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# Agents Towards Production ### _The open-source playbook for turning AI agents into real-world products._ [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/in/nir-diamant-759323134/) [![Twitter](https://img.shields.io/twitter/follow/NirDiamantAI?label=Follow%20@NirDiamantAI&style=social)](https://twitter.com/NirDiamantAI) [![Discord](https://img.shields.io/badge/Discord-Join%20our%20community-7289da?style=flat-square&logo=discord&logoColor=white)](https://discord.gg/cA6Aa4uyDX) [![Sponsor](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=ff69b4)](https://github.com/sponsors/NirDiamant) Agents Towards Production is your go-to resource for building GenAI agents that scale - from zero to production. Whether you're just starting out or refining your deployment stack, this repo gives you the tools, patterns, and code examples to do it right. ⭐ If you find value in this project, **give it a star** to help others discover it too

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--- ## ✨ Introduction **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. --- ## 🔑 Key Features | | | |---|---| | **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 | | 🔄 **Orchestration** | Design multi-tool, memory-aware workflows and agent-to-agent messaging | | 🔌 **Tool Integration** | Connect agents to databases, web data, and external APIs | | 🔍 **Observability** | Add tracing, monitoring, and debugging hooks | | 🚀 **Deployment** | Ship to containers, GPU clusters, and on-prem servers | | 🧠 **Memory** | Implement both short- and long-term stores with semantic search | | 🖥️ **UI & Frontend** | Build chat or dashboard front-ends in minutes | | 🧩 **Agent Frameworks** | Create stateful graphs, expose agents as REST endpoints, and package reusable tools | | 🛠️ **Model Customization** | Fine-tune language models for specialized agent behavior and domain expertise | | 👥 **Multi-agent Coordination** | Enable message passing and shared planning | | 🔒 **Security** | Apply real-time guardrails and injection defenses | | 📊 **Evaluation** | Automate behavioral testing and metric tracking | --- ## 📚 Tutorials ### 🔄 Orchestration

Tutorial	Description	View
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.

### 🔌 Tool Integration

Tutorial	Description	View
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.

### 🔍 Observability

Tutorial	Description	View
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.
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.

### 🚀 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.
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.

### 🧠 Memory

Tutorial	Description	View
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.

### 🖥️ UI & Frontend

Tutorial	Description	View
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.

### 🧩 Agent Frameworks

Tutorial	Description	View
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.
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.
Deploying Agents as APIs with FastAPI	Create and deploy agents as performant APIs, supporting both synchronous and streaming endpoints.

### 🛠️ Model Customization

Tutorial	Description	View
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.

### 👥 Multi-agent Coordination

Tutorial	Description	View
Multi-Agent Communication with A2A Protocol	Simulate collaborative agent workflows and message exchange using open communication protocols for interoperability.

### 🔒 Security

Tutorial	Description	View
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.
Comprehensive Agent Security (LlamaFirewall)	Apply comprehensive input, output, and tool security guardrails for agents. Covers prompt injection, behavior alignment, and tool access control.
Hands-On Agent Security Evaluation (Apex)	Hands-on prompt injection attacks, defenses, and automated security testing for AI agents.

### 📊 Evaluation

Tutorial	Description	View
Automated Agent Evaluation & Behavioral Analysis (IntellAgent)	Automate agent evaluation with behavioral analysis, performance metrics, and actionable insights for improving agent quality.

--- ## 🚀 Getting Started Transform your AI agent ideas into production-ready systems using our battle-tested patterns and implementations. ### 📖 Browse Online 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. ### 🛠️ Clone and Build Download the repository to run tutorials locally, experiment with configurations, customize implementations, and integrate proven patterns directly into your agent development workflow.

#### Quick Setup **1. Get the Code** ```bash git clone https://github.com/NirDiamant/agents-towards-production.git cd agents-towards-production ``` **2. Install Dependencies** Navigate to your target tutorial and set up the environment: ```bash # Example: Multi-tool agent orchestration cd tutorials/agentic-applications-by-xpander.ai pip install -r meeting-recorder-agent\requirements.txt ``` **3. Deploy and Test** Launch tutorials through their preferred interface: ```bash # Run interactive notebooks for experimentation jupyter notebook tutorial.ipynb # Execute production scripts for integration testing python app.py ```

--- ## 🤝 Contributing We 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](CONTRIBUTING.md) for more details. --- ## 📜 License This project is licensed under a custom non-commercial license - see the [LICENSE](LICENSE) file for details. ---

### ⭐️ If you find this repository helpful, please consider giving it a star!

Keywords: AI Agents, Production Deployment, LLM, Orchestration, Multi-agent Systems, Memory Systems, Monitoring, Security, Observability, Agent Frameworks, Infrastructure, Serverless, Enterprise AI, Tool Integration

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