Agents Towards Production
End-to-end, code-first tutorials for building production-grade GenAI agents from prototype to enterprise deployment.
- Source repo
- NirDiamant/agents-towards-production
- Stars
- ★ 22k
- Last updated
- 1d ago
- License
- NOASSERTION
- Primary language
- Jupyter Notebook
- FA score
- 0/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changes
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- A developer learning how to build stateful agent workflows with LangGraph.
- Not a fit if
- Teams wanting a unified runtime or hosted service out of the box
- Teams unwilling to sign up for and pay for external services like Redis, Tavily, or Contextual AI
- Commercial projects needing a permissive commercial license
- Source review
- 0/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
Agents Towards Production is an open-source collection of tutorials that provides a hands-on guide to every building block of a GenAI agent stack. It covers 28 production-grade tutorials including stateful workflows, vector memory, web search APIs, Docker deployment, security guardrails, GPU scaling, multi-agent coordination, observability, evaluation, and UI development. Each tutorial lives in its own folder with ready-to-run notebooks or code files. The repository includes tutorials for integrating with tools such as LangGraph, FastAPI, Docker, Redis, Mem0, Tavily, Bright Data, Contextual AI, RunPod, Ollama, LangSmith, and Streamlit. The README features an AI agent architecture diagram and is maintained by the author with community support via Discord and a newsletter. The project is licensed under a custom non-commercial license.
The repository provides a series of runnable tutorials, each guiding users through building a specific production-grade agent component. The tutorials include Jupyter notebooks and Python scripts, such as building stateful agent workflows with LangGraph, deploying agents as APIs with FastAPI, implementing dual-memory and semantic search with Redis, self-improving memory with Mem0, integrating real-time web search with Tavily, web data collection with Bright Data, production-ready RAG with Contextual AI, GPU deployment with RunPod, on-prem LLM deployment with Ollama, tracing and debugging with LangSmith, security with LlamaFirewall, and building chatbot UIs with Streamlit. Each tutorial includes instructions for installing dependencies and running the code, along with command examples like pip install -r requirements.txt and python app.py.
- A developer learning how to build stateful agent workflows with LangGraph.
- A team needing to deploy an agent as a production API using FastAPI.
- A researcher implementing long-term memory and semantic search for agents to remember user preferences.
- A developer integrating real-time web search into their agent to access up-to-date information.
- An enterprise building a production-grade RAG system for financial document analysis.
- A developer scaling agent deployment on GPU infrastructure with RunPod to handle high loads.
How do you install or deploy this agent?
git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-productionThen navigate to a specific tutorial directory and install its dependencies, for example:
cd tutorials/agentic-applications-by-xpander.ai
pip install -r meeting-recorder-agent/requirements.txtA Python environment is required. Some tutorials may require additional API keys (e.g., Tavily, Redis, Contextual AI) and Docker.
How do you use this agent?
- Read the target tutorial's README for concepts and steps.
- Run the Jupyter notebook for experimentation:
jupyter notebook tutorial.ipynb - Or run the production script for integration testing:
python app.py - Configure necessary API keys and environment variables as directed in the tutorial.
What are this agent's strengths and limitations?
- Provides 28 production-grade tutorials covering the full agent development stack.
- Each tutorial is self-contained and immediately runnable, facilitating rapid learning.
- Includes integration examples with mainstream tools like LangGraph, Redis, Tavily, and more.
- Architecture diagram clearly illustrates components of a production agent.
- Active author with a Discord community and newsletter for support.
- Custom non-commercial license restricts commercial usage.
- Tutorials depend on external services (e.g., Redis, Tavily, Contextual AI) which may require paid plans or registration.
- Some tutorials need API keys, increasing adoption friction.
- Provides tutorials only, not a unified agent runtime.
- Certain tutorials rely heavily on specific cloud services, posing migration risks.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| Agents Towards Production This agent | 0 · Major gaps | Library / SDKFree + model costs | ★ 22k | 1d ago | Jupyter Notebook | — |
| Agent Craft | 49 · Major gaps | CLIFree + model costs | ★ 516 | 3d ago | Python | OpenAI API |
| AI Agent Service Toolkit | 69 · Some gaps | FrameworkFree + model costs | ★ 4.5k | today | Python | OpenAI API |
| FastAPI LangGraph Agent Production-Ready Template | 41 · Major gaps | Self-hosted serviceFree + model costs | ★ 2.7k | 2d ago | Python | OpenAI API |
How does FollowAgents rate this agent?
Why each dimension lost points
Insufficient evidence: The repository only provides README and partial test files, with no concrete implementation of permission management, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust criteria cannot be confirmed from static files, hence score 0.
Insufficient evidence: No complete codebase or runtime documentation provided to assess self-consistency, dependency availability, or failure messages. Test file only covers FastAPI endpoints, insufficient for overall reliability assessment.
Insufficient evidence: README claims coverage of multiple scenarios but lacks specific configuration or boundary descriptions. Audience and scenarios are described broadly, with no concrete evidence of capability boundaries, trigger precision, or environment fit.
Insufficient evidence: README provides tutorial list and architecture diagram, but lacks installation notes, naming stability, examples/FAQ, known limitations, versioning/changelog, and maintenance responsibility specifics. License is custom non-commercial, but not clearly stated in metadata.
Insufficient evidence: README claims production-grade tutorials, but no concrete evidence of output usability, marginal value, or cost-benefit. Test file only validates basic endpoints, insufficient to prove overall effectiveness.
Insufficient evidence: Claims in README lack traceability, no cross-source corroboration or separation of facts and inferences. Test file covers only partial functionality, unable to verify overall claims.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- Repository only provides README and a few test files, preventing comprehensive static assessment.
- License is custom non-commercial; commercial use requires explicit permission.
- No installation instructions, dependency lists, or security configurations provided; deployment requires caution.