Tribe is a low-code platform for building and coordinating multi-agent teams, targeting users who want to leverage AI for complex tasks without extensive coding. It simplifies the creation of agent workflows, enabling faster and more efficient problem-solving by distributing tasks among specialized agents.

How It Works

Tribe utilizes the LangGraph framework to facilitate the customization and coordination of agent teams. It supports both sequential workflows, where agents execute tasks in a defined order, and hierarchical workflows, where a team leader delegates subtasks to team members. This modular approach allows agents to focus on specific skills, enhancing overall performance and task completion.

Quick Start & Requirements

• Installation: Deploy locally using Docker.
• Prerequisites: Docker, Python (for generating secret keys). Environment variables SECRET_KEY, FIRST_SUPERUSER_PASSWORD, and POSTGRES_PASSWORD must be set.
• Resources: Local deployment via Docker is described as simple and quick.
• Links: Contribution Guide, Release Notes

Highlighted Details

• Supports persistent conversations and real-time observability via LangSmith.
• Enables tool calling, Retrieval Augmented Generation (RAG), and human-in-the-loop approvals.
• Allows integration of open-source LLMs (e.g., Llama, Gemma, Phi) via Ollama or OpenAI-compatible endpoints.
• Offers multi-tenancy and public API endpoints for external application integration.

Maintenance & Community

The project welcomes community contributions. Details on contributing, reporting bugs, and suggesting features are available.

Licensing & Compatibility

Licensed under the MIT license, permitting commercial use and integration with closed-source applications.

Limitations & Caveats

The project is under heavy development, with potential for significant changes. Custom embedding models may require recreating Qdrant collections if vector dimensions differ.