Remember
Lint-AI
Lint for AI-generated knowledge
Drop Lint-AI into your coding agent as its memory: retrieval-grade recall over past sessions and notes, plus corpus linting that flags orphaned pages and missing cross-references — instead of just storing everything it sees.
Open source · Apache-2.0 · Rust · runs entirely on your own hardware
$ lint-ai ./docs
Issues:
- Orphan page: quickstart.md
- Missing cross-ref in claude-code-hooks-design.md -> [[claude code]] (high)
- Missing cross-ref in claude-code-hooks-design.md -> [[codex]] (high)
- Low link density in tier1-light-understanding.md (outgoing 0, avg 0.1)
Stats:
pages: 31
top concepts: lexical data (9), codex (6), claude code (5)
What it saves your agent
Diagnostic smoke-run numbers from a single repetition — a direction, not a guarantee. See the full run conditions.
| Arm | Time | Input tokens | Recall |
|---|---|---|---|
| Claude, native memory | 22.47s | 123,536 | 2/3 |
| Claude, Lint-AI only | 7.05s | 15,914 | 3/3 |
| Codex, native memory | 38.13s | 151,000 | 2/3 |
| Codex, Lint-AI only | 18.53s | 62,478 | 2/3 |
What it does
Lexical + graph indexing
Entity and term tables alongside a graph structure for links and symbols, so retrieval understands structure, not just text.
Corpus-level linting
Flags orphan pages, missing cross-references, and low link density across your whole document set — not just one file at a time.
Works as memory in your CLI
Plugs into Claude Code, Codex, Gemini CLI, and Antigravity CLI (AGY) as their memory, via lifecycle hooks and an MCP search interface — no manual tool selection required.
Semantic + LLM-ready queries
Query modes include semantic retrieval and context generation formatted for direct use in an LLM prompt.
Configurable scope
A single lint-ai.json controls filtering and scoping, so you decide what gets indexed and what gets ignored.
CPU-only by design
Built in Rust with an optional spaCy NER integration. No GPU in the critical path.
Get started
# clone and build from source
git clone https://github.com/RooAGI/Lint-AI/
cd Lint-AI
cargo build --release
Available as a CLI or a library. See the README for provider integration flags and lint-ai.json configuration, or the integration benchmark methodology for how memory performance is measured across each CLI.
Let's talk about your agents
Whether it's agent memory, a no-code build, or in-house model serving, tell us what you're working on and we'll point you to the right place to start.
Free Architecture Review
We'll look at your current agent setup and flag where memory, tooling, or serving is the bottleneck
Custom Deployment Guidance
Tailored recommendations for your use case, team size, and hardware
Enterprise Support Options
24/7 support, priority updates, and dedicated engineering resources
Built for teams running agents in production
From open-source Lint-AI users to enterprises deploying custom inference — RooAGI covers the whole stack.