mirror of
https://github.com/getcompanion-ai/alpha-hub.git
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081d32d50c
- Add `alpha status` command to check auth state - Search results drop raw text blobs by default (opt-in via includeRaw) - Clean and normalize search result fields - Export parsePaperSearchResults for external consumers - Login callback server handles EADDRINUSE, styled HTML pages - Better browser open error handling on all platforms - Search prints mode description before results Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
113 lines
4.3 KiB
Markdown
113 lines
4.3 KiB
Markdown
# Alpha Hub
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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](https://alphaxiv.org).
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[](LICENSE)
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[](https://nodejs.org)
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## Quick Start
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```bash
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npm install -g @companion-ai/alpha-hub
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alpha login # sign in with alphaXiv
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alpha status # show whether alphaXiv auth is present
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alpha search "attention mechanism" # search papers
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alpha get 1706.03762 # fetch paper report
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```
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## How It Works
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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.")
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**Most of the time, it's simple — search, read, use:**
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```bash
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alpha search "transformer attention" # find relevant papers
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alpha get 1706.03762 # fetch AI-generated paper report
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# Agent reads the report, understands the paper. Done.
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```
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**When the agent discovers something useful**, it can annotate locally for next time:
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```bash
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alpha annotate 1706.03762 "Superseded by Flash Attention for efficiency"
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# Next session, the annotation appears automatically on alpha get.
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```
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**Need to go deeper?** Ask questions about any paper:
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```bash
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alpha ask 1706.03762 "What datasets were used for evaluation?"
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```
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## Commands
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| Command | Purpose |
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|---------|---------|
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| `alpha search <query>` | Search papers (semantic, keyword, or agentic) |
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| `alpha get <id\|url>` | Fetch paper report + local annotation |
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| `alpha ask <id\|url> <question>` | Ask a question about a paper |
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| `alpha code <github-url> [path]` | Read files from a paper repository |
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| `alpha annotate <id> <note>` | Attach a note to a paper |
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| `alpha annotate <id> --clear` | Remove a note |
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| `alpha annotate --list` | List all notes |
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| `alpha login` | Sign in with alphaXiv |
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| `alpha status` | Show alphaXiv authentication status |
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| `alpha logout` | Sign out |
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All commands accept `--json` for machine-readable output.
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## Self-Improving Agents
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Alpha Hub is designed for a loop where agents get better over time.
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**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.
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```
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Without Alpha Hub With Alpha Hub
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───────────────── ──────────────
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Search the web for papers Semantic search via alphaXiv
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Read raw PDFs AI-generated paper reports
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Miss context and gotchas Agent notes what it learns
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Knowledge forgotten ↗ Even smarter next session
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↻ Repeat next session
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```
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## Key Features
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### Semantic Search
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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.
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```bash
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alpha search "methods for reducing hallucination in LLMs" # semantic
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alpha search "LoRA" --mode keyword # keyword
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alpha search "retrieval-augmented generation for QA" --mode agentic
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alpha search "alignment of vision language models" --mode all
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```
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### Paper Q&A
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Ask questions about any paper without reading the full PDF. The answer is grounded in the paper's actual content.
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```bash
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alpha ask 2106.09685 "What is the rank used for the low-rank matrices?"
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```
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### Annotations
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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.
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### Repository Reading
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Read files directly from a paper's GitHub repository when the implementation matters.
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```bash
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alpha code https://github.com/openai/gpt-2 /
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alpha code https://github.com/openai/gpt-2 src/model.py
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```
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## License
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[MIT](LICENSE)
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