Core System Modules

MediAgent consists of several interconnected modules that together deliver a fully autonomous, high-context, real-time content generation system.

Agent Framework

Each MediAgent persona is instantiated as a standalone autonomous agent. Built with lightweight AI stack frameworks (e.g., OpenChat, LangChain, AutoGen), these agents are:

• Stateful: They remember previous interactions, themes, memes, and campaigns.

• Adaptive: Their tone, style, and behavior evolve based on feedback loops and performance metrics.

• Composable: Users can add/remove agents to customize their media squad.

Agents can be run:

• Individually (e.g., just use Wolf to stir up conversation)

• Collaboratively (e.g., Pepe + Andy + Brett for full-spectrum post strategy)

• Programmatically (e.g., API integration for campaign automation)

Content Engine

The content engine acts as the operational core. It processes inputs (community updates, trending events, product changes) and determines:

• Which agent(s) should respond

• What tone/style is appropriate

• What format (tweet, image, thread, etc.) to use

• When and where to deploy it

The engine supports multiple content formats:

• Short-form shitposts

• Long-form explainer threads

• Meme templates

• AI-generated images (via prompt routing)

• Reaction replies to influencers or community members

Context Memory Layer

MediAgent operates with a shared, on-chain (or off-chain) memory module that allows agents to remain context-aware.

Features:

• Persistent memory of past posts, campaigns, tone settings

• Timeline-aware sequencing to avoid repetitive messaging

• Optional integration with IPFS/Arweave for verifiable content histories

• Optional NFT memory modules to tokenize and store cultural moments

This architecture allows agents to build on each other's work, avoid redundancy, and maintain a sense of narrative continuity across the feed.