Gives your AI coding agent access to 2M+ computer science papers through five research tools: explore_approaches surveys technique families with evidence, deep_dive investigates implementation details and failure modes, compare_approaches does side-by-side analysis, and check_feasibility returns a GO/PROTOTYPE/RECONSIDER verdict based on your constraints. Setup is one command via npx that handles OAuth and configures Claude Code, Cursor, Windsurf, Copilot, or other supported agents. The agent automatically reaches for it when making technical decisions about algorithms or architectures, not for syntax or debugging. Essentially turns research literature into executable guidance at decision points.
Research intelligence that makes your AI coding agent smarter - one command setup.
Paper Lantern gives your AI coding agent access to 2M+ CS research papers - the right technique for your problem, with tradeoffs, benchmarks, and implementation guidance.
npx paperlantern@latest
That's it. Pick your agents, log in, and Paper Lantern is configured.
Prefer to set up manually, or using a client not listed above? See paperlantern.ai/docs for per-agent config snippets and the raw MCP endpoint.
The setup CLI:
Once configured, your agent gets these tools:
| Tool | What it does |
|---|---|
explore_approaches | Survey 4-6 approach families with evidence and tradeoffs |
deep_dive | Investigate one technique in depth - implementation, hyperparameters, failure modes |
compare_approaches | Side-by-side comparison of 2-3 candidates |
check_feasibility | GO / PROTOTYPE / RECONSIDER verdict given your constraints |
give_feedback | Tell us what helped and what didn't |
Paper Lantern activates when your agent is making technical decisions where research evidence could improve the outcome - choosing between algorithms, architectures, or techniques.
It does not activate for syntax questions, library lookups, debugging, or general programming tasks.