by ybeven · MCP Server · ★ 181
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4D-ARE: Attribution-Driven Agent Requirements Engineering Build LLM agents that explain why, not just what. The Problem Your LLM agent has full data access and executes flawlessly. But when asked: "Why is our customer retention rate only 56%?" It returns a list of metrics instead of a causal explanation: This is the Attribution Gap - agents can report what happened, but struggle to explain why. The Solution 4D-ARE provides a framework for building agents that trace causal chains through 4 dimensions: Instead of a metric dump, you get: Quick Start Installation Basic Usage python from fourdare i
| Stars | 181 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 45.25/100 |
| Last Updated | 2026-01-09 |
| Created | 2026-01-09 |
| Platforms | mcp, python |
| Est. Tokens | ~3k |
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4D-ARE is Build LLM agents that explain why, not just what. Attribution-driven agent requirements engineering framework. Based on the 4D-ARE Paper - https://arxiv.org/abs/2601.04556. It is categorized as a MCP Server with 181 GitHub stars.
4D-ARE is primarily written in Python. It covers topics such as agents, causal-reasoning, llm.
You can find installation instructions and usage details in the 4D-ARE GitHub repository at github.com/ybeven/4D-ARE. The project has 181 stars and 19 forks, indicating an active community.
4D-ARE is released under the MIT license, making it free to use and modify according to the license terms.
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