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.
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Agent Framework & Workflows 
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Memory & Vector Database
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Real‑time Web Search API
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Web Data Platform
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GPU Cloud Computing
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Agent Orchestration Platform
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Security & Observability
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Agentic Browser Automation
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Agents Towards Production is your hands-on guide to every building block of a GenAI agent stack.
All 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.
| Tutorial-first learning | Every topic comes with a practical walkthrough you can run locally |
| Full lifecycle coverage | All the capabilities required to take agents from prototype to production |
| 🚀 GPU Deployment | Deploy to scalable GPU infrastructure for high-performance agent workloads |
| 🔍 Real-Time Monitoring | Gain end-to-end tracing, monitoring, and debugging for agent workflows |
| 🔌 Tool Integration | Connect agents to real-time web data, databases, and external APIs |
| 🧠 Memory | Implement both short- and long-term stores with semantic search |
| 🔄 Orchestration | Design multi-tool, memory-aware workflows and agent-to-agent messaging |
| 🔒 Security | Apply real-time guardrails and injection defenses |
| 🧩 Agent Frameworks | Create stateful graphs, expose agents as REST endpoints, and package reusable tools |
| 🚀 Deployment | Ship to containers and on-prem servers with containerization patterns |
| 🛠️ Model Customization | Fine-tune language models for specialized agent behavior and domain expertise |
| 👥 Multi-agent Coordination | Enable message passing and shared planning |
| 🔍 Tracing & Debugging | Add comprehensive observability to debug and improve agent performance |
| 📊 Evaluation | Automate behavioral testing and metric tracking |
| 🖥️ UI & Frontend | Build chat or dashboard front-ends in minutes |
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| Browser Automation for AI Agents (Anchor Browser) | Enable agents to interact with web applications through browser automation. Learn to extract data from dashboards, automate form filling, and navigate complex web interfaces using cloud-hosted browsers. |
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| Real-Time Web Data Integration for Agents (Tavily) | 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. |
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| Agent Observability: Tracing, Monitoring & Debugging (Qualifire) | Gain end-to-end tracing, real-time monitoring, and debugging for agent workflows. Learn to capture logs, traces, and quality metrics for troubleshooting and optimization. |
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| Agent Memory: Dual-Memory & Semantic Search (Redis) | Implement dual-memory (short-term and long-term), semantic search, and persistent state for agents that remember user preferences and learn from conversations. |
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| 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. |
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| Agent Orchestration: Multi-Tool, Memory & Messaging Workflows (xpander.ai) | Learn to orchestrate tools, memory, multi-user state, and agent-to-agent messaging for production-ready AI agents. Example: Automate meeting recording and reporting workflows. |
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| Real-Time Security Guardrails for Agents (Qualifire) | Block prompt injections, hallucinations, unsafe content, and enforce security policies in real time. Learn to implement robust guardrails for agent safety. |
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| Comprehensive Agent Security (LlamaFirewall) | Apply comprehensive input, output, and tool security guardrails for agents. Covers prompt injection, behavior alignment, and tool access control. |
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| Hands-On Agent Security Evaluation (Apex) | Hands-on prompt injection attacks, defenses, and automated security testing for AI agents. |
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| Tool & API Integration via Model Context Protocol (MCP) | Integrate agents with external tools and APIs using a standardized protocol. Example: Seamless tool and API integration for advanced agent workflows. |
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| Stateful Agent Workflows with LangGraph | Design complex, stateful agent workflows using a directed graph architecture. Example: Multi-step text analysis pipeline with classification, entity extraction, and summarization. |
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| Deploying Agents as APIs with FastAPI | Create and deploy agents as performant APIs, supporting both synchronous and streaming endpoints. |
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| Containerizing Agents with Docker | Containerize agents for portability and scalability. Learn foundational patterns for running agents in containers across environments. |
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| 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. |
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| Fine-Tuning AI Agents for Domain Expertise & Efficiency | 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. |
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| Multi-Agent Communication with A2A Protocol | Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability. |
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| Agent Tracing & Debugging with LangSmith | Add comprehensive observability to AI systems. Capture detailed traces, decision points, and timing data to debug, monitor, and systematically improve agent performance. |
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| Automated Agent Evaluation & Behavioral Analysis (IntellAgent) | Automate agent evaluation with behavioral analysis, performance metrics, and actionable insights for improving agent quality. |
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| Tutorial | Description | View |
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| Building a Chatbot UI with Streamlit | Build a beginner-friendly chatbot web app with a chat interface, file upload, and session state for interactive agent demos. |
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Transform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations.
Explore 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.
Download the repository to run tutorials locally, experiment with configurations, customize implementations, and integrate proven patterns directly into your agent development workflow.
1. Get the Code
git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-production2. Install Dependencies Navigate to your target tutorial and set up the environment:
# Example: Multi-tool agent orchestration
cd tutorials/agentic-applications-by-xpander.ai
pip install -r meeting-recorder-agent/requirements.txt3. Deploy and Test Launch tutorials through their preferred interface:
# Run interactive notebooks for experimentation
jupyter notebook tutorial.ipynb
# Execute production scripts for integration testing
python app.pyWe 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.
Please see our Contributing Guidelines for more details.
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.
Your responsibility: Conduct due diligence, verify legitimacy, test in isolation, ensure legal compliance. Security tools require ethical use with proper authorization.
By using this repository, you agree to this disclaimer.
This project is licensed under a custom non-commercial license - see the LICENSE file for details.
