(-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.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| agent_lit-0.5.0.tar.gz | 43.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| agent_lit-0.5.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 82.6 kB
Release files / agent_lit-0.5.0.tar.gz
| Download URL | agent_lit-0.5.0.tar.gz |
|---|---|
| Size | 43.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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uv/0.8.8
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Release files / agent_lit-0.5.0-py3-none-any.whl
| Download URL | agent_lit-0.5.0-py3-none-any.whl |
|---|---|
| Size | 39.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.8.8
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