siyuan
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
open-kritt — проект с открытым исходным кодом из категории «ИИ-агенты» от Kritt-ai (Kritt-ai/open-kritt на GitHub). У него 2 236 звёзд на GitHub и 383 форка, он распространяется по лицензии AGPL-3.0 и последний раз обновлялся на GitHub 5 октября 2026.
Self-hosted AI vulnerability research: agents find, validate, deduplicate, and rank real security issues.
Decide which codebase you want researched and how findings should be validated before they reach your team.
Clone the repository and follow the getting-started guide in the project documentation for the current self-hosted setup.
Configure the deployment with the model access and validation settings your environment requires.
Point an agent run at your code, then review the deduplicated, ranked findings it produces.
Multiple focused agents analyse code for security issues rather than relying on a single pattern-matching pass.
Candidate issues are validated with configurable checks, reducing the noise of unverified AI-generated reports.
Results are merged and ranked so reviewers see unique, prioritised findings instead of repeated alerts.
Findings can be enriched with additional context to support triage and remediation.
The platform runs on your own infrastructure, keeping proprietary source code inside your environment.
Positioned for vulnerability research and bug bounty style work, with a companion research paper and documentation site.
Get the source and its documentation locally.
git clone https://github.com/Kritt-ai/open-kritt.git
cd open-krittThe README directs users to the official getting-started guide for the current install steps.
# see README for the exact command - getting started: https://docs.kritt.aiConfigure the deployment per the docs, then start an agent run against a target repository. # see README for the exact command
# see docs.kritt.ai getting started for run commandsIt is maintained by Kritt-ai, the team behind kritt.ai.
The code is AGPL-3.0 licensed, so it is free to use and self-host, with copyleft obligations if you modify and distribute or serve it. Model usage for the agents is billed by your provider.
Yes in practice: the agents that perform the research run on LLM access that you configure and pay for.
Traditional SAST scanners match known patterns deterministically; Open Kritt orchestrates reasoning agents that search for and validate issues, which can surface logic flaws pattern tools miss, at the cost of model spend.
Clone the repository and follow the getting-started guide at docs.kritt.ai, which the README designates as the canonical setup path.
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
Self-hosted ai agents: minimal ai coding agent (~1,000 lines of python) inspir.
PraisonAI 🦞 — Hire a 24/7 AI Workforce. Stop writing boilerplate and start shipping autonomous self-improving agents that research, plan, code, and execute t…
The open-source platform for building and running autonomous AI agents, hosted or self-hosted
Autonomous AI agents for penetration testing — self-hosted, multi-agent, with a full web UI
SWE-Gym: the first training environment for real-world software engineering agents and verifiers (ICML 2025).
Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если open-kritt вам не подходит.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к open-kritt — продолжайте изучать полку по обе стороны от него.
Unla MCP Gateway: turn existing APIs and MCP servers into MCP endpoints with zero code changes.
Open Cowork: a desktop AI agent app with one-click Claude Code, MCP tools, skills, and VM sandboxing.
Agno's self-learning data agent: grounded text-to-SQL answers over your database that improve with use.
A benchmark for tool-agent-user interaction in real-world domains.
NVIDIA's object-oriented, Pythonic approach to building AI agents.
Shrimp Task Manager: an MCP server that turns natural language into structured, dependent dev tasks.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.