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agents towards production

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Multi-Agent Platforms & Frameworks
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Basic Information

Agents Towards Production is an open-source playbook of runnable, code-first tutorials for building production-ready generative AI agents. It collects step-by-step examples that cover the full agent stack, including stateful workflows, vector memory, real-time web search APIs, browser automation, GPU deployment, container patterns, FastAPI endpoints, security guardrails, fine-tuning, multi-agent coordination, observability, evaluation, and UI frontends. Each tutorial is organized in its own folder with notebooks or code files so users can run and adapt implementations locally. The repo targets developers and engineers who want practical, production-oriented patterns and reference implementations to move agent prototypes into deployable systems. The materials emphasize reproducible examples, architectural decisions, and integration patterns rather than theoretical discussion.

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9910
App URL
https://github.com/NirDiamant/agents-towards-production
Github Repository
https://github.com/NirDiamant/agents-towards-production/blob/main/README.md

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App Details

Features
Tutorial-first learning with runnable walkthroughs for each topic. Full lifecycle coverage from prototyping to deployment, including containerization and on-prem LLM hosting. GPU deployment guidance and examples for scaling compute. Real-time monitoring and tracing examples for observability and debugging. Tool integration tutorials showing how to connect agents to web data, APIs, and databases. Dual-memory and semantic search patterns using vector stores. Orchestration patterns for multi-tool and multi-agent workflows. Security and guardrail tutorials covering injection defenses and runtime policies. Examples for exposing agents as REST/streaming endpoints, fine-tuning models, automated evaluation, and building simple UIs.
Use Cases
The repository provides hands-on, ready-to-run code and documented patterns that shorten the path from prototype to production for GenAI agents. Users can follow focused tutorials to implement memory systems, realtime web access, browser automation, or containerized deployments without designing each integration from scratch. The collection includes observability and security patterns so teams can instrument, test, and harden agents before release. Orchestration and multi-agent examples demonstrate message passing and stateful graphs for complex workflows. FastAPI and UI tutorials show practical ways to expose agents as services and demos. Overall it is a practical reference and learning resource for engineering teams building scalable, maintainable agent applications.

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