[GitHub Trending] jordan-gibbs/hyperresearch

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Agent-driven research knowledge base; directly relevant to AI agent workflows and data engineering.

AI/ML github.com

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Why It Matters

For platform engineers building AI agent systems, Hyperresearch showcases how to implement complex, auditable multi-step pipelines with adversarial critics, tool-locked patching, and tier-adaptive routing—architecture patterns directly applicable to production agent orchestration with Claude Code or similar LLM tool-use frameworks.

Key Takeaways

Evaluate Hyperresearch as a reference architecture for building deep research agents with structured pipelines, adversarial validation, and persistent memory layers instead of treating agent loops as simple invoke-generate cycles.

Agent-driven research knowledge base. Agents collect, search, and synthesize web research into a persistent, searchable wiki. - jordan-gibbs/hyperresearch

Summary

Hyperresearch turns Claude Code into a state-of-the-art deep research agent using a 16-step adversarial pipeline, currently leading the DeepResearch-Bench RACE leaderboard. It processes 250+ sources per run with automated citation verification, independence auditing to eliminate syndicated sources, and legal open-access paper retrieval via Unpaywall and Europe PMC. The tool persists all sources in a SQLite vault for session reuse and supports resumable runs from 30 minutes to 80,000-word dissertation chapters.

Author

jordan-gibbs