Agents Towards Production
ActiveDescription
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.
Key Features
- 28 production-grade tutorials — Covering stateful workflows, vector memory, web search APIs and more
- End-to-end coverage — From Docker deployment, FastAPI endpoints to security guardrails and GPU scaling
- Multi-agent coordination — Teaching multi-Agent collaboration architecture design and implementation
- Observability & evaluation — Including logging, monitoring, testing and evaluation for production
- Browser automation — Tutorials cover agent browser automation operations
- Code-first — All tutorials as Jupyter Notebooks, ready to run
Use Cases
💡 Learning how to advance AI agents from prototype to production
💡 Mastering best practices for RAG systems and vector database integration
💡 Building production-grade agent applications with security guardrails
💡 Learning Docker and GPU deployment methods for agents
💡 Understanding multi-agent coordination and observability approaches
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (21.4k stars)
- • Responsive to issues, low backlog
⚠️ Limitations
- • No clear open-source license
Categories
Quick Start
```bash
# Clone the repository
git clone https://github.com/NirDiamant/agents-towards-production.git
cd agents-towards-production
# Follow tutorials in order:
# 1. LangGraph basics
# 2. Agent memory systems
# 3. RAG integration
# 4. Security guardrails
# 5. Deployment & monitoring
# Each tutorial is an independent Jupyter Notebook
```