A multi-agent LLM system where multiple agents coordinate to solve complex tasks. Each agent has its own role, and they communicate through structured protocols to divide work, share findings, and converge on solutions. Model published on HuggingFace.
Architecture
- Custom reflection mechanisms — agents evaluate their own outputs and revise before passing to others
- Inter-agent communication protocols with dynamic task allocation and sequential ordering
- Permission-based constraints controlling which agents can communicate with which
- Modular interaction system supporting different coordination patterns
Training
Fine-tuned LLaMA-3 8B using LoRA on 6.9M tokens. The LoRA approach gave an 89% compute reduction while matching the performance of the full 70B parameter model on coordination benchmarks.
Results
85% success rate across 7 complex coordination environments.