Research Assistant Guide
Research Assistant Review We've covered a few major LangGraph themes: * Memory * Human-in-the-loop * Controllability Now, we'll bring these ideas together to tackle one of AI's most popular applications: research automation. Research is often laborious work offloaded to analysts. AI has considerable potential to assist with this. However, research demands customization: raw LLM outputs are often poorly suited for real-world decision-making workflows. Customized, AI-based research and report generation workflows are a promising way to address this. Goal Our goal is to build a lightweight, multi-agent system around chat models that customizes the research process. Source Selection * Users can choose any set of input sources for their research. Planning * Users provide a topic, and the system generates a team of AI analysts, each focusing on one sub-topic. * Human-in-the-loop will be used to refine these sub-topics before research begins. LLM Utilization * Each analyst will conduct in-depth interviews with an expert AI using the selected sources. * The interview will be a multi-turn conversation to extract detailed insights as shown in the STORM paper. * These interviews will be captured in a using sub-graphs with their internal state. Research Process * Experts will gather information to answer analyst questions in parallel. * And all interviews will be conducted simultaneously through map-reduce. Output Format * The gathered insights from each interview will be synthesized int
When to use Research Assistant
Research Assistant Review We've covered a few major LangGraph themes: * Memory * Human-in-the-loop * Controllability Now, we'll bring these ideas together to tackle one of AI's most popular applications: research automation. Research is often laborious work offloaded to analysts. AI has considerable potential to assist with this. However, research demands customization: raw LLM outputs are often poorly suited for real-world decision-making workflows. Customized, AI-based research and report generation workflows are a promising way to address this. Goal Our goal is to build a lightweight, multi-agent system around chat models that customizes the research process. Source Selection * Users can choose any set of input sources for their research. Planning * Users provide a topic, and the system generates a team of AI analysts, each focusing on one sub-topic. * Human-in-the-loop will be used to refine these sub-topics before research begins. LLM Utilization * Each analyst will conduct in-depth interviews with an expert AI using the selected sources. * The interview will be a multi-turn conversation to extract detailed insights as shown in the STORM paper. * These interviews will be captured in a using sub-graphs with their internal state. Research Process * Experts will gather information to answer analyst questions in parallel. * And all interviews will be conducted simultaneously through map-reduce. Output Format * The gathered insights from each interview will be synthesized int
How to use Research Assistant
Research Assistant is a multi-agent crew agent built on the LangGraph framework. Set it up from the source repository, configure your model credentials, and invoke it for tasks that match its description. Review the safety profile below before running it against production data or systems.
Safety profile
Autonomy
Semi-autonomous
Sandbox-aware
No declared sandbox guidance
Network access
Unspecified
Filesystem access
Unspecified