{"data":{"slug":"nirdiamant-agents-towards-production","name":"agents-towards-production","tagline":"End-to-end, code-first tutorials for building production-grade GenAI agents","github_url":"https://github.com/NirDiamant/agents-towards-production","owner":"NirDiamant","repo":"agents-towards-production","owner_avatar_url":"https://avatars.githubusercontent.com/u/28316913?v=4","primary_language":"Jupyter Notebook","stars":21298,"forks":2824,"topics":["agent","agent-framework","agentic-ai","agents","ai-agents","deployment","genai","generative-ai","langgraph","llm","llms","mcp","mlops","multi-agent-systems","observability","production","python","rag","tutorials"],"archived":false,"github_pushed_at":"2026-08-15T00:52:10+00:00","maintenance_label":"Very active","stars_delta_30d":191,"url":"https://www.graphcanon.com/tools/nirdiamant-agents-towards-production","markdown_url":"https://www.graphcanon.com/tools/nirdiamant-agents-towards-production.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nirdiamant-agents-towards-production","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nirdiamant-agents-towards-production","description":"End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.","homepage_url":"https://diamant-ai.com","license":"Other","open_issues":15,"watchers":233,"ai_summary":"Provides comprehensive guides on transforming AI agent prototypes into enterprise-ready systems, covering deployment strategies including containerization, cloud services (AWS Bedrock), on-prem solutions with Ollama, and scalable GPU infrastructure.","readme_excerpt":"### ๐ Deployment\n\n\n \n | Tutorial | \n Description | \n View | \n
\n \n AWS Bedrock AgentCore: Managed Agent Deployment ![](\"https://img.shields.io/badge/NEW-brightgreen\") | \n 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. | \n \n ![](\"https://img.shields.io/badge/GitHub-View-blue\") \n | \n
\n \n | Containerizing Agents with Docker | \n Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments. | \n \n \n | \n
\n \n | On-Prem LLM Deployment with Ollama | \n Run and interact with large language models locally. Replace cloud APIs with on-prem models for privacy, cost control, and low-latency agent workflows. | \n \n \n | \n
\n
\n\n---\n\n### ๐ GPU Deployment\n\n\n \n | Tutorial | \n Description | \n View | \n
\n \n | Scalable GPU Deployment for AI Agents (Runpod) | \n Deploy AI agents on scalable GPU infrastructure. Learn to set up cost-effective, high-performance environments for demanding agent workloads. | \n \n \n | \n
\n
\n\n---\n\n## ๐ Getting Started\n\nTransform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations.\n\n---\n\n## ๐ License\n\nThis project is licensed under a custom non-commercial license - see the [LICENSE](LICENSE) file for details.\n\n---\n\n\n\n๐ *Go deeper on RAG: **[RAG Made Simple](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=agents-towards-production--readme&click=book-buy-amazon-rag-cta&target=https%3A%2F%2Fwww.amazon.com%2Fdp%2FB0GWDH8JJL%3Fmaas%3Dmaas_adg_api_580351185840460851_static_9_573%26ref_%3Daa_maas%26tag%3Dmaas%26aa_campaignid%3Drag-en%26aa_adgroupid%3Dgithub-readme%26aa_creativeid%3Dbook-section&retarget=0&text=book-buy-amazon-rag-cta)** by the author of this repo (Amazon bestseller, โญ 4.6) ยท [read Chapter 1 free](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=agents-towards-production--readme&click=free-chapter&target=https%3A%2F%2Fdiamant-ai.com%2Frag-made-simple%2Fchapter-1&retarget=0&text=free-chapter).*","github_created_at":"2025-06-16T17:33:44+00:00","created_at":"2026-07-07T17:36:31.518669+00:00","updated_at":"2026-08-18T06:01:53.102053+00:00","categories":[{"slug":"ai-agents","name":"AI Agents","url":"https://www.graphcanon.com/categories/ai-agents","markdown_url":"https://www.graphcanon.com/categories/ai-agents.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/ai-agents"}],"tags":[{"slug":"agent-framework","name":"agent-framework"},{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"deployment","name":"deployment"},{"slug":"genai","name":"genai"},{"slug":"langgraph","name":"langgraph"},{"slug":"llms","name":"llms"},{"slug":"multi-agent-systems","name":"multi-agent-systems"},{"slug":"observability","name":"observability"}],"trust":{"provenance":{"is_fork":false,"github_id":1003143578,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-18T06:01:52.443Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":3,"last_release_at":null,"stars_delta_30d":191,"open_issues_delta_30d":4},"security_summary":{"status":"no_manifest","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:07:38.277Z","medium_count":0,"scan_profile":"mcp_manifest","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-18T06:01:52.856Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-18T06:01:52.856Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-08-18T06:01:52.856Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["* When you aim to deploy AI agents using cloud services such as AWS Bedrock AgentCore Runtime, where automatic infrastructure management and standardized communication patterns are key.","* If on-premises deployment of large language models (LLMs) is desired for reasons like privacy, cost control, or low-latency requirements with the use of Ollama.","* When scalability with GPU resources is a requirement for supporting demanding AI agent workloads at minimal costs, using Runpodโs scalable GPU infrastructure."],"when_not_to_use":["* If your enterprise strictly forbids using cloud services; this tool emphasizes both cloud and on-prem deployment strategies but may not fit entirely on-prem infrastructures.","* When you are looking for a fully managed service without code-first or tutorial-guided approaches, as 'agents-towards-production' focuses heavily on hands-on tutorials and end-to-end guide creation.","* If your specific AI agent workload does not align with the foundational deployment patterns covered (containerization, AWS Bedrock, Ollama on-prem solutions, Runpod GPU deployment), other tools may,","other_remarks_and_conditions_of_use_or_nonuse_examples_with_links_or_code_snippets_e.g_github_issues__pull_requests__branch_names_etc_that_affect_anyoftheabove_can_be_cited_if_pertinent."],"source":"enrich:decision_facts","observed_at":"2026-07-11T14:11:21.582Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide includes best-pr"}]}}