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1. User sends input
2. Runner receives input
3. Input guardrails check the request
4. Agent sends prompt to model
5. Model decides: answer OR call a tool
6. Runner executes tool if needed
7. Model produces final response
8. Output guardrails check the response
Welcome to the Chainlit Demos repository! Here you'll find a collection of example projects demonstrating how to use Chainlit to create amazing chatbot UIs with ease. Each folder in this repository represents a separate demo project.
To run a demo, follow these steps:
Clone this repository:
The ELK stack itself has not “become Autonomous AI.” What changed is that has added AI and automation layers on top of the existing ELK platform. The core stack is still the same:
E – Elasticsearch
L – Logstash
K – Kibana
Those pieces still ingest, index, and visualize data exactly as before. What Elastic has done since roughly 2023–2025 is layer AI features into the Elastic Platform.
1. AI Assistant inside Kibana
I could build this. Is there a Use Case? Let's use a "for example", that I don't necessarily see as SOLID one.
A clean, technically coherent happy path where SOLID actually makes sense.
Artists. Music. Digital rights. Clean room scenario.
Scroll down for time to build estimates
🎵 Happy Path: Independent Artist Data + Sales Pod
Imagine:
An independent musician.
They have:
These help build AI assistants, tools, and workflows.
Chainlit
The one you’re using now. It provides the chat UI and event system for AI apps.
LangChain
A toolkit for connecting LLMs, tools, memory, and retrieval pipelines.
LlamaIndex
Focused on retrieval-augmented generation (RAG) — connecting models to data sources.
Isolation issues
Version conflicts
System pollution
A fancy way of saying using agents that operate autonomously. If the shoe fits, because yes this IS pretty fancy!
That sentence alone creates entire consulting practices.
Cloud backups are Schrödinger’s disaster plan
So much can be said in a name. Schrödinger’s disaster plan. That's classic in a box. Had to say it!