by redhat-community-ai-tools · MCP Server · ★ 40
UnifAI A platform for building and running multi-agent AI workflows over your enterprise knowledge. UnifAI lets you connect internal data sources — Slack, Jira, documents — into a unified vector store, then query them through composable, visual multi-agent pipelines. Define agent graphs as YAML blueprints or build them with a drag-and-drop UI, execute locally or at scale, and stream results in real time. What It Does Most teams have knowledge scattered across Slack threads, Jira tickets, PDFs, and internal wikis. Finding answers means manually digging through multiple systems.
| Stars | 40 |
| Forks | 20 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 64.4284998084431/100 |
| Open Issues | 19 |
| Last Updated | 2026-07-07 |
| Created | 2025-11-20 |
| Platforms | k8s, mcp, python |
| Est. Tokens | ~15k |
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UnifAI is Production-grade multi-agent orchestration engine. Compose agentic workflows from a pluggable catalog of Agents, LLMs, tools, and retrievers. Execute locally with LangGraph or distributed with Tempora. It is categorized as a MCP Server with 40 GitHub stars.
UnifAI is primarily written in Python. It covers topics such as a2a-protocol, agent-orchestration, ai-agents.
You can find installation instructions and usage details in the UnifAI GitHub repository at github.com/redhat-community-ai-tools/UnifAI. The project has 40 stars and 20 forks, indicating an active community.
UnifAI is released under the Apache-2.0 license, making it free to use and modify according to the license terms.