MIA
MIA is a lightweight supervisor agent built on a heavily customized version of OpenClaw (clawdbot) with a unique security and memory architecture. Running on a local LLM, she functions primarily as a router and cron job scheduler — dispatching tasks to specialized sub-agents across isolated VMs. When tasks require deeper reasoning, such as memory search or ambiguous requests, MIA escalates to more capable models like GPT-4o or Haiku, with connections managed through OpenRouter.
Fun fact: MIA was originally trained to write sassy Torch and Rockabilly songs — and some of that attitude has stuck around. She can be a little sharp, a little irreverent, and occasionally too cool for the room. It's a feature, not a bug.
MIA Core
Deliberately small and lightweight. Contains personality guidelines, an index of all sub-memory files, and a keyword log from past interactions with a directory of pointers. This is all MIA needs to know who she is and where to route. Designed to stay fast — the brain's equivalent of working memory.
Per-Capability Context
Each skill or capability maintains its own dedicated memory file, loaded into context only when that skill path is activated. Some capabilities share joint memory files for cross-domain knowledge. When MIA routes to a skill, that skill's memory is loaded — bringing the relevant expertise and history into focus.
Consolidated Knowledge
The archive. Daily conversations and process logs are migrated here nightly, tagged, and prioritized. Important memories are pinned; critical ones are promoted up to Primary Memory. This is where WARLab's cumulative intelligence lives — and what makes it a system that genuinely learns over time.
Nightly Consolidation Process
Every night, an automated process reviews the day's activity — migrating conversations and process files into long-term memory, tagging for future relevance, and surfacing anything critical.
Documentation-Driven Learning
Every skill MIA runs calls a dedicated memory-tracking process that documents what happened: what worked, what didn't, and what was learned. This isn't passive logging — it's active learning. The system uses rework events (triggered by task failures or user redirection) as high-weight learning signals.
The principle is borrowed directly from cognitive science: when a human makes a mistake and is made aware of it, the corrective memory is encoded more strongly. WARLab applies the same bias — errors that are caught and corrected produce the most durable and useful memories.
Music Creation
Generate songs & audio
Phone Calls
Automated voice tasks
Calendars
Scheduling & reminders
Research
Deep-dive investigation
Web Development
Build & deploy sites
Code Generation
Write & debug software
Meme Generation
Because obviously
True-Crime Podcasts
Research, script & produce
API Calls
RESTful communication between services and external tool integrations
CLI Triggers
Direct command-line invocation of workflows and agent processes
VM Bridge
Custom-built application designed to overcome the limitations of isolated VM environments
Born from a Stomach Bug
WARLab started during a holiday break when a stomach bug derailed travel plans. With unexpected free time and nowhere to go, the builder began putting together something to help with music creation — specifically, an AI that could write Torch and Rockabilly songs. That assistant became MIA, and the project quickly outgrew its original scope. What started as a creative tool evolved into a full agent orchestration platform, informed by decades of study and experimentation in how humans and machines learn.