Ask deva to help you get started with OpenClaw!

#devchallenge #algoliachallenge #ai #agents

This is a submission for the Algolia Agent Studio Challenge: Consumer-Facing Conversational Experiences

What I Built: deva a conversation assistant to help you get started with OpenClaw.

deva code on GitHub

What I Built

deva is a lightweight, consumer‑facing conversational assistant designed to help users explore and understand OpenClaw without digging through scattered documentation. Instead of searching across READMEs, GitHub issues, and config examples, users can simply ask deva questions and get contextual, retrieval‑augmented answers. The experience is intentionally simple: a clean landing page, a focused chat interface, and a friendly agent that guides users through setup, configuration, and onboarding concepts for OpenClaw.

Demo
Live site: https://askdeva.netlify.app/

deva runs entirely in the browser using a Vite + React frontend and an Algolia Agent Studio backend.

Motivation
I wanted to use this challenge as a chance to explore Algolia’s new Agent Studio in a hands‑on, practical way. My goals were simple but meaningful:

experiment with building agents using Algolia’s new tooling
create a playful demo that still reflects a real use case I care about: DevRel and builder‑focused assistance
challenge myself to build something end‑to‑end in a solo sprint
dig deeper into the OpenClaw ecosystem and its documentation
and, honestly, just have fun with it deva became the perfect blend of exploration, creativity, and technical curiosity.

How I Used Algolia Agent Studio

I created a custom agent in Algolia Agent Studio and connected it to an index containing OpenClaw documentation, GitHub issues, and onboarding references. The agent uses retrieval to surface relevant content and summarize it conversationally.

A few key pieces of the setup:

Indexed data: README sections, configuration examples, GitHub issues, and onboarding‑related content from the OpenClaw ecosystem.
Retrieval‑augmented responses: The agent pulls relevant documents and uses them to answer user questions with context.
Targeted prompting:
Instructing the agent to avoid raw JSON output
Encouraging natural‑language summaries
Guiding the tone toward helpful onboarding support
Frontend integration: The Algolia chat widget connects directly to the agent using environment variables injected at build time.

This combination creates a smooth, conversational way to explore OpenClaw without manually searching through multiple sources.

Why Fast Retrieval Matters

OpenClaw’s documentation and examples are spread across different places — READMEs, issues, config snippets, and community discussions. Fast retrieval ensures that:

the agent can surface relevant information immediately
users don’t wait for long LLM reasoning cycles
the conversation feels responsive and natural
the assistant can handle broad or vague questions by grounding answers in indexed content

Algolia’s speed keeps the experience fluid, which is essential for a consumer‑facing conversational tool.

I was impressed with how easy it was to scaffold and deploy an agent in my app with minimal fuss. This could be a game changer for those seeking to integrate robust, intelligent search into their agentic applications and assistants! I definitely intend to extend deva and use Algolia Agent Studio to quickly build more AI search features in future apps.