Deep Research Agent: Applications for Finance and Medicine
Tip: Use the search box at the top of this page to find specific content.
Welcome to the The AI Alliance: Deep Research Agent: Applications for Finance and Medicine. This project started as an example application of using a deep research agent for financial research and recently a medical research application was added, demonstrating the universality and flexibility of the deep research approach and the underlying tool kits.
Note: Do you have domain expertise, especially in finance, medical, legal, automation, industrial processes, etc.? Do you have AI agent expertise? Or, do you want to grow your expertise in these areas. Please join us! See our contributing page for details.
What Is It?
This applications use a deep research agent designed to collect comprehensive information from public sources and generate detailed research reports.
The finance application targets financial analysts or investors as users, who want to research publicly-traded companies.
The medical application targets interested “lay” people and eventually domain experts who want to do research on medical conditions, pharmaceuticals, etc. (This application is currently in its early stages…)
About
The applications leverage AI to perform automated research, analysis, and reporting. In fact, they are a thin veneer over a sophisticated agent framework, mcp-agent, a framework for creating AI agents with Model Context Protocol (MCP) integration, from LastMile AI, and application library code built on top of it.
The finance research application gathers data from multiple reliable financial sources to create structured investment reports with:
- Basic stock information and metrics
- Business overviews and revenue analysis
- Recent news and market events
- Financial performance summaries
- Risk and opportunity assessments
- Investor sentiment analysis
The medical research application currently uses medical MCP servers and web search, prioritizing known-reliable and freely-accessible sources.
Other applications are planned. Possibilities include legal research, general science, industrial processes, including automation, etc.
The project README provides extensive information on running the applications, configuring them, and how to create new applications.
On our roadmap are plans to demonstrate techniques for testing AI-enabled applications, where model outputs are not deterministic, i.e., 100% predictable for a given set of inputs. Developers are accustomed to testing deterministic components and applications, but AI experts have the techniques developers can use to be confident their AI-enabled application works as designed. See Testing Generative AI Applications for more on this topic.
Try It!
The project README describes how to install the dependencies and run the applications. Once set up, the following make commands provide an easy way to get help on the commands and run them.
# For the default finance app:
make list-apps # List the currently supported applications.
make app-help-finance # Details on running the finance app.
make app-run-finance # Run the finance app with default arguments.
make -n app-run-finance # Show the command that would be run, but don't run it.
make app-medical-help # Details on running the medical app.
make app-medical-run # Run the medical app with default arguments.
make -n app-run-medical # Show the command that would be run, but don't run it.
# For additional information:
make help # General help about the make targets.
NOTE:
The
app-run-*commands shown will invoke the inference service (OpenAI by default), incurring charges.