16\">\u003C\u002Ftd>\n    \u003Ctd>Build agents that collect and process web data at scale using enterprise-grade scraping infrastructure. Learn to integrate proxy networks, handle CAPTCHAs, and extract structured data from complex websites.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-with-brightdata\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Real-Time Web Data Integration for Agents (Tavily)\u003C\u002Ftd>\n    \u003Ctd>Enable agents to access, search, and extract real-time web data. Build workflows that combine live web information with private knowledge for research, monitoring, and up-to-date recommendations.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-with-tavily-web-access\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🔍 RAG & Knowledge Management\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Production-Ready RAG Agents with Contextual AI (Contextual AI)\u003C\u002Ftd>\n    \u003Ctd>Build enterprise-grade RAG systems in 15 minutes using Contextual AI's managed platform. Learn document processing, intelligent indexing, agent deployment, and automated evaluation with LMUnit testing framework for financial document analysis.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"tutorials\u002Fagent-RAG-with-Contextual\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🧠 Memory\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Agent Memory: Dual-Memory & Semantic Search (Redis)\u003C\u002Ftd>\n    \u003Ctd>Implement dual-memory (short-term and long-term), semantic search, and persistent state for agents that remember user preferences and learn from conversations.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-memory-with-redis\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Self-Improving Memory with Mem0: Hybrid Vector & Graph Storage \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNEW-brightgreen\" height=\"16\">\u003C\u002Ftd>\n    \u003Ctd>Build intelligent agents with self-improving memory that automatically extracts insights, resolves conflicts, and evolves with each interaction. Learn hybrid memory architecture combining vector search for semantic recall and graph databases for relationship mapping.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-memory-with-mem0\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>AI Memory with Cognee\u003C\u002Ftd>\n    \u003Ctd>Build intelligent AI memory systems that learn from Python's creator and improve your development workflow. Transform scattered development data into unified knowledge graphs with contextual insights.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fai-memory-with-cognee\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🚀 Deployment\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>AWS Bedrock AgentCore: Managed Agent Deployment \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNEW-brightgreen\" height=\"16\">\u003C\u002Ftd>\n    \u003Ctd>Deploy and manage AI agents on AWS Bedrock AgentCore Runtime. Learn to transform local agents into production-ready managed services with automatic infrastructure, request tracking, and standardized communication patterns.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Faws_agentcore\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Containerizing Agents with Docker\u003C\u002Ftd>\n    \u003Ctd>Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fdocker-intro\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>On-Prem LLM Deployment with Ollama\u003C\u002Ftd>\n    \u003Ctd>Run and interact with large language models locally. Replace cloud APIs with on-prem models for privacy, cost control, and low-latency agent workflows.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fon-prem-llm-ollama\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 👥 Multi-agent Coordination\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n\n  \u003Ctr>\n    \u003Ctd>Multi-Agent Communication with A2A Protocol\u003C\u002Ftd>\n    \u003Ctd>Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fa2a\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🚀 GPU Deployment\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Scalable GPU Deployment for AI Agents (Runpod)\u003C\u002Ftd>\n    \u003Ctd>Deploy AI agents on scalable GPU infrastructure. Learn to set up cost-effective, high-performance environments for demanding agent workloads.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Frunpod-gpu-deploy\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🔒 Security\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Comprehensive Agent Security (LlamaFirewall)\u003C\u002Ftd>\n    \u003Ctd>Apply comprehensive input, output, and tool security guardrails for agents. Covers prompt injection, behavior alignment, and tool access control.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-security-with-llamafirewall\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Hands-On Agent Security Evaluation (Apex)\u003C\u002Ftd>\n    \u003Ctd>Hands-on prompt injection attacks, defenses, and automated security testing for AI agents.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"tutorials\u002Fagent-security-apex\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 👥 Multi-agent Coordination\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n\n  \u003Ctr>\n    \u003Ctd>Multi-Agent Communication with A2A Protocol\u003C\u002Ftd>\n    \u003Ctd>Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fa2a\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🧩 Agent Frameworks\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Tool & API Integration via Model Context Protocol (MCP)\u003C\u002Ftd>\n    \u003Ctd>Integrate agents with external tools and APIs using a standardized protocol. Example: Seamless tool and API integration for advanced agent workflows.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-with-mcp\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Stateful Agent Workflows with LangGraph\u003C\u002Ftd>\n    \u003Ctd>Design complex, stateful agent workflows using a directed graph architecture. Example: Multi-step text analysis pipeline with classification, entity extraction, and summarization.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002FLangGraph-agent\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Deploying Agents as APIs with FastAPI\u003C\u002Ftd>\n    \u003Ctd>Create and deploy agents as performant APIs, supporting both synchronous and streaming endpoints.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Ffastapi-agent\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Building AI Agents in \u003Ca href=\"https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=kotlinlang&target=https%3A%2F%2Fkotlinlang.org%2F%3Futm_source%3Dgithub%26utm_medium%3Dinfluencers%26utm_campaign%3Dkotlin_nir_supporter_1&text=Kotlin\">Kotlin\u003C\u002Fa> with Koog \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FNEW-brightgreen\" height=\"16\">\u003C\u002Ftd>\n    \u003Ctd>Build your first AI agent in \u003Ca href=\"https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=kotlinlang&target=https%3A%2F%2Fkotlinlang.org%2F%3Futm_source%3Dgithub%26utm_medium%3Dinfluencers%26utm_campaign%3Dkotlin_nir_supporter_1&text=Kotlin\">Kotlin\u003C\u002Fa> using JetBrains' Koog framework. Step-by-step from hello world to tool calling and structured output in under 30 minutes.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fkotlin-agent-with-koog\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🛠️ Model Customization\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Fine-Tuning AI Agents for Domain Expertise & Efficiency\u003C\u002Ftd>\n    \u003Ctd>Learn how to fine-tune language models for specialized agent behavior, domain expertise, and efficient, cost-effective responses. Covers data preparation, training, evaluation, and integration into agent workflows.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Ffine-tuning-agents\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🔍 Tracing & Debugging\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Agent Tracing & Debugging with LangSmith\u003C\u002Ftd>\n    \u003Ctd>Add comprehensive observability to AI systems. Capture detailed traces, decision points, and timing data to debug, monitor, and systematically improve agent performance.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Ftracing-with-langsmith\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 📊 Evaluation\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Automated Agent Evaluation & Behavioral Analysis (IntellAgent)\u003C\u002Ftd>\n    \u003Ctd>Automate agent evaluation with behavioral analysis, performance metrics, and actionable insights for improving agent quality.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-evaluation-intellagent\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n### 🖥️ UI & Frontend\n\n\u003Ctable width=\"100%\">\n  \u003Ctr style=\"background-color:#f8f9fa\">\n    \u003Cth width=\"30%\">Tutorial\u003C\u002Fth>\n    \u003Cth width=\"50%\">Description\u003C\u002Fth>\n    \u003Cth width=\"20%\">View\u003C\u002Fth>\n  \u003C\u002Ftr>\n  \u003Ctr>\n    \u003Ctd>Building a Chatbot UI with Streamlit\u003C\u002Ftd>\n    \u003Ctd>Build a beginner-friendly chatbot web app with a chat interface, file upload, and session state for interactive agent demos.\u003C\u002Ftd>\n    \u003Ctd align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production\u002Ftree\u002Fmain\u002Ftutorials\u002Fagent-with-streamlit-ui\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGitHub-View-blue\" height=\"20\">\u003C\u002Fa>\n    \u003C\u002Ftd>\n  \u003C\u002Ftr>\n\u003C\u002Ftable>\n\n---\n\n## 🚀 Getting Started\n\nTransform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations.\n\n### 📖 Browse Online\nExplore tutorials directly on GitHub to understand production-grade implementations, architectural decisions, and integration patterns. Each tutorial includes comprehensive documentation and code that you can study and adapt to your specific requirements without any local setup.\n\n### 🛠️ Clone and Build\nDownload the repository to run tutorials locally, experiment with configurations, customize implementations, and integrate proven patterns directly into your agent development workflow.\n\n\u003Cdiv align=\"left\">\n\n#### Quick Setup\n\n**1. Get the Code**\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FNirDiamant\u002Fagents-towards-production.git\ncd agents-towards-production\n```\n\n**2. Install Dependencies**\nNavigate to your target tutorial and set up the environment:\n\n```bash\n# Example: Multi-tool agent orchestration\ncd tutorials\u002Fagentic-applications-by-xpander.ai\npip install -r meeting-recorder-agent\u002Frequirements.txt\n```\n\n**3. Deploy and Test**\nLaunch tutorials through their preferred interface:\n\n```bash\n# Run interactive notebooks for experimentation\njupyter notebook tutorial.ipynb\n\n# Execute production scripts for integration testing\npython app.py\n```\n\n\u003C\u002Fdiv>\n\n---\n\n## 📚 Recommended reading\n\n*This list contains Amazon affiliate links. As an Amazon Associate I earn from qualifying purchases. Every book below is one I've read and genuinely recommend to engineers working in this space. The companion book to this repo is featured separately at the top of this README.*\n\n- [Build a Large Language Model (From Scratch)](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=amazon-product&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2F1633437167%3Ftag%3Ddiamantai-atp-20&text=Build%20a%20Large%20Language%20Model%20%28From%20Scratch%29) by Sebastian Raschka. Build a GPT-style model end to end in PyTorch.\n- [AI Engineering: Building Applications with Foundation Models](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=amazon-product&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2F1098166302%3Ftag%3Ddiamantai-atp-20&text=AI%20Engineering%3A%20Building%20Applications%20with%20Foundation%20Models) by Chip Huyen. Canonical reference for productionizing foundation-model apps.\n- [Hands-On Large Language Models](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=amazon-product&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2F1098150961%3Ftag%3Ddiamantai-atp-20&text=Hands-On%20Large%20Language%20Models) by Jay Alammar and Maarten Grootendorst. Visual, practical LLM walkthroughs.\n- [Natural Language Processing with Transformers](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=amazon-product&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2F1098136799%3Ftag%3Ddiamantai-atp-20&text=Natural%20Language%20Processing%20with%20Transformers) by Lewis Tunstall, Leandro von Werra, and Thomas Wolf. From the Hugging Face team.\n- [Designing Machine Learning Systems](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=agents-towards-production--readme&click=amazon-product&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2F1098107969%3Ftag%3Ddiamantai-atp-20&text=Designing%20Machine%20Learning%20Systems) by Chip Huyen. ML systems in production, still the standard reference.\n\n## 🤝 Contributing\n\nWe welcome contributions of tools, infrastructure, and frameworks that support agent development. This includes monitoring, deployment platforms, security tools, databases, APIs, and other horizontal services that enable production agent systems.\n\nPlease see our [Contributing Guidelines](CONTRIBUTING.md) for more details.\n\n---\n\n## ⚠️ Disclaimer\n\n**Educational use only.** Authors disclaim all responsibility for use, misuse, or consequences. We do not endorse, verify, or guarantee third-party companies, tools, or services referenced herein. Not liable for damages, losses, security breaches, or fraudulent activities by referenced parties.\n\n**Your responsibility:** Conduct due diligence, verify legitimacy, test in isolation, ensure legal compliance. Security tools require ethical use with proper authorization.\n\nBy using this repository, you agree to this disclaimer.\n\n---\n\n## 📜 License\n\nThis project is licensed under a custom non-commercial license - see the [LICENSE](LICENSE) file for details.\n\n---\n\n\u003Cdiv align=\"center\">\n\n### ⭐️ If you find this repository helpful, please consider giving it a star!\n\n\u003Cbr>\n\n![](https:\u002F\u002Feurope-west1-atp-views-tracker.cloudfunctions.net\u002Fworking-analytics?notebook=main-readme)\n\n\n\u003Cp>\u003Ci>Keywords: AI Agents, Production Deployment, LLM, Orchestration, Multi-agent Systems, Memory Systems, Monitoring, Security, Observability, Agent Frameworks, Infrastructure, Serverless, Enterprise AI, Tool Integration\u003C\u002Fi>\u003C\u002Fp>\n\n\u003C\u002Fdiv>\n","Agents Towards Production 是一个专注于构建生产级生成式AI代理的端到端教程项目，从原型设计到企业部署全流程覆盖。该项目提供了包括状态工作流、向量记忆、实时网络搜索API集成、Docker部署、FastAPI接口创建、安全防护措施、GPU扩展、浏览器自动化、微调、多代理协调、可观察性及用户界面开发等在内的多种技术指导，采用Jupyter Notebook编写，便于理解和实践。适用于希望将AI代理应用于实际产品中的开发者和团队，特别是那些寻求提升其解决方案成熟度并实现规模化应用的企业。",2,"2026-06-11 03:48:12","high_star"]