agents-towards-production
End-to-end, code-first tutorials for building production-grade GenAI agents
GraphCanon updated 3d · GitHub synced 3d
Decision brief
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
Good fit when
- * 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.
Avoid when
- * 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.
Observed Jul 11, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (3d since push)
- As of 3d
- Provenance
- Not a fork · Personal account
- As of 3d
- Security (OSV)
- No MCP manifest
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/NirDiamant/agents-towards-productionHow it fits your stack(19)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Successor
Integrates
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 18, 2026
Categories
Graph entities
Tags
README
🚀 Deployment
| Tutorial | Description | View |
|---|---|---|
AWS Bedrock AgentCore: Managed Agent Deployment  | 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. |
|
| Containerizing Agents with Docker | Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments. |
|
| On-Prem LLM Deployment with Ollama | Run and interact with large language models locally. Replace cloud APIs with on-prem models for privacy, cost control, and low-latency agent workflows. |
|
🚀 GPU Deployment
| Tutorial | Description | View |
|---|---|---|
| Scalable GPU Deployment for AI Agents (Runpod) | Deploy AI agents on scalable GPU infrastructure. Learn to set up cost-effective, high-performance environments for demanding agent workloads. |
|
🚀 Getting Started
Transform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations.
📜 License
This project is licensed under a custom non-commercial license - see the LICENSE file for details.
📖 Go deeper on RAG: RAG Made Simple by the author of this repo (Amazon bestseller, ⭐ 4.6) · read Chapter 1 free.
For agents
This page has a .md twin and JSON over the API.