ARK (Automated Resource Knowledgebase) revolutionizes resource management via automation. Using advanced algorithms, it streamlines collection, organization, and access to resource data, facilitating efficient decision-making.

The entire codebase is in Python, except for a few shell scripts.

• openai>=1.61.0 - OpenAI Python SDK for standardizing inference engine communication and API compatibility
• pyyaml>=6.0.2 - YAML parser for configuration files (state graphs, etc.)
• pydantic>=2.10.6 - Data validation and schema definition using Python type annotations
• requests>=2.32.3 - HTTP library for making API requests to external services and tools

• fastapi>=0.115.0 - Modern, fast web framework for building the API server with automatic OpenAPI documentation
• uvicorn>=0.32.0 - ASGI server for running FastAPI applications

• psycopg2-binary>=2.9.11 - PostgreSQL adapter for Python (binary distribution, no compilation required). Used for storing conversation context and long-term memory
• mem0ai - Memory management library for vector-based memory storage and retrieval using Supabase

Install all dependencies using:

pip install -r requirements.txt

Note: psycopg2-binary is used instead of psycopg2 to avoid requiring PostgreSQL development libraries (libpq-dev) on the system. For production deployments, you may want to use psycopg2 with proper system dependencies.

(As of September 11, 2025.)

• base_module/ for main interface
• config_module/ for YAML configuration files
• model_module/ for core LLM-inference logic
• agent_module/ for agentic structure
• state_module/ for defining agent state graphs
• tool_module/ for MCP compatibility
• memory_module/ for long term memory and context management
• schemas/ for JSON schemas when communicating between frontend and backend
• state_module/
• tool_module/
• .gitignore
• README.md (this very file)
• requirements.txt (Python dependencies)

The project uses SGLang to run the Qwen 2.5-7B-Instruct model. Start the inference server:

bash model_module/run.sh

This will start the SGLang server on port 30000. The model Qwen/Qwen2.5-7B-Instruct is currently in use.

You need to run both the API server and the test interface:

1. Start the API server (in one terminal):

   cd base_module
   python app.py

   This starts the FastAPI server on port 1111, providing the /v1/chat/completions endpoint.

2. Run the test interface (in another terminal):

   cd base_module
   python main_interface.py

   This provides an interactive CLI to test the agent. Type your messages and press Enter. Type exit or quit to stop.