# Paperclip Paperclip is an agent-native CLI for searching, retrieving, and synthesizing insights from 8M+ biomedical papers. Built by Generative Expert Labs (gxl.ai), it targets the gap left by stateless literature APIs; agents need persistent context to deep-dive across a corpus, not one-shot searches. ## Commands - `search` — hybrid BM25 + embedding retrieval with TL;DR summaries - `grep` — corpus-wide regex matching, reportedly 36-294x faster than native grep - `map` — parallel query across many papers (~8x speedup vs sequential) - `ask-image` — VLM analysis of paper figures without downloading - `sql` — read-only metadata queries (author, journal, date, source) - `from` — stateful persistence of result sets for chained operations ## Why it matters Most agentic research tools treat each API call as isolated. Paperclip's `from` command persists paper subsets between calls, so agents can narrow a search, then grep, then map across the same set without rebuilding context each time. This turns literature review into a composable pipeline. ## Integration - MCP server; integrates directly with [[Claude Code]] and other MCP clients - Complements Sy, gxl.ai's human-facing literature search agent ## References - Blog post: https://gxl.ai/blog/paperclip - Website: https://gxl.ai ## Related - [[Model Context Protocol (MCP)]] - [[Claude Code]] - [[Large Language Models (LLMs)]] - [[Embeddings]]