Agent

SoMe

A semi human in the loop swarm network.

Project Overview

A swarm network of agent to draft, refine and evaluate social media posts, it can post for the user.

AI Agent Architecture

This project leverages autonomous agent networks to create, optimize, and distribute social media content with minimal human intervention. Built for scalability and intelligent decision-making.

View Live Project
SoMe

See It In Action

Live demonstration of the AI agent network creating, refining, and posting social media content

Technical Implementation

Core Technologies

  • TypeScript - Type-safe development
  • LangChain - AI orchestration framework
  • LangGraph - Agent workflow management
  • Next.js - Frontend by LangChain team

Key Features

  • • Custom handoff mechanisms
  • • Built-in evaluation & tracing
  • • Direct social media posting
  • • User authentication integration

Agent Workflow

Agent workflow diagram showing the flow from Start through EntryPoint, draft, refiner, editor to End

Agent Architecture Overview

Visual representation of the decoupled agent architecture using handoff tools and message passing. Each agent operates independently and communicates through structured transfer mechanisms.

The architecture leverages LangGraph’s handoff tools where agents communicate exclusively through transfer functions like transfer_to_editor({"reason": "context"}). Each transfer includes reasoning context, preserving the complete conversation state while enabling intelligent routing decisions. This ensures fault tolerance and allows agents to make informed decisions based on prior conversation history.

State management follows a message-centric approach. Transfer messages become tool messages containing both the transfer action and the reasoning context, allowing receiving agents to understand why they were invoked.

This design pattern eliminates direct node connections in favor of message-driven handoffs. Each agent receives the full conversation context plus transfer reasoning, enabling decision-making while maintaining system modularity. The approach scales horizontally and allows for dynamic agent addition without architectural changes, making it ideal for complex multi-agent workflows.

Technical Implementation Details

Agents are fully decoupled and communicate exclusively through message passing, no direct node connections. This ensures scalable, maintainable, and fault-tolerant agent networks that can dynamically adapt to changing requirements, each with access to specific tools.

Start → EntryPoint

Initial routing with full conversation state

Draft Agent

Creates content using handoff tool with reason context

Refiner → Editor Chain

Sequential handoffs with state preservation and transfer reasoning

Message Passing

Agents communicate through structured message queues, ensuring loose coupling and high reliability.

Tool Execution

Custom tool execution framework with built-in monitoring and error handling capabilities.

Open Source UI

Frontend built using LangChain team's open-source UI components and design system.