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Agents Towards Production is an opinionated, code-first playbook for taking AI agents from prototype to production-ready systems. Instead of focusing only on toy examples, it dives into every layer of an agent stack: orchestration, memory, RAG, tool and API integration, security, observability, deployment, evaluation, and UI. The repository is built around runnable tutorials, each in its own folder, often sponsored by or built in collaboration with infrastructure providers like LangChain, Redis, Bright Data, Contextual AI, Tavily, Runpod, Portia, and others. These tutorials show how to implement things like secure tool calling with OAuth, dual-memory architectures, production RAG agents, multi-agent communication protocols, GPU deployment, containerization with Docker, FastAPI endpoints, and Streamlit chat UIs. The architecture diagram and accompanying material provide a mental model for how production-grade agents should be wired together.
Features
- End-to-end tutorials covering orchestration, memory, tools, RAG, and deployment
- Production-focused patterns for Docker, FastAPI, GPU scaling, and on-prem LLM setups
- Deep dives into security guardrails, prompt-injection defenses, and safe tool calling
- Integration examples with real services like Redis, Bright Data, Contextual AI, Tavily, and more
- Observability and evaluation tutorials using tracing, behavioral analysis, and automated tests
- Ready-to-run notebooks and scripts that can be cloned, customized, and adapted into real products
Project Samples
