ValueCell is a multi-agent financial research and trading platform designed to run locally and provide API-like access to its capabilities. It orchestrates several agents (e.g., DeepResearch, Strategy, and News Retrieval) to analyze documents, synthesize market insights, and drive automated strategies. When running as a server, you can query it to obtain interpretable research summaries, track signals across multiple assets, and trigger strategy executions on configured exchanges. The server keeps sensitive data on your device, helping to preserve data security while enabling automated decision-making. Start the server and use the provided endpoints or client libraries to request agent outputs, merge insights, or execute prebuilt trading strategies.

Prerequisites:

• Python 3.12 or newer
• Git

Installation steps:

1. Clone the repository: git clone https://github.com/ValueCell-ai/valuecell-ai-valuecell.git cd valuecell-ai-valuecell

2. (Optional) Create and activate a virtual environment: python -m venv .venv source .venv/bin/activate # Linux/macOS .venv\Scripts\activate # Windows

3. Install dependencies (adjust if a requirements file exists): pip install -r requirements.txt

   or install necessary packages individually, e.g.:

   pip install some-dependency

4. Run the MCP server (example): python -m valuecell_server

5. (Optional) Configure environment variables for local data directories and API keys as needed before starting the server.

Notes:

• Ensure Python 3.12+ is installed and available on your PATH.
• If a specific entry point or module name differs, refer to the project documentation for the exact command to start the server.

Tips and common issues:\n- Data directories (LanceDB, knowledge, and SQLite DB) can be customized via environment variables VALUECELL_HOME, LANCE_DB_PATH, and KNOWLEDGE_PATH. Ensure these paths exist or the server has permission to create them.\n- If you see permission or path errors, run with elevated permissions or adjust directory permissions.\n- When integrating external data sources or exchanges, securely manage your API keys and consider whitelisting your IP if supported.\n- If the server starts but agents do not respond, check that the required dependencies for each agent (e.g., specific LLM providers or data connectors) are correctly installed and that any network access is permitted.\n- For troubleshooting, consult the local logs produced by the server and ensure Python dependencies are up to date.\n- If you need to restart services during development, use a clean virtual environment and re-install dependencies to avoid conflicts.