ValueCell is a community-driven, multi-agent platform for financial applications. Our mission is to build the world's largest decentralized financial agent community.

It provides a team of TOP investment Agents to help you with stock selection, research, tracking, and even trading.

Welcome to join our Discord community to share feedback and issues you encounter, and invite more developers to contribute 🔥🔥🔥

Note: ValueCell team members will never proactively contact community participants. This project is for technical exchange only. Investing involves risk. ⚠️

• DeepResearch Agent: Automatically retrieve and analyze fundamental documents to generate accurate data insights and interpretable summaries
• Auto Trading Agent: Support for multiple crypto assets and AI-powered trading strategies, creating automated trading based on technical indicators
• News Retrieval Agent: Supports personalized scheduled news delivery to track key information in real time
• Trading Agents: Agents work for market analysis, sentiment analysis, news analysis, and fundamentals analysis
• AI-Hedge-Fund: Agents collaborate to provide comprehensive financial insights
• Others: More agents are in planning...

• Multiple LLM Providers: Support OpenRouter, SiliconFlow, Google and OpenAI
• Popular Market Data: Cover US market, Crypto market, Hong Kong market, China market and more
• Multi-Agent Framework Compatible: Support Langchain, Agno by A2A Protocol for research and development integration
• Exchange Connectivity: Optional live routing to OKX with built-in guardrails

ValueCell is a Python-based application featuring a comprehensive web interface. Follow this guide to set up and run the application efficiently.

For optimal performance and streamlined development, we recommend installing the following tools:

uv - Ultra-fast Python package and project manager built in Rust
bun - High-performance JavaScript/TypeScript toolkit with runtime, bundler, test runner, and package manager

1. Clone the repository

   git clone https://github.com/ValueCell-ai/valuecell.git
   cd valuecell

2. Configure environment variables

   cp .env.example .env

   Edit the .env file with your API keys and preferences. This configuration file is shared across all agents. See Configuration Guide for details.

More detailed configuration information can be found at CONFIGURATION_GUIDE

Configure your preferred model providers by editing the .env file:

• Simple Setup: Just configure the model provider's API Key

• Advanced Configuration: For research-type agents, you need to configure more environment variables. Please refer to the .env.example file for details.

• Official Recommendation: Configure OpenRouter + any supplier that provides embedding models. Reason: This enables quick model switching across providers and provides RAG+Memory AI capabilities

Choose your preferred models and providers based on your requirements and preferences.

Launch the complete application (frontend, backend, and agents):

bash start.sh

.\start.ps1

• Web UI: Navigate to http://localhost:1420 in your browser
• Logs: Monitor application logs at logs/{timestamp}/*.log for detailed runtime information of backend services and individual agents

Before running the application, ensure all prerequisites are installed and environment variables are properly configured. If it has been a long time since the last update, you can delete the database files in the project (lancedb/, valuecell.db, .knowledgebase/) and start again.

Once the application is running, you can explore the web interface to interact with ValueCell's features and capabilities.

• Set AUTO_TRADING_EXCHANGE=okx and populate the required OKX_* credentials in .env (see Configuration Guide and OKX Setup).
• Start the stack with ./start.sh after configuring the environment.
• Keep OKX_ALLOW_LIVE_TRADING=false until strategies pass paper trading validation and stakeholders approve mainnet deployment.

Note: Ensure all prerequisites are installed and environment variables are properly configured before running the application.

• Crypto: Support more exchanges
• Securities: Gradually support AI securities trading

• European Markets: Add support for FTSE, DAX, CAC 40, and other European exchanges
• Asian Markets: Expand coverage to Nikkei and emerging Asian markets
• Commodity Markets: Oil, Gold, Silver, Agricultural products analysis
• Forex Markets: Major currency pairs and cross-currency analysis

• Fixed Income: Government bonds, corporate bonds, and yield analysis agents
• Derivatives: Options, futures, and complex financial instruments
• Alternative Investments: Private equity, hedge funds, and venture capital analysis

• Real-time Alerts: Price movements, volume spikes, and technical breakouts
• Scheduled Reports: Daily/weekly/monthly portfolio summaries
• Event-driven Notifications: Earnings releases, dividend announcements, regulatory changes
• Custom Triggers: User-defined conditions and thresholds
• Multi-channel Delivery: Discord and webhook integrations

• Desktop Support: Gradually support desktop and client capabilities
• Database Hot Updates: Gradually support compatibility upgrades

• Multi-language Support: English, Chinese (Simplified/Traditional), Japanese, Korean, Spanish, French
• Localized Market Data: Region-specific financial terminology and formats
• Cultural Adaptation: Time zones, date formats, and currency preferences
• Agent Personality Localization: Culturally appropriate communication styles

• API Key Management: Secure storage and rotation of third-party API keys
• OAuth Integration: Support for major financial data providers

• Investment Profile: Risk tolerance, investment horizon, and strategy preferences
• UI/UX Customization: Dark/light mode, dashboard layouts, and widget preferences
• Agent Behavior: Communication frequency, analysis depth, and reporting style
• Portfolio Management: Custom benchmarks, performance metrics, and allocation targets

• Conversation History: Persistent chat history across sessions
• User Learning: Adaptive recommendations based on user behavior
• Market Memory: Historical context and pattern recognition
• Preference Evolution: Dynamic adjustment of recommendations over time

• Python SDK: Comprehensive library for agent integration and customization
• WebSocket Support: Real-time data streaming and bidirectional communication

• Plugin Architecture: Easy integration of third-party agents and tools
• Agent Registry: Marketplace for community-contributed agents

• Interactive API Explorer: Swagger/OpenAPI documentation with live testing
• Code Examples: Sample implementations in multiple programming languages
• Testing Framework: Unit tests, integration tests, and mock data providers