ValueCell – an open-source multi-agent financial platform that collaborates to provide financial insights

What is ValueCell？

ValueCell is an open-source multi-agent financial platform that leverages AI technology to accelerate financial research and investment decision-making. It breaks down various functional modules in the investment research process—such as market analysis, data collection, sentiment analysis, and strategy generation—into independent intelligent agents. These agents are then interconnected to form a collaborative and scalable investment research ecosystem.

The platform supports real-time data across multiple financial markets, including the U.S. stock market, cryptocurrency market, Hong Kong market, and Chinese market, with plans to expand further into European, Asian, and other global markets. ValueCell offers a wide range of features such as trading agents, AI hedge funds, and SEC agents, providing users with comprehensive financial insights and investment advice.

Main Features of ValueCell

Multi-Agent System:
Built-in financial agents—including trading agents, AI hedge fund agents, and SEC agents—handle different analytical tasks and work collaboratively to deliver comprehensive financial insights.

Flexible Integration:
Supports multiple LLM providers such as OpenAI and Anthropic, and is compatible with agent frameworks like LangChain and Agno, allowing users to flexibly select and integrate models according to their needs.

Multi-Market Coverage:
Provides real-time support for various markets including the U.S. stock market, cryptocurrency, Hong Kong, and China, with future expansion plans to European and Asian markets as well as commodities and other trading domains.

Personalized Configuration:
Users can customize investment preferences, risk tolerance, learning-based recommendations, and multilingual interfaces to meet individual needs.

Open-Source Ecosystem:
Developed in Python, ValueCell provides SDKs, REST APIs, and WebSocket support. Developers can freely extend and contribute new agents to collectively advance the platform’s ecosystem.

Project Repository

• GitHub: https://github.com/ValueCell-ai/valuecell

How to Use ValueCell

1. Environment Setup:
Install necessary software such as uv, bun, and Python 3.10+, ensuring all dependencies required for running ValueCell are met.

2. Clone and Install:
Clone the project repository from GitHub, copy and edit the .env file, and fill in your API keys along with model and data source preferences.

3. Configure Model Providers:
Register supported LLM providers in the .env file to ensure agent models support embeddings.

4. Launch the Application:
Run bash start.sh (Linux/macOS) or .start.ps1 (Windows) to start the ValueCell server.

5. Access the Interface:
Open http://localhost:1420 in your browser to interact with the multi-agent system via the web UI and view output results in the log files.

Application Scenarios

Individual Investors:
Gain multi-dimensional market insights and automated investment strategies to improve personal investment decisions.

Financial Researchers:
Develop, test, and optimize financial AI applications in an open-source environment using ValueCell’s data-rich, agent-based framework.

Financial Institutions:
Build customized intelligent agent systems to enhance research and risk control capabilities and optimize portfolio management.

Market Analysis:
Access and analyze real-time data from multiple markets, including stocks, crypto, and forex, to track market trends and sentiment.

Strategy Generation:
Generate personalized investment strategies based on market data and user preferences, supporting diverse asset classes.

Risk Monitoring:
Continuously monitor portfolio risk, provide alerts and adjustment suggestions, and help users manage investment risks effectively.