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nirdiamant/agents-towards-production

📈 Trending★ 20,195Jupyter NotebookAudience · developerComplexity · 4/5ActiveSetup · moderate

TLDR

28 hands-on tutorials teaching you how to build AI agents from prototype to production-ready deployment, covering memory, APIs, Docker, security, and scaling.

Mindmap

mindmap
  root((repo))
    What it does
      AI agent tutorials
      Prototype to production
      28 Jupyter notebooks
    Key topics
      Vector databases
      Web search integration
      FastAPI endpoints
      Docker deployment
    Tech stack
      Python
      LangGraph
      LangChain
      FastAPI
    Use cases
      Deploy agents safely
      Scale to production
      Add security guardrails
      Monitor live systems
    Audience
      AI engineers
      Backend developers
      Startup founders

Things people build with this

USE CASE 1

Deploy an AI agent behind a FastAPI web endpoint so users can interact with it over HTTP.

USE CASE 2

Add persistent memory to your agent using a vector database so it remembers past conversations.

USE CASE 3

Containerize your agent with Docker and deploy it to a cloud platform for reliable scaling.

USE CASE 4

Implement security checks and rate limiting to prevent misuse of your production agent.

Tech stack

PythonJupyter NotebookLangGraphLangChainFastAPIDockerVector databases

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Python environment setup, LangChain/LangGraph dependencies, and likely API keys for LLM services to run tutorials.

License could not be detected automatically. Check the repository's LICENSE file before use.

In plain English

Agents Towards Production is an open-source collection of 28 code-first tutorials that guide you from building a basic AI agent prototype all the way to deploying it in a real enterprise environment. An "AI agent" is a program that uses a large language model to reason, plan, and take actions, like searching the web, reading documents, or calling APIs, rather than just answering a single question. The tutorials are delivered as Jupyter Notebooks (interactive coding documents) and cover the full journey: giving agents persistent memory using vector databases, connecting them to real-time web search, wrapping them in FastAPI web endpoints, deploying them inside Docker containers, adding security guardrails to prevent misuse, scaling to GPUs, coordinating multiple agents working together, and monitoring their behavior in production. Topics include frameworks like LangGraph and LangChain. You would use this resource if you have experimented with AI agents in a demo but don't know how to make them reliable and scalable enough for real users. It bridges the gap between a working notebook proof-of-concept and a system you can actually put in front of customers. The tech stack centers on Python, Jupyter Notebook, LangGraph, Docker, and FastAPI.

Copy-paste prompts

Prompt 1
Show me how to take my working AI agent notebook and wrap it in a FastAPI endpoint using the patterns from this repo.
Prompt 2
How do I add vector database memory to my LangGraph agent so it can recall past interactions?
Prompt 3
Walk me through the Docker and deployment steps to get my agent running in production safely.
Prompt 4
What security guardrails should I add before letting real users interact with my AI agent?
Prompt 5
How do I coordinate multiple agents working together and monitor their behavior in production?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.