Leeroopedia

from leeroo-ai

The Brain that turns Generalist Agents into ML Experts.

🔥🔥🔥✓ VerifiedFreeQuick setup

#automation #devops #git #customer-support

Kapso

A Knowledge-grounded framework for Autonomous AI/ML Program Synthesis and Optimization

Learn more · Join Discord · Website

If you like this project, please support us by giving it a star ⭐

Early Access: Sign up for the hosted version of Kapso.

Kapso Framework Architecture

News

Leeroopedia MCP Integration: Kapso now connects to Leeroopedia MCP — your ML & Data Knowledge Wiki. Learnt by AI, built by AI, for AI. A centralized playbook of best practices and expert-level knowledge for Machine Learning and Data domains. Kapso agents use it during ideation and implementation to search knowledge, build plans, diagnose failures, and more.
Moltbook Agents 🦞: Build AI agents that optimize other agents and debate on Moltbook! Get started →
Technical Report: Our technical report is now available! Read the paper
#1 on MLE-Bench: KAPSO achieved top ranking among open-source systems on Kaggle ML competitions (MLE Benchmark).
#1 on ALE-Bench: KAPSO achieved top ranking on long-horizon algorithmic discovery problems (ALE Benchmark).

What is KAPSO?

KAPSO combines iterative experimentation with a knowledge base of best practices and tricks to discover ML/AI code improvements.

It automates the cycle of designing, testing, and refining algorithms, eventually adapting the optimized solution for deployment on your chosen infrastructure.

The Four Pillars

Pillar	Method	Description
Evolve	`.evolve()`	Run iterative experiments to build software for a goal. Uses tree search, coding agents, and KG context to generate and refine solutions.
Learn	`.learn()`	Ingest knowledge from repositories, past solutions, or research results. Extracts patterns and best practices into the Knowledge Graph.
Research	`.research()`	Run deep web research to gather ideas and implementation references. Returns structured findings you can feed into the knowledge base or use as context for evolving solutions.
Deploy	`.deploy()`	Turn a solution into running software. Supports local execution, Docker containers, or cloud platforms like Modal.

Examples

Example	Description
CUDA Optimization	Optimize CUDA kernels for GPU performance
PyTorch Optimization	Optimize PyTorch operations for speedup
ML Model Development	Improve ML model accuracy on tabular data
Prompt Engineering	Optimize prompts for better LLM performance
Agentic Scaffold	Optimize agentic AI workflows

Supported Benchmarks

Benchmark	Description
MLE-Bench	Kaggle ML competitions — tabular, image, text, audio problems
ALE-Bench	AtCoder algorithmic optimization — C++ solution generation

📚 Documentation & Support

Full Documentation: docs.leeroo.com
Community: Discord
Website: leeroo.com

Citation

If you use Kapso in your research, please cite:

@misc{nadaf2026kapsoknowledgegroundedframeworkautonomous,
      title={KAPSO: A Knowledge-grounded framework for Autonomous Program Synthesis and Optimization}, 
      author={Alireza Nadafian and Alireza Mohammadshahi and Majid Yazdani},
      year={2026},
      eprint={2601.21526},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.21526}, 
}

🚀 Quickstart

Installation

From PyPI (recommended)

pip install leeroo-kapso

From source (for development or to access wiki knowledge data)

git clone https://github.com/leeroo-ai/kapso.git
cd kapso

# Pull Git LFS files (wiki knowledge data)
git lfs install
git lfs pull

# Create conda environment (recommended)
conda create -n kapso python=3.12
conda activate kapso

# Install in development mode
pip install -e .

Leeroopedia MCP (optional) — connect Kapso to Leeroopedia, a curated ML/AI knowledge base. Sign up at leeroopedia.com for an API key, then:

pip install leeroopedia-mcp
echo 'LEEROOPEDIA_API_KEY=kpsk_your_key_here' >> .env

Basic Usage

from kapso import Kapso, Source, DeployStrategy

# Initialize Kapso
# If you have a Knowledge Graph, pass kg_index; otherwise just use Kapso()
kapso = Kapso(kg_index="data/indexes/legal_contracts.index")

# Research: Gather domain-specific techniques from the web
# mode: "idea" | "implementation" | "study" (can pass multiple as list)
# depth: "light" | "deep" (default: "deep")

findings = kapso.research(
    "RLHF and DPO fine-tuning for legal contract analysis",
    mode=["idea", "implementation"],
    depth="deep",
)

# Learn: Ingest knowledge from repositories and research into the KG
kapso.learn(
    Source.Repo("https://github.com/huggingface/trl"),
    *findings.ideas,           # List[Source.Idea]
    *findings.implementations, # List[Source.Implementation]
    wiki_dir="data/wikis",
)

# Evolve: Build a solution through experimentation
# Use research results as context via to_string()
solution = kapso.evolve(
    goal="Fine-tune Llama-3.1-8B for legal clause risk classification, target F1 > 0.85",
    data_dir="./data/cuad_dataset", 
    output_path="./models/legal_risk_v1",
    context=[findings.to_string()],
)

# Deploy: Turn solution into running deployed_program
deployed_program = kapso.deploy(solution, strategy=DeployStrategy.MODAL)
deployed_program.stop()

For detailed integration steps, see the Quickstart and Installation guides.