SwarmResearch: Orchestrating Coding Agents for Open-Ended Discovery

Long-running coding agents such as autoresearch can persistently discover optimizations for open-ended problems. However, they tend to converge onto a single high-level approach, then proceed with low-level edits while missing superior approaches to the problem. We hypothesize two harness-level design choices contribute to this behavior: accumulating context in a single long-running agent and only exposing a single program state to edit. We introduce SwarmResearch, a type of orchestrator-subagent harness in which a Shepherd Agent uses global context to steer a population of Search Agents without prescribing exact tasks, while each Search Agent operates with local context in its own git branch. On open-ended optimization tasks, SwarmResearch discovers better or comparable solutions to state-of-the-art LLM-guided evolution and multi-agent techniques on 13/15 tasks, driven by higher-level exploration. Compared with fixed scaling of serial and parallel agents, SwarmResearch's orchestrator-guided scaling discovers better solutions by adapting parallelism at different search depths.