Built an agentic RAG (Retrieval-Augmented Generation) application using LangChain, OpenAI APIs, and vector retrieval for complex multi-step queries. The agent is designed to handle the GAIA benchmark — a challenging evaluation framework that tests AI systems on real-world tasks requiring multi-step reasoning, tool use, and knowledge retrieval.

Achieved a 90% score on the GAIA benchmark, earning the Top Performer certification from Hugging Face. The benchmark evaluates AI assistants on tasks that require browsing the web, analyzing documents, executing code, and combining information from multiple sources to answer complex questions.

Engineered a multi-tool agent architecture with robust guardrails and context management. The agent dynamically selects between document retrieval, web search, code execution, and direct reasoning based on the task requirements. Each tool is wrapped with error handling and retry logic to ensure reliable execution across diverse query types.

The system implements sophisticated context management to maintain coherence across multi-step reasoning chains. Vector retrieval enables efficient document search, while the agent's planning module breaks complex queries into manageable sub-tasks, executing them in sequence and synthesizing the results into comprehensive answers.

The GAIA Agent is deployed on Hugging Face Spaces and available for interactive testing. You can submit queries and observe the agent's multi-step reasoning process in real-time.