Leeroopedia

The Brain that turns Generalist Agents into ML Experts.

View on docs.leeroopedia.com

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Getting Started

Leeroopedia MCP

$20 free credit on sign-up. That’s plenty of searches, plans, and diagnoses. Skip the guesswork on your next fine-tuning run or inference deployment. No credit card required. Get your API key →

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What is Leeroopedia?

Your ML & AI Knowledge Wiki. Learnt by AI, built by AI, for AI. Expert-level knowledge across the full ML & AI stack: fine-tuning and distributed training, inference serving and GPU kernel optimization, building agents and RAG pipelines. 1000+ frameworks and libraries, all in one place. This MCP server turns your AI coding agent (Claude Code, Cursor, Claude Desktop, ChatGPT, OpenAI Codex, …) into an ML/AI expert engineer. Browse the full knowledge base at leeroopedia.com.

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Want to go end-to-end?

Leeroopedia gives your agent the knowledge. Kapso gives it the ability to act on it: research, experiment, and deploy. Together: a complete ML/AI engineer agent.

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Connect to Your Agents

Use our hosted server for zero-setup. Just paste this URL into any MCP client that supports remote servers:

https://mcp.leeroopedia.com/mcp?token=kpsk_your_key_here

Or see the per-client guides below for detailed instructions (including local setup).

Claude Code

Set up with Claude Code

Cursor

Set up with Cursor

Claude Desktop

Set up with Claude Desktop

OpenAI Codex

Set up with OpenAI Codex

ChatGPT

Set up with ChatGPT

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Benchmarks

We measured the effect of Leeroopedia MCP on real ML tasks:

  • ML Inference Optimization. Write CUDA/Triton kernels for 10 KernelBench problems. 2.11x geomean speedup vs 1.80x (+17%), with/without Leeroopedia MCP.
  • LLM Post-Training. End-to-end SFT + DPO + LoRA merge + vLLM serving + IFEval on 8×A100. 21.3 vs 18.5 IFEval strict-prompt accuracy, 34.6 vs 30.9 strict-instruction accuracy, 272.7 vs 231.6 throughput.
  • Self-Evolving RAG. Build a RAG service that automatically improves itself over multiple rounds. 45.16 vs 40.51 Precision@5, 40.32 vs 35.29 Recall@5, in 52 vs 62 min wall time.
  • Customer Support Agent. Multi-agent triage system classifying 200 tickets into 27 intents. 98 vs 83 benchmark performance, 11s vs 61s per query.

See Full Benchmark Results

Detailed results, analysis, and replication instructions for all 4 benchmarks

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Available Tools

The server provides 8 agentic tools: search, plan, review, verify, diagnose, hypothesize, query hyperparameters, and retrieve pages.

Tools Overview

See all 8 tools with parameters and usage

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Quick Links

Claude Code

Connect in 2 minutes

Cursor

OpenAI Codex

ChatGPT

Benchmarks

See the results

All 8 tools explained

Benchmarks

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