Matrix is a distributed, large-scale engine for multi-agent synthetic data generation and experiments: it provides the infrastructure to run thousands of “agentic” workflows concurrently (e.g. multiple LLMs interacting, reasoning, generating content, data-processing pipelines) by leveraging distributed computing (like Ray + cluster management). The idea is to treat data generation as a “data-to-data” transformation: each input item defines a task, and the runtime orchestrates asynchronous, peer-to-peer agent workflows, avoiding global synchronization bottlenecks. That design makes Matrix particularly well-suited for large-batch inference, model benchmarking, data curation, augmentation, or generation — whether for language, code, dialogue, or multimodal tasks. It supports both open-source LLMs and proprietary models (via integration with model backends), and works with containerized or sandboxed environments for safe tool execution or external code runs.
Features
- Distributed multi-agent workflow engine enabling thousands of concurrent agent interactions
- Support for mainstream LLM inference backends (open-source or proprietary) with multi-node and multi-model orchestration
- Data-pipeline utilities: generation, quality filtering, augmentation, and post-processing support for high-throughput dataset creation
- Asynchronous, peer-to-peer agent architecture — avoids global barriers, supports fine-grained scheduling, and maximizes resource utilization
- Containerized execution support (via sandbox or container wrappers) for safe execution of tool-use or external code during generation
- Configurable and modular runtime (via configuration system) adaptable to different data-generation or benchmarking tasks
Project Samples
