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.
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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 |
| 🔄 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 |
| 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. |
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| Tutorial | Description | View |
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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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| 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. |
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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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| Tutorial | Description | View |
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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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| 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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| 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. |
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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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| Tutorial | Description | View |
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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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| Tutorial | Description | View |
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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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| Tutorial | Description | View |
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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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| Tutorial | Description | View |
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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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| Tutorial | Description | View |
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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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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.
This project is licensed under a custom non-commercial license - see the LICENSE file for details.
