Showing 32 open source projects for "scikit-learn"

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  • 1
    Microsoft Learn MCP Server

    Microsoft Learn MCP Server

    Official Microsoft Learn MCP Server, powering LLMs and AI agents

    Microsoft Learn MCP Server is the official GitHub repository for the Microsoft Learn MCP (Model Context Protocol) Server, a service that implements the Model Context Protocol to provide AI assistants and tools with reliable, real-time access to Microsoft’s official documentation. Rather than relying on training data that may be outdated or incomplete, MCP servers let agents like GitHub Copilot, Claude, or other LLM-based tools search and pull context directly from up-to-date Microsoft Learn content, including Azure, .NET, and other tech docs. ...
    Downloads: 0 This Week
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  • 2
    Agentic Data Scientist

    Agentic Data Scientist

    An end-to-end Data Scientist

    ...Each agent is designed to independently call functions, interact with data sources, and adapt to uncertainties during processing, enabling iterative refinement of models without manual coordination. The framework supports interoperability with existing data tools and libraries, letting the agents leverage libraries like pandas, scikit-learn, and visualization frameworks to perform real computations rather than mock demonstrations.
    Downloads: 2 This Week
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  • 3
    GaiaNet

    GaiaNet

    Install and run your own AI agent service

    Gaia is building an active, intelligent ecosystem that supports applications that learn, improve and grow over time. Put your knowledge to work and watch it evolve by creating a node on Gaia or by contributing to a domain supporting an existing knowledge base. Gaia’s decentralized platform ensures robust protection for user data and IP. Gaia allows secure ownership and monetization of IP without compromising privacy.
    Downloads: 96 This Week
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  • 4
    Hindsight

    Hindsight

    Hindsight: Agent Memory That Learns

    Hindsight is an advanced, open-source memory system for AI agents designed to enable long-term learning, reasoning, and consistency across interactions by treating memory as a first-class component of intelligence rather than a simple retrieval layer. It addresses one of the core limitations of modern AI agents, which is their inability to retain and meaningfully use past experiences over time, by introducing a structured, biomimetic memory architecture inspired by how human memory works....
    Downloads: 31 This Week
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  • 5
    LobeHub

    LobeHub

    Workspace to find, build, and collaborate with AI agents

    ...With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. The platform is built around the idea of co-evolution, where both humans and agents continuously learn and improve together.
    Downloads: 13 This Week
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  • 6
    Alook

    Alook

    The collaboration layer for your AI workforce

    ...It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. Alook also emphasizes memory and traceability, so agents can remember past decisions, learn preferences, and keep a record of instructions and replies. Its main value is turning separate AI coding tools into an organized, always-on operating system for agent-based work.
    Downloads: 12 This Week
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  • 7
    Postiz

    Postiz

    The ultimate social media scheduling tool, with a bunch of AI

    ...Manage your social media channels with ease. Collaborate with your team and delegate tasks. Expose your brand to a wider audience by connecting with influencers and brands. Learn from your data and improve your social media strategy. Track your performance and optimize your content.
    Downloads: 9 This Week
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  • 8
    Personal AI Infrastructure

    Personal AI Infrastructure

    Agentic AI Infrastructure for magnifying HUMAN capabilities

    ...Unlike once-stateless chatbots, this platform captures context, memory, goals, preferences, and feedback to enable an AI that understands you and improves over time, using a full agentic stack rather than simple question-answer loops. PAI blends tools like browsing, code editing, execution, and more into a continuous Observe → Think → Plan → Execute → Verify → Learn cycle, letting the system refine its behavior with each use. Its architecture supports long-term memory, verification of actions, and ongoing self-improvement, blurring the line between “assistant” and persistent, evolving collaborator.
    Downloads: 4 This Week
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  • 9
    React Doctor

    React Doctor

    Your agent writes bad React

    ...The scanner checks areas such as state management, effects, performance, architecture, accessibility, security, and dead code. It works across popular React environments, including Next.js, Vite, and React Native. It can also be installed into coding agents so they learn better React practices before generating new code. For teams, it supports GitHub Actions workflows that can comment on pull requests and expose scores for automated quality gates.
    Downloads: 2 This Week
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  • 10
    Vibe-Trading

    Vibe-Trading

    Vibe-Trading: Your Personal Trading Agent

    ...It features a swarm-based architecture with prebuilt expert agent teams for research, trading, and risk management. Advanced backtesting engines provide statistical validation, optimization, and performance metrics. The system also includes persistent memory, enabling it to learn from past interactions and refine strategies over time. Overall, it delivers an end-to-end AI-driven trading environment for both research and execution.
    Downloads: 3 This Week
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  • 11
    Agent Reinforcement Trainer

    Agent Reinforcement Trainer

    Train multi-step agents for real-world tasks using GRPO

    Agent Reinforcement Trainer, or ART is an open-source reinforcement learning framework tailored to training large language model agents through experience, making them more reliable and performant on multi-turn, multi-step tasks. Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ART also supports scalable training patterns, observability tools, and integration with hosted platforms like Weights & Biases, and it provides notebooks that demonstrate training on standard benchmarks and tasks.
    Downloads: 2 This Week
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  • 12
    WrenAI

    WrenAI

    Open-source SQL AI Agent for Text-to-SQL. Make Text2SQL Easy

    ...Wren AI has implemented a semantic engine architecture to provide the LLM context of your business; you can easily establish a logical presentation layer on your data schema that helps LLM learn more about your business context. With Wren AI, you can process metadata, schema, terminology, data relationships, and the logic behind calculations and aggregations with “Modeling Definition Language”, to generate accurate SQL queries with semantic context. When starting a new conversation in Wren AI, your question is used to find the most relevant tables. ...
    Downloads: 2 This Week
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  • 13
    OpenClaw-RL

    OpenClaw-RL

    Train any agents simply by 'talking'

    OpenClaw-RL is an open-source reinforcement learning framework designed to train and personalize AI agents built on the OpenClaw ecosystem. The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. The system incorporates...
    Downloads: 1 This Week
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  • 14
    Mem0

    Mem0

    The Memory layer for AI Agents

    ...Mem0 is perfect for projects such as customer support, where chatbots remember past interactions to reduce repetition and speed up resolution times; personal AI companions that recall preferences and past conversations for more meaningful interactions; AI agents that learn from each interaction to become more personalized and effective over time.
    Downloads: 1 This Week
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  • 15
    anti-distill

    anti-distill

    Anti-distillation for employee Skills

    anti-distill is a research-oriented project focused on protecting machine learning models from knowledge distillation attacks, where smaller models attempt to replicate the behavior of larger proprietary systems. The project explores techniques that make it harder for external models to learn from outputs, thereby preserving intellectual property and model uniqueness. It likely introduces methods such as output perturbation, watermarking, or response shaping to prevent accurate imitation. The system is particularly relevant in contexts where models are exposed via APIs and risk being reverse-engineered through repeated querying. ...
    Downloads: 0 This Week
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  • 16
    AgentHandover

    AgentHandover

    AgentHandover observes, learns and teaches agents with skills

    ...It stores learned knowledge locally and uses feedback from later executions to improve confidence, add decision branches, and demote stale or failing skills. Its main value is helping agents learn how a person actually works, so recurring tasks can be handed off with more context, consistency, and trust.
    Downloads: 0 This Week
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  • 17
    OpenSRE

    OpenSRE

    Build your own AI SRE agents. The open source toolkit for the AI era

    ...Its multi-agent architecture allows parallel reasoning across systems, mimicking how experienced SRE teams debug complex issues. The platform also incorporates memory and knowledge graph capabilities to learn from past incidents and improve future investigations. It is designed to run locally within an organization’s infrastructure, ensuring data privacy and compliance.
    Downloads: 0 This Week
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  • 18
    Pal

    Pal

    A personal context-agent that learns how you work

    Pal is an open-source AI personal agent built within the Agno ecosystem that functions as an intelligent digital assistant designed to learn from user activity over time. The system acts as an AI-powered “second brain” capable of capturing, organizing, and retrieving personal knowledge such as notes, bookmarks, research findings, people, and meeting information. Instead of acting as a simple chatbot, Pal continuously builds a structured database of a user’s knowledge and context so it can answer questions, recall information, and assist with future tasks more effectively. ...
    Downloads: 0 This Week
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  • 19
    rLLM

    rLLM

    Democratizing Reinforcement Learning for LLMs

    rLLM is an open-source framework for building and training post-training language agents via reinforcement learning — that is, using reinforcement signals to fine-tune or adapt language models (LLMs) into customizable agents for real-world tasks. With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to...
    Downloads: 0 This Week
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  • 20
    OpenAI Swarm

    OpenAI Swarm

    Educational framework exploring multi-agent orchestration

    Swarm focuses on making agent coordination and execution lightweight, highly controllable, and easily testable. It accomplishes this through two primitive abstractions; Agents and handoffs. An Agent encompasses instructions and tools, and can at any point choose to hand off a conversation to another Agent. These primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solutions while avoiding a steep learning...
    Downloads: 0 This Week
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  • 21
    SuperAGI

    SuperAGI

    A dev-first open source autonomous AI agent framework

    ...Control token usage to manage costs effectively. Enable your agents to learn and adapt by storing their memory. Get notified when agents get stuck in the loop, and provide proactive resolution. Read and store files generated by Agents.
    Downloads: 0 This Week
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  • 22
    Dragonfire

    Dragonfire

    The open-source virtual assistant for Ubuntu based Linux distributions

    Dragonfire is the open-source virtual assistant project for Ubuntu-based Linux distributions. Her main objective is to serve as a command and control interface to the helmet user. So that you will be able to give orders just by using your voice commands and your eye movements. That makes the helmet handsfree. We are planning to ship Dragonfire as a preinstalled software package on DragonOS Linux Distribution. DragonOS will be a Linux distribution specially designed for the helmet. It will...
    Downloads: 2 This Week
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  • 23

    2OPL

    2OPL is a norm programming language for agent organizations.

    ...Finally sanction rules can be programmed to respond to norm violations. The current implementation is based on logic programming (Prolog in particular). For those users who are not familiar with Prolog we advise the free ebook Learn Prolog Now (http://www.learnprolognow.org). The interpreter supports three different norm specifications. For more information see the documentation or mail one of the developers.
    Downloads: 0 This Week
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  • 24
    Sentence Composer

    Sentence Composer

    Compose the writings semi-automatically

    ...Transduction, or transductive inference, tries to predict new outputs on specific and fixed (test) cases from observed, specific (training) cases. The simplest realization for transductive inference is the method of k-nearest neighbors. Learning to learn learns its own inductive bias based on previous experience. ...AND SO ON.... Enjoy the program !
    Downloads: 0 This Week
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  • 25
    Ms. Pac-Man Framework

    Ms. Pac-Man Framework

    Using reinforcement learning with relative input to train Ms. Pac-Man

    This Java-application contains all required components to simulate a game of Ms. Pac-Man and let an agent learn intelligent playing behaviour using reinforcement learning and either Q-Learning or SARSA. The framework was developed by Luuk Bom and Ruud Henken, under supervision of Marco Wiering, Department of Artificial Intelligence, University of Groningen. It formed the basis of a bachelor's thesis titled "Using reinforcement learning with relative input to train Ms.
    Downloads: 0 This Week
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