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RAGentA
by faerber-lab
Multi-agent RAG for attributed question answering and provenance-aware answers
Python
Updated Jul 10, 2025
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How It Works
Implements a multi-agent retrieval-augmented generation (RAG) approach for attributed question answering. Agents retrieve, propose answers, and argue provenance so final responses include source attribution and agent-level contributions. Designed for research and challenge settings, it focuses on agent collaboration and accountable answer assembly.
The Value Proposition
As agents make decisions using external sources, attribution and cross-agent scrutiny become essential to trust their outputs. RAGentA surfaces which agent relied on which evidence and how answers were aggregated, enabling evaluation of agent reliability and provenance. This helps shift evaluation from single-shot benchmarks to assessing agent-to-agent behavior and traceable answers.
Target Use Cases
Researchers and engineers experimenting with multi-agent retrieval, provenance tracking, and accountable QA pipelines.
Applications
- Evaluating how multiple agents retrieve and attribute sources for QA
- Building provenance-aware QA pipelines that record which agent contributed which evidence
- Experimenting with agent collaboration patterns and error modes in RAG workflows
Topics
large-language-modelmulti-agent-systemquestion-answeringretrieval-augmented-generation
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Keywords
multi-agent trustretrieval-augmented-generationagent-evaluation