Faros is a blueprint-driven runtime for orchestrating multi-agent AI research workflows that span from topic formulation through literature review, experimental design, code generation, execution, paper writing, and peer review.
The tool addresses the gap between loosely-chained language model calls and rigorous scientific practice. Rather than passing unstructured text between stages, Faros uses a PlanPackage contract that formalizes assumptions, variables, procedural steps, acceptance criteria, and evidence citations. It grounds research ideas in literature evidence before generation, maintains traceable identifiers linking code, experiments, figures, and papers, and implements ReviewX to map peer review feedback to specific claims, evidence gaps, or experimental issues with actionable next steps. The system preserves sources, decisions, artifacts, and human feedback at each stage, creating an auditable research loop rather than a black box.
Faros suits teams conducting AI and LLM research who need to move beyond prototype workflows toward reproducible, evidence-backed scientific practice. The underlying architecture uses Blueprint, Capability, Profile, and Provider abstractions, positioning it for extension beyond its current focus. Adoption makes sense where human oversight at critical junctures—plan approval, experiment interpretation, final review—is valued alongside automation, and where traceability and revisability of research decisions matter more than speed alone.
Development shows active investment in test coverage and stability, with substantial backend and frontend test suites passing. The project maintains dual-language documentation and has integrated support for specific model providers, indicating attention to both usability and practical deployment concerns.
