Runnable playbooks for building sharp, production-ready AI workflows with Regolo. Each folder includes code, setup notes, and a companion article.
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Welcome to the Regolo.ai tutorials repository.
This collection focuses on practical, runnable AI examples for developers and product teams. Each tutorial is designed to be easy to follow, easy to run, and easy to adapt.
- Ready-to-run AI blueprints
- Practical implementation notes
- OpenAI-compatible Regolo integrations
- Examples focused on real workflows, not toy demos
- Faster setup with minimal moving parts
- Code structured for real-world usage
- Clear article-to-code mapping
- Regolo-hosted workflows with a production-friendly API
- Multi-agent systems for launch automation and campaign execution
- Enterprise support and operations workflows
- RAG pipelines with retrieval, reranking, and evaluation
- Predictable orchestration for policy-constrained AI behavior
| Tutorial | Description | Labels | Article Link |
|---|---|---|---|
| Clawdbot Knowledge Base | Internal knowledge bot with hybrid retrieval (embeddings + BM25 + reranker) and Telegram interface. | Python · Runnable · GPU 100% Ready |
Read Article |
| CrewAI Product Launch Campaign | Automated product launch system with crewAI multi-agent workflow and Regolo infrastructure. | Python · Runnable · GPU 100% Ready |
Read Article |
| Cheshire Cat AI + Regolo: Enterprise AI Agent Setup | Enterprise-ready AI agent setup via OpenAI-compatible API and open models. | Python · Runnable · GPU 100% Ready |
Read Article |
| Build Faster: LLMaaS with Qwen 3.5 122b | Practical LLMaaS patterns for developers: boilerplate generation, streaming assistant, lightweight RAG, and structured extraction. | Python · Runnable · GPU 100% Ready |
Read Article |
| Orchestrating Predictable AI Agents with Parlant and Regolo | Deterministic policy orchestration with Parlant-style control layer and Regolo backend. | Python · Runnable · GPU 100% Ready |
Read Article |
| Advanced RAG in 2026: Long Context Is Not Memory | Enterprise ticket triage that uses Regolo for structured incident analysis, escalation, and mitigation planning. | Python · Runnable · Enterprise Triage |
Read Article |
| Programmatic Tool Calling on Regolo GPUs | Build smarter agents with classic JSON tool calling and programmatic tool calling using a restricted runtime and multi-step orchestration. | Python · Runnable · GPU 100% Ready |
Read Article |
| Production-Ready RAG on Open Models | End-to-end production RAG: chunking, retrieval, reranking, evaluation, and optimization. | Python · Runnable · GPU 100% Ready |
Read Article |
| Build Hybrid Inference Stack Without Sacrificing Quality | Regolo-only incident triage demo with colored logs, local .env loading, and structured JSON responses. |
Python · Runnable · Logging |
Read Article |
| LLM Architectures in 2026: Optimize for What Matters, Not Benchmarks | A lightweight architecture router that loads .env, reads REGOLO_CORE_MODEL, and selects a Regolo model before sending the request. |
Python · Runnable · Model Routing |
Read Article |
| AI Agents and Tool Chaining in 2026 | Contract-review workflow that chains extraction, reranking, and policy decisions into one runnable script. | Python · Runnable · Workflow |
Read Article |
| How to Build a PR Review Assistant | Automated PR review assistant that reads local .env settings, picks a model, and reviews Git diffs through Regolo. |
Python · Runnable · Code Review |
Read Article |
| AI Governance & Copyright Policy Gateway | Policy gateway example con rimozione PII, classificazione BLOCK/ALLOW/TRANSFORM e compliance in due fasi. | Python · Runnable · Governance |
Read Article |
| Run MiroFish with regolo.ai: A Complete Integration Guide | This guide walks you through every step: cloning MiroFish, configuring it to point at regolo.ai, running your first simulation, and tuning for performance. | Python · Runnable · GPU 100% Ready |
Read Article |
- Clone this repository:
git clone https://github.com/yourusername/tutorials.git - Navigate to the desired tutorial folder.
- Follow the instructions in the folder's README.md.
- Check out the full article for detailed explanations.
- Pick one tutorial and configure
.env. - Run the local setup from that folder README.
- Launch your first production-ready AI feature.
- Start with the use case closest to revenue or cost reduction.
- Deploy a pilot in one team, measure response quality and throughput.
- Expand to additional workflows once baseline KPIs are stable.
Feel free to contribute by adding new tutorials or improving existing ones. Please follow the contribution guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
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