NeuroAIHub/BrainPilot

What it solves

BrainPilot is an AI research workspace designed specifically for brain science. It automates the complex, evidence-heavy workflows of neuroscience research—such as literature reviews, hypothesis refinement, experiment design, data analysis, and scientific auditing—while keeping the human researcher in the loop to maintain control and oversight.

How it works

The system employs a multi-agent architecture coordinated by a Principal Investigator (PI) Agent. This PI Agent manages a team of specialist agents, including a Librarian, Experimentalist, Engineer, Writer, and Auditor. To ensure scientific reliability, the Auditor Agent specifically reviews claims, evidence chains, and citations to mitigate hallucination risks.

Research processes are recorded as a "Graph of Trace" (GoT), which makes every agent action, decision point, and piece of evidence visible and inspectable. The system integrates a built-in library of validated domain skills (methodological guides) and connects to external tools via MCP servers, paper databases, and code execution environments.

Who it’s for

It is built for neuroscience and brain science researchers who need to automate tedious research tasks without sacrificing the transparency and rigor required for scientific publication.

Highlights

• Specialized Multi-Agent Team: A PI agent coordinates specialists (Librarian, Experimentalist, Engineer, Writer, Auditor).
• Scientific Auditing: A dedicated Auditor agent checks for unsupported reasoning and hallucination risks.
• Graph of Trace: Every session is represented as an inspectable graph, ensuring the research process is transparent.
• Extensible Knowledge Base: Includes 72 bundled skills across 21 research domains, with support for adding custom methodology libraries.
• Human-in-the-Loop: Designed to let researchers guide the process while agents handle the heavy lifting of data and literature.