Commander.ai

Multi-Agent AI Orchestration Platform

Overview

Commander.ai is an enterprise-grade multi-agent AI orchestration platform designed to decompose complex tasks into specialized agent workflows. Using LangGraph's state machines and graph-based task execution, it orchestrates 8 specialized agents that work collaboratively to solve problems at enterprise scale.

Version 0.6.0 introduces performance analytics, vector-native search integration, and real-time prompt engineering capabilities for production AI workloads.

Current Version

v0.6.0

Status

Production

Key Features

Multi-Agent Orchestration

8 specialized agents working in concert with LangGraph for complex task decomposition and execution

Live Prompt Engineering

Real-time prompt optimization with performance metrics and A/B testing capabilities

Performance Analytics

Comprehensive metrics on agent performance, latency, token usage, and cost optimization

NLP Scheduler

Intelligent task scheduling using natural language understanding and priority queue management

Vector Search Integration

Semantic search powered by Qdrant with pgvector embeddings for context awareness

Enterprise Configuration

Flexible configuration management for different deployment scenarios and workload types

8 Specialized Agents

@leo

Creative & Research Agent

Handles content generation, research synthesis, and creative task orchestration

@bob

Business Logic Agent

Processes business rules, workflows, and decision trees

@sue

Data Specialist Agent

Manages data extraction, transformation, and analysis

@rex

Technical Agent

Handles system architecture decisions and technical implementation

@alice

Security & Compliance Agent

Ensures security posture, compliance checks, and risk assessment

@maya

Performance Optimization Agent

Monitors and optimizes system performance and resource utilization

@kai

Integration Agent

Manages third-party integrations and API orchestration

@chat

User Interface Agent

Handles user interactions, natural language processing, and conversation flow

Technology Stack

Framework

Next.js 14
React 19

Backend

Python
FastAPI

AI/ML

LangGraph
LangChain
OpenAI API

Database

PostgreSQL
pgvector

Search

Qdrant
Vector Search

Caching

Redis

DevOps

Docker
Kubernetes

Architecture


┌─────────────────────────────────────────────────────┐
│         User Interface (Next.js 14/React 19)        │
│         Real-time Prompt Engineering UI             │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│         API Gateway (FastAPI)                       │
│    Task Distribution, Session Management            │
└──────────────────┬──────────────────────────────────┘
                   │
┌──────────────────▼──────────────────────────────────┐
│    LangGraph Orchestration Engine                   │
│    ┌─────────────────────────────────────────────┐ │
│    │  @leo    @bob    @sue    @rex    @alice     │ │
│    │  @maya   @kai    @chat                      │ │
│    └─────────────────────────────────────────────┘ │
└──────────────────┬──────────────────────────────────┘
                   │
        ┌──────────┼──────────┐
        │          │          │
    ┌───▼──┐ ┌────▼────┐ ┌──▼────┐
    │  PG  │ │ Qdrant  │ │ Redis │
    │ Data │ │ Vectors │ │ Cache │
    └──────┘ └─────────┘ └───────┘

Implementation Highlights

Graph-Based Task Execution

LangGraph enables complex task dependencies with state management, allowing agents to communicate and coordinate work across multiple steps with memory and context preservation.

Vector-Native Search

PostgreSQL pgvector extensions combined with Qdrant for semantic search capabilities, enabling contextual awareness and similarity-based retrieval at scale.

Performance Monitoring

Real-time dashboards tracking agent performance, token usage, latency, and cost per task. Built-in A/B testing for prompt optimization.