Black LLAB: Open-Source Architecture for Dynamic Model Routing and Docker-Sandboxed AI Agents

A developer has released Black LLAB, an open-source project that attempts to replicate frontier AI lab systems for autonomous task execution. The system addresses two main problems: manually deciding which model to use for different prompts and safely executing AI agent code.

Architecture Components

The system consists of several key components:

• Dynamic Complexity Routing: Uses Mistral 3B Instruct to grade prompts on a scale of 1-100. Simple questions get routed to fast/cheap models; complex coding tasks get routed to heavy models with "Lost in the Middle" XML context shaping.
• Docker-Sandboxed Agents: Integrates OpenClaw to deploy agents in dedicated, isolated Docker containers. Agents can write files, scrape the web, and execute code without touching the host OS.
• Advanced Hybrid RAG: Builds a persistent Knowledge Graph using NetworkX and uses a Cross-Encoder for precise context retrieval beyond standard vector search.
• Live Web & Vision: Integrates with local SearxNG for web scraping and Pix2Text for local vision/OCR.
• Budget Guardrails: Includes a daily spend limit slider to prevent cloud API overages.

Model Lineup

The system uses multiple models for different purposes:

• Routing/Logic: Mistral 3B & Qwen 3.5 9B (Local)
• Midrange/Speed: Xiaomi MiMo Flash
• Heavy Lifting (Failover): Claude Opus & Perplexity Sonar

Tech Stack

The project is built with FastAPI, Python, NetworkX, ChromaDB, Docker, Ollama, Playwright, and a vanilla HTML/JS terminal-inspired UI.

The developer describes themselves as "more a mechanical engineer than software" and is seeking senior developer feedback on the architecture, particularly the Docker sandboxing approach. The project is available on GitHub for independent researchers who want to run autonomous tasks without being locked to a single provider.