papi

Paperpipe is a specialized research-to-code utility designed for developers, researchers, and AI agents. It bridges the gap between static PDF research papers and active code implementation by maintaining a structured local database. The agent helps you avoid hallucinations by grounding technical implementation in extracted equations, LaTeX source files, and coding-oriented summaries rather than general-purpose summaries. It serves as an essential tool for cross-referencing mathematical definitions, understanding architectural diagrams from extracted figures, and tracking implementation notes.

• Efficient local database management for academic papers via CLI, supporting arXiv IDs, URLs, and local files.

• Automated extraction and organization of key technical artifacts including equations, LaTeX source code, and high-level summaries for implementation.

• Hybrid search capabilities combining fast literal ripgrep (rg) matching, ranked BM25 search, and semantic RAG integration via PaperQA2 or LEANN backends.

• Seamless integration with coding agents (like Claude Code or Gemini) allowing the agent to fetch citations, page-specific quotes, and verified math during the coding process.

• Cross-paper synthesis capabilities to compare different research approaches, parameter counts, and methodology for complex implementation decisions.

• Metadata tracking and tag-based organization to manage large collections of implementation-focused literature.

• Always prefer the papi CLI for direct lookups to save latency; escalate to RAG-based tools (papi ask, leann_search, retrieve_chunks) only when semantic synthesis or cross-paper reasoning is required.

• The database structure at ~/.paperpipe/ contains critical files such as equations.md, source.tex, and figures/ that should be leveraged when debugging logic or model architecture.

• Utilize the papi export command to move paper-specific context directly into your project repository when preparing for agent sessions.

• Primary inputs include paper identifiers or search terms; primary outputs are precise technical specifications, citable quotes, or synthesized answers focused on practical code implementation.

• Ensure you have the appropriate backend dependencies installed (e.g., [all] for full RAG and figure extraction support) to unlock the full potential of the assistant.