ProspectAI
ProspectAI is an open-source stock research tool that harnesses a six-agent CrewAI pipeline to automate financial analysis. Built with CrewAI, a framework for orchestrating role-playing AI agents, ProspectAI deploys specialized agents that collaborate to gather, process, and synthesize stock-related information. Each agent is assigned a distinct role—such as market sentiment analyzer, fundamental metrics calculator, technical indicator interpreter, news aggregator, risk assessor, and report compiler—working together to produce comprehensive investment insights. The pipeline can be configured with various large language models (LLMs), including OpenAI's GPT series, Anthropic's Claude, or locally hosted models, giving users flexibility in balancing cost and performance. Agents draw data from sources like Yahoo Finance, Alpha Vantage, or custom APIs, and can be extended with additional tools for web search or social media monitoring. The output is typically a structured report covering valuation, growth prospects, red flags, and a summary investment thesis. By breaking down stock research into discrete, agent-managed tasks, ProspectAI dramatically reduces the manual effort required by investors and analysts. The modular architecture allows users to swap in new agents, adjust the analysis workflow, or add entirely new capabilities, such as options analysis or ESG scoring. CrewAI’s underlying orchestration ensures that agents share context, remember previous interactions, and respect a defined sequential or parallel execution order. This makes the pipeline both powerful and transparent, as users can inspect the intermediate reasoning of each agent. ProspectAI is likely distributed as a Python package or directly from its GitHub repository, making it accessible to developers and technically-minded investors. Installation may involve cloning the repo, setting up a Python virtual environment, and installing dependencies with pip. Configuration of LLM API keys and data sourc
- Category
- Analytics
- Pricing
- Free
Problem solved
Individual investors, financial analysts, and portfolio managers spend countless hours manually gathering, normalizing, and interpreting disparate data points for stock research. This process is repetitive, prone to oversight, and difficult to scale. ProspectAI solves this by orchestrating a team of AI agents that automatically collect financial statements, market data, news sentiment, and technical signals, then collaboratively reason about the investment merits of a stock. The result is a comprehensive, unbiased research report generated in a fraction of the time, allowing users to focus on decision-making rather than data wrangling.
Use cases
- Generating a detailed investment thesis for a single stock by combining fundamental, technical, and sentiment analysis.
- Screening a large watchlist of stocks to identify those meeting specific value or growth criteria.
- Automating the daily monitoring of portfolio holdings for material news events or technical breakdowns.
- Running competitive analysis between two or more stocks in the same sector to inform stock-picking decisions.
- Teaching stock analysis methodologies through an interactive, agent-driven demonstration.
- Backtesting how historical news and financial data would have influenced an agent-generated rating.
Audience
- Developers
- Researchers
- Business Users
- Startups
Pros
- Multi-agent collaboration that mirrors a real research team, producing richer insights than a single LLM call.
- Highly extensible – new agents, data sources, and analysis tasks can be added with minimal code changes.
- Open-source and free, with no vendor lock-in; users can self-host and inspect all logic.
- Compatible with multiple LLM providers, allowing users to optimize for cost or capability.
- Transparent workflow with agent interaction logs, making the analysis auditable and explainable.
Cons
- Requires technical expertise to set up and configure (Python environment, API keys, data source credentials).
- LLM API calls incur usage costs, which can grow rapidly when analyzing many stocks or using expensive models.
- Out-of-the-box coverage may be limited to major exchanges; exotic instruments require custom integration.
- Lacks a polished commercial UI; interaction is primarily via command line or rudimentary web interface.
Official website: https://prospect-ai.moisesprat.dev/
Canonical LambdaBase page: https://www.lambdabase.com/tools/prospectai