CommonGround: A Transparent and Collaborative AI Agent Platform

What is CommonGround?

CommonGround is an open-source multi-agent collaboration platform developed by the Intelligent-Internet team. It enables users to build, observe, and actively participate in workflows powered by teams of AI agents. With a real-time visual web interface, it allows humans to engage in task planning and execution, serving as a foundational infrastructure for human-AI co-creation.

Key features

• Multi-agent architecture: Adopts a layered design of Partner–Principal–Associates, supporting task decomposition and collaborative execution.

• Declarative configuration: Easily set up agent roles, context, tools, and behavior using YAML files—no complex coding required.

• Full workflow observability: Offers multiple real-time views—Flow (flowchart), Kanban (task board), and Timeline—to track decisions, tool calls, and state changes.

• Flexible tool integration: Allows custom Python tools or external APIs to be wrapped as callable “tools” for agents via the standard Meta-Controller Protocol (MCP).

• Model-agnostic support: Compatible with major large language models (LLMs) through LiteLLM; also supports integration with Google Gemini via Docker.

• Project and knowledge management: Built-in file management and automatic RAG (retrieval-augmented generation) for dynamic context updates and indexing.

Underlying Technology

At its core, CommonGround aims to build a shared “common ground” between humans and AI agents to enable transparent, controllable, and collaborative AI systems.
The Partner–Principal–Associate architecture supports a structured three-tier flow of “thinking – decomposition – execution.” Declarative configuration lowers the entry barrier. The MCP protocol standardizes how tools are exposed and invoked. A powerful UI enables real-time observation, debugging, and human intervention—transforming AI from a black box into a co-creative partner.

Project Address

• GitHub repository: https://github.com/Intelligent-Internet/CommonGround

Use Cases

• Strategic research & analysis: Ideal for collaborative tasks such as market research, policy studies, and multi-step reasoning.

• Consulting agent teams: Simulate AI teams with planning, research, and execution capabilities for business consulting and report generation.

• AI-assisted software development: Build AI teams that cover design, coding, testing, and deployment, with human oversight and refinement through the interface.

• Education and experimentation: Excellent for teaching purposes, allowing demonstration of multi-agent workflows and collaboration using declarative logic.

• Industry-specific API integration: Use MCP to connect external services and data from sectors like finance, healthcare, and law, enabling domain-specific agent workflows.