JSPM

  • ESM via JSPM
  • ES Module Entrypoint
  • Export Map
  • Keywords
  • License
  • Repository URL
  • TypeScript Types
  • README
  • Created
  • Published
  • Downloads 351
  • Score
    100M100P100Q69545F
  • License MIT

AI-powered research assistant for scientists — literature search, data analysis, academic writing, and project management

Package Exports

    This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (research-copilot) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

    Readme

    Research Copilot

    An AI-powered desktop research assistant for scientists and academics. Literature search, data analysis, academic writing, and project management — all in one place.

    Built on pi-mono (agent runtime) + Electron + React.

    Main Interface

    API Keys Setup (READ THIS FIRST)

    Research Copilot requires API keys to function. The easiest way is to enter them directly in the app — on first launch you'll see a setup screen. Keys are saved to ~/.research-copilot/config.json.

    Alternatively, add them to your shell profile (~/.zshrc, ~/.bashrc, etc.):

    # ===== REQUIRED (at least one) =====
export OPENAI_API_KEY="sk-..."           # For OpenAI models (GPT-4o, GPT-5, o3, etc.)
export ANTHROPIC_API_KEY="sk-ant-..."    # For Anthropic models (Claude Sonnet, Opus, etc.)

# ===== RECOMMENDED =====
export BRAVE_API_KEY="BSA..."            # For web search (https://brave.com/search/api/)
export OPENROUTER_API_KEY="sk-or-..."    # For AI-generated scientific diagrams (https://openrouter.ai/)

    Then reload your shell: source ~/.zshrc

    What happens without each key?

    Key Required? What it powers Without it
    OPENAI_API_KEY Yes (if using OpenAI models) Core AI agent — all chat, coding, writing, analysis App cannot start the agent. You'll see an error dialog on first message.
    ANTHROPIC_API_KEY Yes (if using Anthropic models) Core AI agent (same as above, for Claude models) Same — agent won't initialize for Claude models.
    BRAVE_API_KEY Recommended web_search tool — general web search via Brave Search Graceful fallback: web search automatically degrades to arXiv-only (academic papers). No general web results.
    OPENROUTER_API_KEY Optional scientific-schematics skill — AI-generated diagrams, flowcharts, graphical abstracts The schematics skill fails when invoked. All other skills (writing, visualization, data analysis) work fine.

    Minimum viable setup: You need at least one of OPENAI_API_KEY or ANTHROPIC_API_KEY to use the app. Everything else enhances the experience but is not strictly required.

    Semantic Scholar, arXiv, OpenAlex, DBLP: These academic APIs are used for literature search and do not require API keys. They work out of the box.

    How is Research Copilot different from Claude Cowork?

    Claude Cowork is Anthropic's general-purpose autonomous agent for knowledge workers — it handles file organization, document drafting, and data extraction across everyday desktop tasks.

    Research Copilot is a vertical tool built specifically for academic research. The two differ in depth, not surface:

    Claude Cowork Research Copilot
    Scope Horizontal — any knowledge work Vertical — academic research lifecycle
    Literature No academic search Multi-source search (Semantic Scholar, arXiv, OpenAlex, DBLP) with relevance scoring, coverage tracking, and citation tracing
    Paper management Processes files you already have Structured artifact system with DOI, bibtex, citeKey, citation counts, and relevance metadata
    Academic writing Generic document drafting Venue-specific templates (NeurIPS, ICML, journals), IMRAD structure, LaTeX, citation verification (never hallucinated)
    Grant writing None Agency-specific guidance (NSF, NIH, DOE, DARPA, NSTC) with compliance checklists
    Data analysis Extracts data from documents LLM-generated Python scripts with statistical modeling, matplotlib/seaborn visualization, and output manifests
    Domain skills General capabilities 13 pluggable research skills (scientific writing, visualization, scholar evaluation, etc.) — extensible via Markdown
    Knowledge persistence Not specified Artifact store, session summaries, cross-session memory, @-mention references
    Openness Closed-source commercial product Open source (MIT) — fully customizable

    In short: Claude Cowork is like a smart office assistant. Research Copilot is like a lab partner who knows how to search literature, run stats, write papers, and apply for grants.

    Features

    AI Chat with Coding & Writing Tools

    Converse with an AI research assistant that can read, write, and edit files in your workspace. It generates LaTeX manuscripts, creates publication-quality figures, runs Python analysis scripts, and manages your project files — all through natural language.

    Search across Semantic Scholar, arXiv, OpenAlex, and DBLP simultaneously. Papers are scored for relevance, deduplicated, and organized in a searchable table. Quick actions let you do deep searches, fill coverage gaps, or trace citation chains.

    Literature Management

    Extensible Skills System

    Skills are lazy-loaded knowledge modules that give the AI domain expertise. The app ships with 13 builtin skills covering academic writing (paper-writing, grant proposals, rewrite-humanize), visualization (matplotlib, scientific schematics), data analysis, and more. You can also add your own project-specific skills.

    File Attachments in Chat

    Attach files directly in the chat input via the paperclip button, drag & drop, or paste. Supported formats:

    Format How it's processed
    Images (PNG, JPEG, GIF, WebP) Sent as vision content — the LLM sees the image visually
    Text files (CSV, MD, TXT, JSON, XML, HTML) Read directly and injected as text into the message
    Documents (PDF, DOCX) Converted to text via markitdown CLI (with pypdf fallback for PDF), then injected into the message

    Note: Document conversion requires markitdown (pip install markitdown[all]) or pypdf (pip install pypdf) for PDF/DOCX files. Text-based formats work out of the box with no extra dependencies.

    Future plan: The underlying Anthropic API supports native PDF document blocks (preserving layout, tables, and embedded images). Once the pi-mono agent runtime adds DocumentContent support, PDF attachments will be upgraded to use native API handling instead of text extraction.

    More

    • Document conversion — PDF / DOCX / PPTX / XLSX → Markdown (via agent tools)
    • Python data analysis — LLM-generated analysis with matplotlib/seaborn visualization
    • Artifact management — notes, papers, data, web content with CRUD tools
    • @-mention system — reference entities inline in chat
    • Session continuity — automatic context compaction and session summaries
    • Integrated terminal — run commands without leaving the app
    • LLM providers — OpenAI and Anthropic models supported

    Prerequisites

    • Node.js >= 18
    • npm >= 9
    • Python 3 (optional, for data analysis and figure generation)
    • macOS recommended (Linux/Windows: use the git clone method below, untested)

    Getting Started

    npm install -g research-copilot
research-copilot

    Option B: Clone from source

    git clone https://github.com/daidong/PiPilot.git
cd PiPilot
npm install
npm run dev

    API Keys

    On first launch, the app will prompt you to enter your API keys directly in the UI. Keys are saved to ~/.research-copilot/config.json.

    You can also set them as environment variables in your shell profile (~/.zshrc, ~/.bashrc):

    export ANTHROPIC_API_KEY="sk-ant-..."   # or OPENAI_API_KEY="sk-..."

    See API Keys Setup above for the full list.

    Build for Production

    npm run build

    Project Structure

    app/                  # Electron desktop application
├── src/main/         # Main process (IPC handlers, app lifecycle)
├── src/preload/      # Context bridge (renderer ↔ main)
└── src/renderer/     # React UI (components, Zustand stores)

lib/                  # Research agent logic (framework-independent)
├── agents/           # Coordinator agent + prompt registry
├── commands/         # Artifact CRUD, search, enrichment
├── mentions/         # @-mention parsing and resolution
├── memory-v2/        # Artifact storage and session summaries
├── skills/           # Skills system (loader + builtin skills)
└── tools/            # Research tools (web, literature, data, convert)

shared-electron/      # Reusable Electron IPC utilities
shared-ui/            # Shared React components and stores

    Adding Custom Skills

    Create a Markdown file at <your-workspace>/.pi/skills/<name>/SKILL.md:

    ---
id: my-skill
name: My Skill
shortDescription: Brief description of what this skill does
---

Summary loaded at startup.

## Procedures
Detailed guidance loaded on demand when the skill is activated.

    Skills are auto-discovered from three locations (later overrides earlier):

    1. lib/skills/builtin/ — shipped with the app
    2. ~/.research-pilot/skills/ — user-global
    3. <workspace>/.pi/skills/ — project-specific

    Configuration

    Research Copilot stores its data in the workspace under .research-pilot/:

    .research-pilot/
├── artifacts/          # Notes, papers, data, web content
│   ├── notes/
│   ├── papers/
│   ├── data/
│   └── web-content/
└── memory-v2/
    └── session-summaries/

    License

    MIT