Alpha Hub

Research agents hallucinate paper details and forget what they learn in a session. Alpha Hub gives them semantic paper search, AI-generated reports, and persistent annotations — so they get smarter with every task. Search and content powered by alphaXiv.

Quick Start

npm install -g @companion-ai/alpha-hub
alpha login                        # sign in with alphaXiv
alpha status                       # show whether alphaXiv auth is present
alpha search "attention mechanism" # search papers
alpha get 1706.03762               # fetch paper report

How It Works

Alpha is designed for your coding agent to use (not for you to use!). You can prompt your agent to use it (e.g., "Use the CLI command alpha to search for papers on LoRA. Run alpha to see how it works.")

Most of the time, it's simple — search, read, use:

alpha search "transformer attention"     # find relevant papers
alpha get 1706.03762                     # fetch AI-generated paper report
# Agent reads the report, understands the paper. Done.

When the agent discovers something useful, it can annotate locally for next time:

alpha annotate 1706.03762 "Superseded by Flash Attention for efficiency"

# Next session, the annotation appears automatically on alpha get.

Need to go deeper? Ask questions about any paper:

alpha ask 1706.03762 "What datasets were used for evaluation?"

Commands

Command	Purpose
alpha search <query>	Search papers (semantic, keyword, or agentic)
alpha get <id|url>	Fetch paper report + local annotation
alpha ask <id|url> <question>	Ask a question about a paper
alpha code <github-url> [path]	Read files from a paper repository
alpha annotate <id> <note>	Attach a note to a paper
alpha annotate <id> --clear	Remove a note
alpha annotate --list	List all notes
alpha login	Sign in with alphaXiv
alpha status	Show alphaXiv authentication status
alpha logout	Sign out

All commands accept --json for machine-readable output.

Self-Improving Agents

Alpha Hub is designed for a loop where agents get better over time.

Annotations are local notes that agents attach to papers. They persist across sessions and appear automatically on future fetches — so agents learn from past experience.

  Without Alpha Hub                          With Alpha Hub
  ─────────────────                          ──────────────
  Search the web for papers                  Semantic search via alphaXiv
  Read raw PDFs                              AI-generated paper reports
  Miss context and gotchas                   Agent notes what it learns
  Knowledge forgotten                        ↗ Even smarter next session
  ↻ Repeat next session

Key Features

Semantic Search

Three search modes — semantic (embedding similarity), keyword (exact terms), and agentic (multi-turn retrieval) — so agents find the right papers regardless of how they phrase the query. --mode all runs all three in parallel for maximum recall.

alpha search "methods for reducing hallucination in LLMs"  # semantic
alpha search "LoRA" --mode keyword                          # keyword
alpha search "retrieval-augmented generation for QA" --mode agentic
alpha search "alignment of vision language models" --mode all

Paper Q&A

Ask questions about any paper without reading the full PDF. The answer is grounded in the paper's actual content.

alpha ask 2106.09685 "What is the rank used for the low-rank matrices?"

Annotations

Local notes that agents attach to papers — they persist across sessions and appear automatically on future fetches. See the annotation as a gap the agent discovered and recorded so it doesn't repeat the same mistake.

Repository Reading

Read files directly from a paper's GitHub repository when the implementation matters.

alpha code https://github.com/openai/gpt-2 /
alpha code https://github.com/openai/gpt-2 src/model.py

License

MIT