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Multi-Agent Chainlit Assistant

OpenAI Agents SDK with dynamic routing

Intelligent assistant framework using OpenAI Agents SDK with Chainlit UI. Features a main routing agent that intelligently dispatches queries to specialized sub-agents: Shaitani Calculator (humorous math), Web Search (real-time), and custom tools. Demonstrates multi-agent orchestration patterns.

GitHub
Case study snapshot
Role
AI systems builder
Audience
AI product users and recruiters
Timeline
2025-11
Role
AI systems builder
Audience
AI product users and recruiters
Date
2025-11
Problem

Why it mattered

Autonomous agent routing with specialized tool handling

Solution

What shipped

Intelligent assistant framework using OpenAI Agents SDK with Chainlit UI. Features a main routing agent that intelligently dispatches queries to specialized sub-agents: Shaitani Calculator (humorous math), Web Search (real-time), and custom tools. Demonstrates multi-agent orchestration patterns.

Impact

Proof signal

Autonomous agent routing with specialized tool handling

< architecture />

System flow

Query Input -> Main Router -> Agent Selection -> Tool Execution -> Response Generation.

Query Input
Main Router
Agent Selection
Tool Execution
Response Generation
Visual architecture
01
Query Input
02
Main Router
03
Agent Selection
04
Tool Execution
05
Response Generation
quality score

Honest maturity snapshot

Code Quality9/10
UI / UX6/10
Scalability7/10
Production Ready8/10
Before

Manual questions, scattered context, and slow follow-up.

After

An agent workflow that routes intent, calls tools, and returns useful next actions.

capabilities

Features

  • OpenAI Agents SDK integrated into the workflow.
  • Chainlit integrated into the workflow.
  • Python 3.11 integrated into the workflow.
  • uv integrated into the workflow.
  • Query Input stage documented in the delivery flow.
  • Main Router stage documented in the delivery flow.
tradeoffs

Engineering challenges

  • Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Choosing a scope that could be shipped, tested, and documented.
  • Turning technical work into proof a visitor can evaluate quickly.
proof

Results

  • Autonomous agent routing with specialized tool handling
  • Documented the engineering path and important learnings.
  • Made source code or technical proof available for review.
reflection

What I learned

  • Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Proof learned: Autonomous agent routing with specialized tool handling
  • Next iteration: Add richer screenshots or a narrated demo.
< stack />
OpenAI Agents SDKChainlitPython 3.11uvdotenv
< next iteration />

Future improvements

Add richer screenshots or a narrated demo.
Add before/after metrics and usage notes.
Package the project as a reusable template or deployable demo.
n8n WhatsApp AI AgentSchedule Impact Statistical Analysis