Self-Correcting Multi-Agent Research System
What it is
A Python-based multi-agent system that decomposes complex queries into steps, retrieves evidence using vector search, and synthesizes answers. It includes a critic agent that iteratively evaluates and refines the output to improve reliability and transparency.
The problem it solves
Standard LLMs often produce one-shot answers that may be incomplete or incorrect without a mechanism to verify, critique, or iteratively improve the output.
What you could use it for
- Answering complex multi-hop research questions with verifiable evidence
- Generating structured research plans for information retrieval tasks
- Improving the accuracy of LLM-generated answers through iterative self-correction
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