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(-o-) alit

Your agent forgets every paper it's ever read.

alit is a local knowledge base for AI agents doing literature review. One agent reads 50 papers and stores structured summaries, citations, and reading status. The next agent — or the same agent next week — queries that knowledge instantly.

The agent builds the knowledge. You set the taste. Knowledge compounds.

pip install agent-lit → zero dependencies, SQLite-only, works with any coding agent.

The core loop

alit taste "vision-language grounding, embodied AI"   # you set direction
alit recommend 5                                       # agent picks what to read
alit summarize <id> --l4 "..." --model claude          # agent stores findings
alit ask "what approaches exist for X?" --depth 2      # agent synthesizes

Run alit --help for the full command list (25 commands for search, import, export, citations, and more).

What alit does and doesn't do

alit The agent
Stores papers, summaries, citations, taste
Ranks recommendations (PageRank + taste + recency)
Retrieves context for synthesis
Persists across sessions
Reads papers
Writes summaries
Decides what to cite
Answers research questions

alit stores and retrieves. The agent thinks. You set the taste.

Setup

pip install agent-lit    # or: uv add agent-lit

Spin up Claude Code, opencode, Cursor, or whatever you use, then prompt:

Set up alit for literature review in this project. See https://github.com/Zhou-Hangyu/alit

From there:

Find and add the top 10 papers on vision-language grounding from the last 2 years.
Read the next 5 recommended papers and summarize each one.
What does the literature say about cross-modal attention mechanisms?

How it works

.alit/
├── papers.db    ← one SQLite file, entire knowledge base
└── pdfs/        ← auto-downloaded from arXiv

No servers. No API keys. No vector databases.

Update

pip install --upgrade agent-lit

Under the hood

  • Search: BM25 via SQLite FTS5
  • Ranking: PageRank on citation graph (pure Python)
  • Recommendations: PageRank + recency + taste matching
  • Synthesis: multi-stage funnel retrieval (~5K tokens to query 10K papers)
  • Enrichment: arXiv API (batched) with Semantic Scholar fallback
  • Backward compatible: schema auto-migrates on upgrade

License

MIT

Release files for agent-lit 0.5.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for agent-lit 0.5.0
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Table of built distributions (wheels) for agent-lit 0.5.0
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agent_lit-0.5.0-py3-none-any.whl Python 3 none any Details

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