Castor: A Self-Hosted AI Agent Platform for Enterprise Workflows

Castor is a self-hosted AI agent platform for enterprise scenarios, designed to resolve the dilemma enterprises face when using AI assistants—data security risks with SaaS services versus high engineering costs of self-hosted solutions. Its core advantages include: full data localization, support for any OpenAI-compatible LLM, multi-channel interaction (Web UI/terminal/Telegram), and applicability to tasks like customer operations, internal automation, knowledge retrieval, and scheduled reports, providing enterprises with out-of-the-box and controllable AI automation capabilities.

With the popularity of AI assistants today, enterprise users face a key contradiction: SaaS services are convenient but data is separated from their own infrastructure, while self-hosted solutions require significant engineering investment. Castor's core philosophy is "The system takes on heavy tasks, the model remains flexible"—through system-level capabilities such as tool search, semantic memory, and scheduler, it allows LLMs to focus on reasoning and decision-making, avoiding interference from lengthy contexts, and adapting to various scales from local models with 4B parameters to cloud-based large models.

Runtime Architecture

Castor's runtime architecture supports multiple interaction entry points (CLI/Web UI/Telegram Bot), with the core Agent Loop connecting semantic memory (Qdrant), RAG, SQLite (state storage), tool ecosystem, skill system, browser automation, MCP integration, and scheduler.

LLM Compatibility

Supports any OpenAI-compatible API endpoint:

• Hosted services: Azure OpenAI, AWS Bedrock, OpenAI, Groq, etc.
• Local deployment: LM Studio, Ollama Users can switch providers across threads without restarting.

Embedding Model

By default, it uses FastEmbed (multilingual-MiniLM, 384 dimensions, supporting over 50 languages), runs on pure CPU based on ONNX, and can be used smoothly without a GPU.

Castor vs. Hosted SaaS Agents Comparison

Dimension	Castor	Hosted SaaS Agent
Data Control	Fully local, no cross-border transfer	Sent to service provider
Model Selection	Any OpenAI-compatible endpoint	Locked to provider's models
Customization	Full code + skills + personality	System prompts + few hooks
Cost Model	Only LLM call fees	Seat-based/action-based billing
Compliance Audit	Self-built audit trail	Depends on provider's compliance
Hardware Access	Native USB/serial port support	None
Reliability	No service provider outage risk	Depends on provider's SLA

Core Capability Matrix

Castor has capabilities such as multi-channel interaction, tool ecosystem (8 core + search), semantic memory (RAG), browser automation, MCP integration, scheduled tasks, direct hardware connection, and visual canvas.

Typical Application Scenarios

• Customer Operations: Consultation classification routing, intelligent replies, ticket tracking
• Internal Processes: Scheduled reports, data synchronization, approval automation
• Knowledge Retrieval: Document semantic search, code Q&A, meeting summaries
• Hardware Integration: Weighing data collection, scanner inventory updates, PLC monitoring

System Requirements

• Hosted LLM Deployment: Modern laptop/small VM, agent process uses ~300MB memory
• Local LLM Deployment: Minimum 4GB GPU memory (for 4B models), 8GB RAM; recommended 8GB GPU memory, 16GB RAM

Installation Methods

• Linux/macOS: curl -fsSL https://raw.githubusercontent.com/deepfounder-ai/castor/main/install.sh | bash
• Windows: git clone https://github.com/deepfounder-ai/castor.git && cd castor && setup.bat
• Manual Installation: Clone the repository → Create a virtual environment → Install dependencies → Verify

Run Commands

• castor: Terminal chat
• castor --web: Web UI (http://localhost:7860)
• castor --doctor: Diagnostic check

Security & Privacy

• Data Sovereignty: All data remains on the user's infrastructure, supporting fully offline operation
• Access Control: API key authentication, thread isolation, tool permission configuration
• Audit Capability: Complete conversation history, tool call logs, compliance report export

Community Resources

• Telegram Community: https://t.me/castor_ai
• GitHub Issues: Feedback and feature requests
• Documentation: docs/README.md

Castor represents an important direction for enterprise-grade AI agents: under the premise of ensuring data sovereignty, it provides functional experiences comparable to commercial SaaS. Its modular design, multi-LLM support, and hardware integration capabilities make it particularly suitable for enterprises with high compliance requirements, sensitive data, or needs for physical device interaction. For teams looking to upgrade AI from an experiment to a production tool, Castor is a practical and scalable choice.