hyperresearch vs Search-R1
Deep-research harness for Claude Code: a 16-step, tier-adaptive pipeline with adversarial critics, cite-checking and 250+ sources per run, every source kept in a persistent markdown+SQLite vault. — versus — RL framework for training reasoning-and-search interleaved LLMs — the open recipe behind DeepSeek-R1-style search agents: PPO/GRPO on veRL, any search backend, models and data on HF. Two papers.
Two routes to a deep-research agent: hyperresearch engineers the harness around a frontier model (16 steps, adversarial critics), search-r1 trains the search behaviour into the weights with RL.
| hyperresearch | Search-R1 | |
|---|---|---|
| Stars | 3.4k | 5.4k |
| Forks | 336 | 489 |
| Language | Python | Python |
| License | MIT | Apache-2.0 |
| Last activity | 7 days ago | 10 months ago |
| Topics | agents, rag, skills | training |
| Curated connections | 6 | 3 |
hyperresearch — the curator's take
The most serious open deep-research pipeline you can run inside Claude Code: width sweep, contradiction graph, parallel depth investigators, four adversarial critics and a tool-locked patcher that can only apply surgical edits. Scholarly search fans out to OpenAlex/Crossref/CORE/EDGAR/FRED and it fetches legal OA full-text for paywalled papers instead of citing abstracts. Use it for reports you would otherwise pay a research analyst for; the vault means the second run on a topic starts warm. Not for quick lookups - even the 5-step fast path is minutes and the full pipeline is 30 min to hours of Claude Code time, plus your Max quota. Locked to Claude Code (uses the Skill tool for step loading); last30days-skill is the fast social-signal alternative, openresearcher is the trained-model route.
Search-R1 — the curator's take
The reference open recipe for teaching a model WHEN to search, not just how to prompt it — full experiment logs on W&B, models and data released, and Tinker featured it. If you're building deep-research agents by training rather than orchestration, start here. When NOT: this is research infrastructure — GPUs, veRL, retrieval corpora — not something you bolt onto an app; for prompt-time search agents an orchestration framework is the cheaper 95%.