I Open-Sourced My Multi-Agent Orchestration Framework (94% Lower API Costs)

September 3, 2025

This content originally appeared on DEV Community and was authored by daniele pelleri

The Problem: 5 AI Agents = Complete Chaos

Ever tried running multiple AI agents together? Here’s what happens:

Agent A analyzes data
Agent B rewrites everything from scratch (doesn’t know what A found)
Agent C duplicates A’s work
You become a human copy-paste machine between ChatGPT windows
Your API bill explodes

I burned through $3,000 learning this the hard way.

The Solution: AI Team Orchestrator

I built a framework that orchestrates AI agents like a real company:

Watch 2-min Demo

How It Works

Your goal: “Increase Instagram engagement by 40%”

What happens behind the scenes:

Director Agent analyzes and assembles team
Marketing Strategist creates strategy
Content Creator receives strategy context (no duplication!)
Data Analyst tracks metrics
All agents share workspace memory

Key Architecture Decisions

1. Conditional Quality Gates (94% cost savings)

Instead of checking everything:

Frontend-only changes: skip backend validators (saves $0.23 per check)
Database changes: trigger all validators (full validation when needed)

2. Agent Handoffs with Context

Agents pass context like Slack messages:

From: ResearchAgent
To: StrategyAgent
Context: “Found 3 key competitor patterns”
Artifacts: [“analysis.json”, “data.csv”]

3. Workspace Memory (No repeated work)

Semantic memory prevents re-doing tasks:

If similar task found: use previous approach
If new task: execute and learn

Real Production Metrics

Metric	Before	After
API Costs	$240/month	$3/month
Task Recovery	Manual	<60s autonomous
Context Retention	12%	89%
Setup Time	2 days	15 minutes
Error Rate	23%	1.2%
Throughput	2.3/sec	8.7/sec

Tech Stack

Backend: FastAPI + OpenAI Agents SDK
Frontend: Next.js 15 + TypeScript
Database: Supabase
Architecture: Blackboard pattern with Pydantic contracts

Get Started

git clone https://github.com/khaoss85/AI-Team-Orchestrator
cd ai-team-orchestrator
./scripts/quick-setup.sh

What I Need From The Community

This isn’t a finished product – it’s a starting point. Looking for:

Test it with your use cases
Report what breaks (it will break)
Suggest improvements based on real needs
Contribute if you want to

The roadmap is completely open. Your use case = our next feature.

Lessons Learned (The Hard Way)

Documented everything in a 62,000-word guide:

Why agents create infinite loops (5,000 tasks in 20 minutes!)
Race conditions with parallel agents
Why agents don’t use tools even when available
The $40 CI test that forced us to build mock providers

Example: The Infinite Loop Problem

What went wrong:

Agent decomposes task
Each subtask gets decomposed again
No depth limit = infinite recursion
5,000 tasks created in 20 minutes

The fix:

Hard depth limit (MAX_DEPTH = 5)
AI decides if task is atomic
Anti-loop counter at workspace level

Architecture Deep Dive

The system uses a multi-layer architecture:

Layer 1: Input Processing

User Input → Goal Engine → Task Planner

Layer 2: Execution

Agent Team → Task Executor → Deliverable Generator

Layer 3: Optimization

Memory & Learning → Quality Assurance → Improvement Loop

Each layer feeds back into the system, creating continuous improvement.

Real-Time Thinking Process (Claude/o3 Style)

You can watch agents think in real-time:

[THINKING] Breaking down objective into sub-goals
[ANALYZING] Identifying required specialist skills
[MEMORY CHECK] Found 3 similar patterns from workspace #42
[DECISION] Assembling team of 4 specialists…
[HANDOFF] Marketing strategy completed
[CONTEXT PASSED] 3 key insights from research
[CONFIDENCE] 92%